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Norwegian University of Life Sciences
Department of Chemistry, Biotechnology and Food Science
PHILOSOPHIAE DOCTOR THESIS 2008:1
Reliable prediction and determination of Norwegian
lamb carcass composition and value
Pålitelig bestemmelse av sammensetningen i norske lammeslakt og verdi nedskåret vare
Jørgen Kongsro
ISBN 978-82-575-0798-5
ISSN 1503-1667
TABLE OF CONTENTS
PREFACE ................................................................................................................ii
SUMMARY ............................................................................................................iii
OPPSUMMERING (Summary in Norwegian) .............................................................. iv
LIST OF PAPERS ..................................................................................................... v
Background and motivation......................................................................................... 1
Dissection, cutting and value of cuts from lamb carcasses ................................................ 5
Classification of lamb and sheep carcasses; the EUROP classification system..................... 8
Measuring systems for lamb carcass composition ......................................................... 11
Multivariate calibration............................................................................................. 22
Main results of papers I-V and future perspectives. ....................................................... 28
References .............................................................................................................. 31
PAPERS I - V
PREFACE
This work was sponsored by grant 162188 of the Norwegian Research Council, as a part of a
Ph. D. study program. The Ph.D. study is a part of a research project at Animalia – Norwegian
Meat Research Centre, which among other activities is also devoted to optimizing
classification and grading of Norwegian lamb carcasses. The main area of activity for
Animalia is to conduct generic work funded by a farmer Research and Development levy. The
classification and grading system in Norway is supervised by Animalia, but the system is
owned by Nortura BA. Nortura BA has served as an industry partner in this project, and has
provided the sampled carcasses from different abattoirs located in southern Norway.
I would like to thank my supervisors at Norwegian University of Life Sciences, Prof. Are
Aastveit, Associate Prof. Knut Kvaal and last but not least, my main supervisor, Prof. Bjørg
Egelandsdal, who’s scientific and administrative skills, experience and valuable opinions have
guided me through this work to a higher academic level. Morten Røe at Animalia is
acknowledged for his practical and universal skills concerning the meat industry, carcass
classification and dissection, and for providing data and advice, and guiding me through this
work on a pragmatic level. The butchers at Animala are acknowledged for their skills in
dissection of carcasses, and for showing me the art of cutting and dissection of carcasses. Tor
Arne Ruud, Dr. Ole Alvseike and Per Berg are acknowledged for their support and help
during start-up of the project. Dr. Mohamed Kheir Omer Abdella is gratefully acknowledged
for his editing support. I would also like to thank the Norwegian Research Council for
funding this work (grant 162188).
I would also like to thank my family and friends, and especially my wife Tone for her love,
support and motivation during this work.
SUMMARY
The main objective of this work was to study prediction and determination of Norwegian
lamb carcass composition with different techniques spanning from subjective appraisal to
computer-intensive methods. There is an increasing demand, both from farmers and
processors of meats, for a more objective and reliable system for prediction of muscle (lean
meat), fat, bone and value of a lamb carcass. When introducing new technologies for
determination of lamb carcass composition, the reference method used for calibration must be
precise and reliable. The precision and reliability of the current dissection reference for lamb
carcass classification and grading has never been quantified. A poor reference method will not
benefit even the most optimal system for prediction and determination of lamb carcasses. To
help achieve reliable systems, the uncertainty or errors in the reference method and measuring
systems needs to be quantified. Using proper calibration methods for the measuring systems,
the uncertainty and modeling power can be determined for lamb carcasses.
The results of the work presented in this thesis show that the current classification system
using subjective appraisal (EUROP) is reliable; however the accuracy with respect to carcass
composition, especially for lean meat or muscle and carcass value, is poor. The reference
method used for determining lamb carcass composition with respect to lamb carcass
classification and grading is precise and reliable for carcass composition. For the composition
and yield of sub-primal cuts, the reliability varied, and was especially poor for the breast cut.
Further attention is needed for jointing and cutting of sub-primals to achieve even higher
precision and reliability of the reference method. As an alternative to butcher or manual
dissection, Computer Tomography (CT) showed promising results with respect to prediction
of lamb carcass composition. This method is nicknamed “virtual dissection”. By utilizing the
spectroscopic features of CT histograms of tissue density estimates, the composition of a lamb
could be modeled and validated using multivariate calibration. The precision and reliability of
virtual dissection was higher than for butcher dissection, and the running costs are much
lower, even though fixed costs of CT equipment is somewhat high. When summarizing all the
different techniques for lamb carcass composition used in this work, it seems like the most
precise and reliable system at the present time for prediction of lamb carcass composition and
value, is on-line optical probing of carcass side calibrated against Computer Tomography
(CT) virtual dissection.
OPPSUMMERING (Summary in Norwegian)
Hovedmålet med dette arbeidet var å studere måling og prediksjon av sammensetningen
(kjøtt, fett og bein) av norske lammeslakt ved bruk av forskjellige måleteknikker som strekker
seg fra subjektiv visuell bedømming til data-intensive instrumentelle metoder. Det er et
konstant ønske, både fra produsenter og foredlingsledd av kjøtt, om et mer objektivt og
pålitelig system for prediksjon av kjøtt, fett, bein og fastsettelse av verdi i et lammeslakt. Når
man introduserer og kalibrerer nye teknikker for bestemmelse av sammensetningen, er man
helt avhengig av en presis og pålitelig referansemetode. Nøyaktigheten til dagens
referansemetode, nedskjæring av slakt, har aldri blitt kvantifisert. Et optimalt system for
bestemmelse av sammensetningen i lammeslakt vil ikke kunne dra nytte av en god
måleteknikk når referansemetoden ikke er tilstrekkelig god nok. For å oppnå en høy
pålitelighet av et system, må usikkerheten eller feilen i referansemetoden kunne oppgis. Ved å
kombinere en god referansemetode med en god kalibrering av målesystemer, vil man kunne
kvantifisere usikkerheten og forklaringsgraden til målesystemer for bestemmelse av
kroppsinnhold i lammeslakt.
Resultatene i denne avhandlingen viste at det nåværende klassifiseringssystemets (EUROP)
bruk av subjektiv bedømming er pålitelig, men nøyaktigheten for prediksjon av
sammensetningen i lammeslakt, spesielt for muskelvev og fastsettelse av verdi, er ikke god
nok. Nedskjæring av slakt ved bruk av et panel av kjøttskjærere, viste seg å være akseptabel
som referansemetode for å bestemme sammensetningen av lammeslakt. Resultatene var noe
varierende for utbytte av stykningsdeler og innhold av kjøtt, fett og bein i stykningsdelene.
Skjærepanelet hadde store problemer med nedskjæring av bryststykket. Ytterligere
oppmerksomhet må rettes mot presisjon ved stykking av slakt, spesielt for bryststykket, for å
oppnå enda høyere nøyaktighet i referansemetoden nedskjæring av slakt. Resultatene har vist
at datatomografi (CT) er et godt alternativ til nedskjæring av slakt, og CT var både mer presis
og mer pålitelig enn nedskjæring av slakt. Ved å utnytte de spektroskopiske egenskapene til
pikselverdier i CT-bilder, og koble data mot nedskjæring, kan man estimere og studere
sammensetningen i lammeslakt ved bruk av multivariat kalibrering. De faste kostnadene (CT-
skanner og utstyr) er noe høy, mens driftskostnadene på sikt er mye lavere enn ved
nedskjæring. Evalueringen av forskjellige teknikker for å predikere sammensetningen i norske
lammeslakt viste at det mest presise og pålitelige systemet ved nåværende tidspunkt, synes å
være ”on-line” optisk probemåling av sidetykkelse kalibrert mot CT.
LIST OF PAPERS
I. J. Johansen, A.H. Aastveit, B. Egelandsdal, K. Kvaal and M. Røe (2006). Validation
of the EUROP system for lamb classification in Norway; repeatability and accuracy of
visual assessment and prediction of lamb carcass composition. Meat Science 74: 497-
509.
II. J. Kongsro, B. Egelandsdal, K. Kvaal, M. Røe, A.H. Aastveit (2008). The reference
butcher panel’s precision and reliability of dissection for calibration of lamb carcass
classification in Norway. Animal, Submitted manuscript.
III. J. Johansen, B. Egelandsdal, M. Røe, K. Kvaal and A.H. Aastveit (2007). Calibration
models for lamb carcass composition analysis using Computerized Tomography (CT)
imaging. Chemometrics and Intelligent Laboratory Systems 87: 303-311.
IV. J. Kongsro, M. Røe, A.H. Aastveit, K. Kvaal and B. Egelandsdal (2007). Virtual
dissection of lamb carcasses using computer tomography (CT) and its correlation to
manual dissection. Journal of Food Engineering, In Press, Accepted Manuscript.
V. J. Kongsro, M. Røe, K. Kvaal, A.H. Aastveit and B. Egelandsdal (2007). Prediction of
fat, muscle and value in Norwegian lamb carcasses using EUROP classification,
carcass shape and length measurements, visible light reflectance and computer
tomography (CT). Meat Science, Submitted manuscript.
Note: The author J. Johansen has changed his name as from 12th of July 2007 to J. Kongsro.
1
Background and motivation
Grading and classification of farmed animal carcasses and determination of carcass value are
the basis for the economical interface between the farmers and abattoirs in Norway. It is
critical to have an accurate and reliable determination of carcass quality and its value. The
definitions of accuracy and reliability are not always equal between different fields of
science. Accuracy is defined, from a technical and general perspective, to be an
approximation to a certain expected value (Hofer et al., 2005). Esbensen (2000) defined
accuracy as faithfulness of a method, i.e. how close the measured values is to the actual or
true values. Accuracy has to be seen in relation to precision, which indicates how close
together or how repeatable the results are (information about measurement error). Reliability
is defined as to express a degree of confidence that a part or system will successfully function
in a certain environment during a specified time period (Juran and Gryna, 1988). This means
to minimize uncertainty or doubt about the validity of the measurement method or experiment
(Martens and Martens, 2001), expressed as experimental error. For prediction of lamb carcass
composition and value, accuracy is defined as the relationship or closeness between the actual
and predicted value for the lamb carcass tissues and value, and is expressed as explained
variance (R2) and prediction error (RMSEP). Precision of measurements is the degree to
which measurements show the same or similar results, and is expressed as the ratio between
standard deviation of the difference between two repeated measurements and the mean value
of the measure (expressed as coefficient of variation, CV %). Reliability is expressed as the
correlation (Pearson’s r) between repeated measurements.
The major motivation behind this work was to characterize and predict lamb carcass
composition and value using a range of technologies, spanning from simple, univariate
carcass weighing, to computer intensive Computer Tomography (CT). It is crucial to know
what is measured, its relation to carcass composition and value and the accuracy and
reliability of the measurement. Another important feature of the measurements is how it can
be applied in abattoirs. Is one type of technology more relevant in small scale abattoirs in
comparison to larger ones? What is most crucial, speed, cost or accuracy?
For sheep, the classification system in Norway is under constant debate with respect to
accuracy and reliability. Sometimes, the sheep farmers are not satisfied with the current
classification system, and complain that their animals are not correctly assessed (i.g. obtain
2
too low classification scores) compared to other farmers in other parts of the country. An
example from the US, shows that some cattle producers are reluctant to market cattle on a
carcass merit system because of subjective grading (Savell and Cross, 1991). The sheep
farmers in Norway seems to be less reluctant as the farmers in the US, however, the same
problem prevails here also for both sheep and cattle farmers. Sometimes, the meat processors
argue that the current system does not reflect the real value of the carcass, and the payment to
farmers does not correspond to the yield obtained from different classes of carcasses. Another
Norwegian example which highlights the disparity between classification and yield is the
abattoirs reluctance towards cutting carcasses with high conformation class. The price level of
high conformant carcasses is too high compared to the saleable meat yield obtained from the
carcasses. The opposite situation with respect to carcass prices is the willingness to cut low
conformant carcasses due to the low price of carcasses compared to the saleable meat yield
obtained from them. This situation highlights the need to have a price system which is reliable
and reflects the value and yield obtained from the carcasses. The implications or usefulness of
any technology for prediction of lamb carcass composition will depend on the future
commitment of the sheep industry to developing a lamb price system based on carcass or
primal cut composition (Berg et al., 1997).
During the last decades, methods for measuring lamb carcass composition have moved from
subjective appraisal towards more objective and computer intensive methods. Scientifically,
the development of methods for prediction of lamb carcass composition is moving forward,
however, the application and practice in the meat industry has not kept up with the science.
The pig industry is the most advanced of the meat industries with respect to objectivity and
use of new technologies in practice (Kirton, 1989). Even though the disadvantage of using
subjective appraisal has been document in several studies (Diaz et al., 2004; Kirton, 1989;
Swatland, 1995), the lamb meat industry still applies subjective methods for prediction of
lamb carcass composition. There seems to be a huge gap between science and practice in
terms of prediction of lamb carcass composition. In Norway, the European classification
system EUROP is used for determination of lamb carcass composition. The system is based
on visual appraisal of carcass conformation and fatness, in addition to carcass weight, sex and
age. In addition to the system being based on subjective appraisal, the major concerns have
been relationship between classification and saleable meat yield, and the confounding
between conformation and fatness. The confounding is due to carcasses with thicker fat cover
tend to be judged to have better conformation (Navajas et al., 2007).
3
In most cases, the national sheep population in previous studies, does not reflect the
worldwide sheep population, especially with respect to fatness (Diaz et al., 2004). The carcass
weight, breed and time of slaughter (maturity) of sheep varies between regions, i.e.
Mediterranean lambs having a carcass weight of approx. 10 kg compared to northern
European lambs (UK, Germany) of approx. 22 kg. It is difficult to have a global validity of
studies performed on carcasses sampled around the national or regional mean carcass weight.
Sampling of lean vs. fat carcass and proper validation must be taken into consideration when
addressing global prediction models which are valid both scientifically and for practical
applications in abattoirs worldwide. Building a solid experimental design for sampling will
make the modeling of measurement systems more efficient, bring focus and ensure a more
global variability. This must be the overall aim from a sampling point of view, even when it
may seem difficult in practice.
During recent years, new computer intensive and technologically advanced measurements
have become available for prediction of lamb carcass composition. However, the studies or
applications of these new emerging technologies have been too narrowly focused, or have not
been adapted for sheep (i.e. developed for pigs). When applying new technologies for
classification or prediction of lamb carcass composition, the precision of measurements in an
industry environment is of the greatest importance. In a scientifically controlled experiment,
the precision of measurements will most probably be better than in an industrial environment.
This may be one of the main reasons why science has not kept up with industry applications.
Berg et al. (1997) stated that further testing of emerging technologies in an industrial setting is
needed before adoption of specific technology to quantify lamb carcass composition can
occur. Precision studies including repeatability and reproducibility standard deviations,
preferably in an industrial environment, can help bring the gap between science and industry
closer together.
Emerging technologies which are computer and technology intensive, challenge the modeling
and analysis of measurement data. The data generated by these instruments are often complex
(i.e. spectral, image or profile data) and are characterized by being multi-component and
having many-to-many relationships. The data may also be organized not only as matrices of
rows and columns, but as multi-level matrices (i.e. 3D cubes). The basis of statistical
modeling is to separate the relevant information in a data set from the background noise. By
introducing computer intensive chemometric methods such as Partial Least Square Regression
4
(PLSR) for 2-way (rows*columns) and multi-level PLSR (NPLSR) and Parallel Factor
Analysis (PARAFAC) for multi-way modeling and analysis of data, calibration and prediction
of lamb carcass composition can be carried out in a short time collecting relevant information
from the complete spectrum of complex instrument data. Meat science, like other food
sciences, draws on a wealth of disciplines from chemistry and physics, mathematics and
statistics, to biology, genetics, medicine, microbiology, agriculture, technology and
environmental science, and even further to the cognitive sciences like sensory and consumer
analysis and psychology as well as to other social disciplines like economy (Munck et al.,
1998). Such a wide field of sciences increases the need for the establishment of basic
principles for multivariate data analysis. Chemometric methods can contribute to food and
meat science with new more flexible data programs which display the exploratory results in
cognitively accessible graphical data interfaces.
The aim of the project was to evaluate state of art technologies for grading and classification
of lamb carcasses, and to study the accuracy and reliability of the different technologies for
prediction of lamb carcass composition and value. New approaches for calibration and data
analysis are also addressed to achieve robust prediction models of carcass tissues like fat and
muscle, and the value or yield of products derived from lamb carcasses.
5
Dissection, cutting and value of cuts from lamb carcasses
The main tissues of a lamb carcass are (proportion average; decreasing order) muscle, bone
and fat. Dissection of carcasses is defined as separation of the different tissues in carcasses
where the main purpose is scientific analysis, such as anatomical studies. Cutting of carcasses
is defined as separation of carcass tissues performed by a butcher with respect to producing
meat for consumption and to maximize profit. Dissection is performed in controlled scientific
environments; while cutting is performed in industrial environments. Lamb cutting in Norway
is based on three primal cuts; legs, side and forepart, and their respective five sub-primals;
legs, loin, side, shoulder and breast (Fig. 1). The five sub-primal cuts are cut into retail
products such as filets, steaks, manufacturing meats, fat and bone. In addition, residual tissues
like glands are removed, as waste, at time of cutting. The leg (proximal pelvic limb) may be
cut long or short, with or without the sirloin (Swatland, 2000). The mid-part (lumbar region)
of the carcass is divided into loin and flank or side (Fig. 1). The shoulder (proximal thoracic
limb) is removed to contain the large anterior (forepart) bones (Os scapula, humerus, ulna and
radius), leaving the anterior ribs and cervical and anterior thoracic vertebrae as a breast with
neck (Swatland, 2000) (Fig. 1). The Norwegian dissection of lambs is based on guidelines
supervised by Gunnar Malmfors, SLU, Sweden, exemplified in a Swedish Master Thesis
(Einarsdottir, 1998) and the EAAP standard described by (Fisher and de Boer, 1994).
6
1
2
3
45
Figure 1. Norwegian sub-primal cuts; lamb carcass. Shoulder (proximal thoracic limb, 1), breast (neck and anterior thorax, 2), side (lumbar, ventral side, 3), loin (lumbar, dorsal side, 4) and leg (proximal pelvic limb, 5). Surrounding pictures: Different retail products derived from lamb carcass primal cuts.
The loin and the leg for all livestock animals are in average higher priced compared to the
side, shoulder and breast. This is due to the high content of tender and lean muscle i.e. M.
longissimus dorsi in loin and M. semimembranosus in leg. In Norway, there are some
exceptions, i.e. during Christmas where the side of pig and lamb is highly appreciated. The
retail products derived from lamb leg and loin are roast, filets and lean manufacturing meats.
The side is mostly used for rolls and cold cuts, and the largest retail products from shoulder
and breast are stew meat with bone (for sheep and cabbage stew, which is a Norwegian
tradition) and manufacturing meats with higher fat content compared to leg and loin.
When dissection is used as a reference method for grading, classification or breeding traits,
one must be able to quantify the size of the error and bias. Introduction of new classification
or grading methods, or maintenance of existing methods, will be compared through the
accuracy of the reference method. A large error and bias in the reference method will
eventually lead to a poorer reliability for the whole system for lamb carcass classification and
grading. For dissection of pig carcasses, the accuracy of dissection was high, although
7
significantly different dissection results were found between butchers with respect to lean
meat percentage (Nissen et al., 2006). The dissection of ruminants like sheep is more complex
compared to non-ruminants such as pig, due to differences in level of subcutaneous fat (higher
proportion in pig carcasses). An international reference method for lamb carcass
measurements and dissection procedures was presented in 1994 (Fisher and de Boer, 1994),
where the approach was to describe carcass form and size, and quantify carcass composition.
The reference method involved four stages of operation: Measurement of carcass dimensions,
preparation of half carcass to a defined standard, carcass jointing and tissue separation. All
stages were defined so that it could be implemented by all research groups in this international
reference exercise. However, the authors stated that it was probably too costly to carry out
studies on carcass composition involving a large number of animals. In Norway, the tradition
has been to dissect carcasses to produce saleable products (commercial dissection).
Commercial dissection is based on separation of saleable retail products (lean muscle,
manufacturing meats, fat and bone) rather than complete anatomical dissection. The main
advantage of commercial cutting is that the dissected parts produced are saleable (industry
products; steaks, filets, manufacturing meats etc) after dissection, which makes the operation
less expensive, and the cutting trials can involve a larger number of animals. The
disadvantage of commercial cutting is that the procedure is difficult to harmonize between
countries, since commercial industry products may vary in shape, size and fat/lean ratio
between countries. Complete anatomical dissection is regarded to be the theoretical value of
carcass components, while commercial dissection is the economic value of the carcass
components, reflected by i.e. saleable meat yield.
8
Classification of lamb and sheep carcasses; the EUROP classification
system
Grading is defined as a single measurement or set of measurements sampled from carcasses
to assign or estimate the amount or value of meat, fat and bone obtained from carcasses.
Classification is defined as sorting or classifying carcasses into groups or meat trade classes
which reflect the value and allow sorting of carcasses for further processing of fresh meat
merchandising, and transfer information back to the farmers (Kvame, 2005).
Classification of sheep and lamb has been carried out systematically in Norway since 1931
performed by trained operators or assessors. Category (age and sex), carcass weight,
conformation and fatness have formed the basis for classification. In 1996, the European
classification system EUROP was introduced in Norway. EUROP is very similar to previous
classification systems in Norway, based on a subjective assessment of category, conformation
and fatness, in addition to carcass weight. However, like any other subjective system, the
system has its weaknesses with respect to accuracy and reliability within and between
operators or assessors. The reference method used for the EUROP system is based on
quantified expertise according to EU commission standards (Commission Regulation (EC) No
823/98, 1998; Commission Regulation (EEC) No 461/93, 1993), but has never been validated
with respect to fat content, saleable or lean meat yield. For cattle, it was stated that the EC or
EU plan for grading and classification had two main disadvantages: it is subjective, and the
carcass characteristics that determine value are not recorded accurately enough. There is no
lack of demand for the recording of carcass values to be objectivized (Augustini et al., 1994).
This situation is also valid for sheep. For cattle, the inclusion of conformation in the EUROP
system was done to make the classification system more acceptable to meat trades concerned
than because of the additional accuracy of the yield information provided (Colomer-Rocher et
al., 1980). Little evidence supports the use of conformation as a classification factor for
predicting meat yield in sheep (Kirton, 1989).
The EUROP system is based on visual appraisal of carcass conformation and fat cover laid
down by the EU Commission (Commission Regulation (EC) No 823/98, 1998; Commission
Regulation (EEC) No 461/93, 1993) (Fig. 2).
9
Figure 2. Visual appraisal of lamb carcass conformation and fat using EUROP classification system.
Table 1. EUROP classification system; conformation class E-U-R-O-P and fat class 5-4-3-2-1, with +/- for each class. Numerical discrete scale from 1 to 15 for each class with +/-. Conformation + E - + U - + R - + O - + P -
Scale 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Fat + 5 - + 4 - + 3 - + 2 - + 1 -
The system is based on 5 main classes, both for conformation and fat cover, with the
possibility of extending +/- for each class, making the total number of classes 15 (Tab. 1).
Conformation is classified using the letter E-U-R-O-P, where E is the most convex
conformation group (Fig. 3). Fat cover is classified 1-2-3-4-5, where 5 is the highest fat cover
(Fig. 3). In some cases with extreme conformation, an additional S has been added (S-
EUROP), i.e. for Belgian blue cattle and callipygian gene sheep.
Figure 3. Left: EUROP conformation classification of lamb carcasses. Carcass with convex shape (U+) vs. a carcass with concave shapes (P). Right: EUROP fat classification of lamb carcasses. Carcasses with low fat cover (1) vs. a carcass with high fat cover (5).
10
From a scientific perspective, one of the shortcomings of the EUROP classification system is
that conformation tends to be confounded with fatness, i.g. conformation tends to be
correlated with fatness (Navajas et al., 2007). It is difficult to obtain lean, high conformant
carcasses in sheep population, even though some callipygian gene sheep have shown to yield
lean and high conformant carcasses. In general, any improvement in conformation will
inevitably lead to increased fatness and lead to a lower proportions (%) of lean meat.
One of the main objectives of the EUROP classification system for sheep is to improve
market transparency in the sheep meat sector; (Council Regulation (EEC) No 2137/92, 1992).
In order to improve the market transparency, a more objective, accurate and reliable
classification standard is needed, based on the direct relationship between the amount of lean
meat and fat content, and the value of saleable meat obtained from the lamb carcasses.
11
Measuring systems for lamb carcass composition
Measurement systems for lamb carcass composition must be based on robust predictions that
explain highest possible carcass and meat variation, and provides the lowest possible
prediction error. Berg et al. (1997) stated that determination of carcass yield and composition
must be determined by instrument means that can be monitored, standardized, and regulated
(Berg et al., 1997). One of the best established and accepted sheep carcass grading systems is
that in New Zealand, which is the largest international trader of sheep meat products (Kirton,
1989). The system is based on objective carcass weighing and fat classes specified
subjectively or objectively by grading rule (GR) total tissue thickness in the region of the 12th
rib, 11 cm from the dorsal mid-line. The GR is assessed by a metal ruler or grading probe.
Due to high chain speed, the bulk of New Zealand sheep carcasses are classified subjectively
for fatness, however improvements are being made to measure fatness electronically on-line
at chain speed at least as accurately, preferably more accurately, than the subjective
measurement (Kirton, 1989). Recent advances of on-line carcass grading in New Zealand
involve i.e. Video Image Analysis (VIA) and visible light reflectance probing with frames for
classification of lamb carcasses (Chandraratne et al., 2006; Hopkins et al., 1995; Kirton et al.,
1995). New marketing initiatives have been introduced, involving payment of farmers based
directly on the assessment of carcass value using ultrasound, Computer Tomography (CT) or
Video Image Anaysis (VIA) (Jopson et al., 2005).
Objective systems for prediction of lamb carcass composition have developed from easily
obtainable carcass measures such as specific gravity or the ratio of the density of a given
substance, to the density of water (H2O) (Barton and Kirton, 1956), carcass weight, backfat
thickness, kidney fat weight and sub-primal weight (Judge et al., 1966), towards more
advanced and computer – and equipment intensive measurements using Bioelectrical
Impedance (BIA) or Computer Tomography (CT) (Berg et al., 1994; Lambe et al., 2006).
Visual scores and linear carcass measurements
Kempster et al. (1986) exemplified linear measurements, visual scores and the proportions of
tissues in primal or sub-primal cuts as predictors of carcass composition (Kempster et al.,
1986b). The result from this study outlines the importance of breed differences, especially in a
highly diverse population of sheep. The methods are based on subjective appraisal of the
carcasses similar to the EUROP system. The results showed that there was a considerable bias
12
(predicted vs. actual lean percentage) when applying an overall (global) prediction to
individual breeds. No significant sex differences were found. Joints and combination of joints
with high predictive precision tended to have predictions that were robust to differences
between breeds. The convex and concave shapes of carcass conformation can be assessed
more detailed or objective than the EU Commission guidelines for the industry. Unpublished
trials for scientific use have been tested in Norway using a more detailed assessment of
conformation across the entire carcass. Linear shape and size measurements of conformation
from the unpublished Norwegian trial are shown in Figure 4 (from paper #5); utilizing the
convex and concave shapes on a carcass more objectively using i.e. rulers and measuring
tapes.
Figure 4. EUROP advanced carcass shape (white or gray L1-L4, R1 and F1-F2) and length / width (black) measurements based on the detailed rules laid down by the EU commission concerning the classification of ovine animals. In addition, carcass length from 1st anterior rib to carcass steel hook was measured (from paper #5).
Video image analysis (VIA)
Video image analysis (VIA) is a fast and automatic method to assess the shape, length and
color of carcass surfaces. The technology is based on objective and computed assessment of
carcass shapes, lengths and surface color from digital images captured by a charge-coupled
device (CCD) camera on-line (Fig. 5) (Hopkins et al., 2004; Newman, 1987; Stanford et al.,
1998; Swatland, 1995). In a comparison study, a video image analysis system developed by
Meat and Livestock Australia, VIAScan®, was compared to hot carcass weight (HCW) and
tissue depth at grading rule (GR) site (thickness over the 12th rib, 11 cm from the midline),
13
with respect to prediction of lean meat yield (Hopkins et al., 2004). A greater prediction
accuracy (R2=0.52) was achieved by the VIAScan® system compared to HCW and GR
(R2=0.41). The VIAScan® system offered a workable method for predicting lean meat yield
automatically. The video image device Lamb Vision System (LVS), accounted for 50-54% of
the observed variation in boxed carcass value, compared to traditional HCW based value
assessment which accounted for 25-33% of the variation in boxed carcass value (Brady et al.,
2003). The LVS assessed individual lamb carcass value more accurately than the traditional
HCW assessment. Interestingly, the LVS was found to be highly accurate with respect to
prediction of lamb fabrication yields, with a repeatability of 0.98 (Cunha et al., 2004). For
beef carcasses, it was found that VIA was equally accurate to the EUROP classification scores
plus HCW in predicting saleable and primal yield (Allen, 2003). In a Norwegian trial using
the E+V vision system VSS2000 for lamb carcasses, it was found that VSS2000 compared
well with EUROP conformation scores (Berg et al., 2001). The repeatability was higher for
VSS2000 compared to trained operators for EUROP scores. In EU member states, new
technologies presented for carcass classification must be approved according to EU
Commission standards (Commission Regulation (EC) No 1215/2003, 2003). An annex was
added to this regulation in 2003, setting conditions and minimum requirements for
authorisation of automated grading techniques for beef. This annex is also valid for lamb,
since the requirements are equal, in practice. These requirements are based on prediction of
EUROP grading or classification scores, and not weight or yield of meat and sub-primals. The
prediction of EUROP scores will be a prediction of a prediction, since EUROP is a method
for predicting market value. This cannot be considered an optimal solution in practice, and
raises the following question: What is the actual reference; EUROP scores or weight / yield?
The common practice in some countries have been to meet the requirements of the EU
commission for EUROP grading and classification towards farmers, and use the VIA systems
for predicting saleable meat yield within the company for process control. The main concern
from the EU Commission is that saleable meat yield is difficult to standardize and to
harmonize between the member states. For now, it seems like harmonization is favoured in
contrast to higher accuracy and estimation of yields by using VIA and other automatic
technologies. In Norway, the VSS2000 system has not yet passed the requirements for
prediction of EUROP scores. The use of the system for on-line prediction of primal cut and
saleable meat yield has not yet been fully utilized in Norway, however, the system have
shown to be very accurate (Berg et al., 2001). The trend in Europe seems to shift towards the
same marketing initiatives involving payment of farmers based directly on the assessment of
14
carcass value by VIA in New Zealand (Jopson et al., 2005). In New Zealand, one of the
largest meat processors has recently installed VIA systems in all of its sheep plants, and the
other meat companies are working on similar systems (Jopson et al., 2005). Despite VIA’s
recent popularity in the meat industry, the main future challenge for VIA systems, however, is
to introduce a new reference or payment system based on saleable meat yield or the value of
the carcass directly. The experience so far has been that this is a rather slow process where the
changes will be gradual.
Figure 5. Video Image Analysis. CCD image of lamb carcass.
Visible light reflectance probing
Visible light reflectance probing is a spectroscopic method which utilizes the reflectance of
visible light from different types of tissues. The probe is inserted into i.e. the loin of a carcass,
and a profile of the loin, from back-fat to the body cavity (costa) is measured (Fig. 6). The
probe is an evolution of the manual caliper used to perform length and width measurements.
The data generated for industrial use from the probe are fat and muscle thickness. The tip of
the probe contains a light-emitting diode followed by a light detection device (Berg et al.,
1997). Muscle and fat tissue reflects the light differently, and this difference is used to
measure muscle and fat depth at the probe site. Optical probes are considered to be invasive,
although penetration damage is minimal (Swatland et al., 1994). Optical probing is currently
used in Norway and other European countries for grading of pig carcasses by measuring
backfat and m. longissimus thickness. Recent advances of the probe provide the color and
level of marbling in the muscle. The color can be related to meat quality attributes, and is
currently used in Norway to identify Pale Soft Exudative (PSE) meat on pigs. However, it has
recently been questioned in the Norwegian pork meat industry how increased marbling (intra
15
muscular fat) impacts the measurements. This concern may be excessive, since the “noise”
from marbling can be modeled statistically and may not compromise the accuracy of
measurements. In New Zealand and Australia, lamb and sheep carcasses are graded using
grading probes, measurements of back-fat in the same fashion as pig carcasses in Europe.
Probing by using GR or other back-fat measures is considered to be more robust and accurate
compared to visual appraisal using the EUROP system (Kempster et al., 1986a). Probe
measurement of backfat thickness between the 12th and 13th rib provided a superior method
compared to visual assessment for prediction of lean content in lamb carcasses (Jones et al.,
1992). In Europe (including Norway), there has been a major concern using probing for sheep
and cattle, due to large variation in breeds and crossbreeds, and damaged subcutaneous fat
cover during slaughter and hide-pulling (Augustini et al., 1994; Kirton, 1989). In Iceland,
probe measurements (ICEMEAT probe) of backfat and side thickness has proven to be
successful (Einarsdottir, 1998), probably due to a very homogenous population of sheep
(Icelandic sheep breed). In Iceland and New Zealand, no major concerns have been raised
concerning damaged subcutaneous fat during slaughter (Kirton, 1989), however there are
some concerns due to positioning and operation of the probe at high chain speed.
Figure 6. Visible light reflectance probe (Hennessy Grading Probe®). Measurement of lamb side and backfat thickness assessed by the author J. Kongsro. Reflectance profile from Hennessy Grading Probe®, from backfat to body cavity. Reflectance peaks (white) at back-fat and costa (high fat).
The repeatability of probe measurements is highly dependent on the operator of the equipment
(Olsen et al., 2007). Robotics or support frames can increase the repeatability of
measurements by visible light reflectance probing (Swatland et al., 1994). The cost of
equipment is also an issue; however, the price of visible reflectance probes is relatively low.
Robotics and support frames will also increase cost; however, increased repeatability will pay
off over time. Stanford et al. (1998) found that the increased accuracy of optical probing
compared to manual GR measurements of back-fat, was likely due to improvements in the
accuracy of prediction of carcass composition of cold as compared to warm carcasses. The
reason for the improvement in accuracy and repeatability of cold vs. warm carcasses may be
16
errors caused by fat bubbles in subcutaneous fat when the hide is removed from warm
carcasses. During chilling of carcasses, the fat bubbles are reduced significantly and the
subcutaneous fat layer obtains a more even shape and thickness. The effect of fat bubbling on
subjective appraisal or VIA has, however, not been documented. Information on meat color
and quality from GP is an additional advantage. When measuring meat color, time post
mortem is of great importance. Measurements of color 24 hours post mortem and 7 days post
mortem are different (Linares et al., 2007). The accuracy of probes can probably be improved
by increasing the number of measuring sites, sampling from several anatomical positions
along the carcass. However, the penetration damage may increase by adding probing sites,
and may be too invasive in practice. The operation at high chain speed may also be an issue
when introducing several measuring sites.
Total Body Electrical Conductivity (TOBEC) and Bioelectrical Impedance (BIA)
Total Body Electrical Conductivity (TOBEC) and Bioelectrical Impedance (BIA) are methods
which utilize the transfer of an electrical current through biological material like a lamb
carcass. Lean tissue is much more conductive than fat and bone tissue due to the high
concentration of water and electrolytes in the tissue (Stanford et al., 1998). A fat lamb carcass
should impede the transmission of electrical current to a larger extent than a lean lamb (Berg
et al., 1996). Using this difference between tissues in electrical conductivity or impedance, the
carcass composition can be predicted. Berg et al. (1996) also found that individual electronic
methodologies tested in their study were moderate predictors of proportional carcass lean
(Berg et al., 1996). Another study reported that the impedance method is not suitable for the
prediction of carcass composition, neither in lambs of similar weight nor in heterogeneous
animals (Altmann et al., 2005). For TOBEC, is was found that the research approach using
electromagnetic scanning was not a reliable tool for predicting body composition of live
lambs (Wishmeyer et al., 1996). Overall, it seems that methods using transfer of an electrical
current through a lamb carcass need to be further developed to achieve higher accuracy and
reliability.
Computer Tomography (CT)
Computer Tomography was introduced for medical diagnostics in the 1970’s (Hounsfield,
1973), for which G. N. Hounsfield and A.M. Cormack received the Nobel Price in Medicine
in 1979. The method is computer intensive, and the principle is based on X-ray attenuation
through an object, where an X-ray source and detectors rotate 360o around the object (Fig. 7).
17
For sheep, CT has primarily been used for selection of breeding traits (Kvame, 2005) and
prediction of lamb carcass tissue weights (Junkuszew and Ringdorfer, 2005; Lambe et al.,
2003).
Figure 7. Left: Computer Tomography (CT) scanner. Lamb carcass subject for assessment. Right: CT Tomogram Image. Image sampled from mid-part of carcass (11th rib).
X-ray images are generated during rotation of the X-ray tube, and data recovered from the X-
ray detectors are reconstructed by a computer to form a tomogram or CT image of the entire
object, both internally and externally (Fig. 7). A set of CT images from a set of trans-sectional
images or spiral scanning can be used to generate 3D images or volumes of the object
subjected for study. Different tissues produce different degrees of X-ray attenuation,
reflecting their density, thickness and atomic number (Harvey and Blomley, 2005). Lower
density tissues will appear more transparent than higher density tissues to X-rays. Air is
transparent to X-rays, and will appear black, while bone, due to its high mineral content, is
not very transparent, and appears white in CT images. In radiographic terms, the transparency
of X-rays is often called radiodensity, and is quantified in Hounsfield Units (HU), where the
X-ray attenuation of distilled water is used as a Hounsfield scale reference (HU=0). The
images generated from CT can be analyzed using the HU value of each pixel. CT images can
be organized according to spectroscopic profiles using the histogram of pixels, where the
intensity of pixels can be visualized according to the respective CT value (HU) (Fig. 8). Fat
tissue has a lower density compared to muscle tissue, and much lower density than bone
tissue. To get a better separation of tissues with respect to radiodensity, contrasting agents can
be added via feeding pre-slaughter or via blood vessels (i.e. for segmentation of internal
organs using iodine).
18
-200 -100 0 100 2000
2000
4000
6000
8000
10000
12000
CT value (HU)
Fre
qu
en
cy p
ixels
Figure 8. CT histogram pixels from 120 lambs (left) (samples from paper III). Soft tissue region from HU value -120 to 120. The first, smaller peak was identified as fat tissue, the second, larger peak identified as muscle tissue (right).
The CT histograms can be decomposed using two strategies: (1) utilize a priori knowledge or
windowing of CT values (Kalender, 2005) reflecting the CT values of fat, muscle and bone
tissue, or (2) through calibration of CT histograms against a known reference such as
commercial or full dissection (Dobrowolski et al., 2004). If the a priori knowledge is robust
and globally valid for new samples, the computation is both fast and efficient. If there are
differences in CT value windows or radiodensity for the same tissue (i.e. muscle) between and
within populations of lambs, the predictions will be less accurate using windowing. A pixel
will represent the mean value of the area covered by the pixel, and the pixel may sometimes
(i.e. border pixels between two types of tissues) represent an average of two tissues, making
discrimination between the tissues difficult. This mixed pixel distribution is called the partial
volume effect (Lim et al., 2006). It is therefore of great importance to perform calibrations by
using representative samples of the actual carcass population which CT is meant to predict.
Using the calibration strategy, the CT values are calibrated against real data sampled from the
actual population you want to model. The calibration is performed using the spectroscopic
approach, where the CT histogram is treated as a spectrum, and can be modeled using
multivariate calibration. Regression coefficients can be estimated from calibration, and can be
used as window levels or models for further prediction of carcass tissues. The disadvantage of
calibration, is that the reference method used (dissection) is often inaccurate and have poor
repeatability due to butcher or operator error, as shown for pig carcass dissection (Nissen et
al., 2006).
19
By using stereological methods such as the Cavalieri principle (Russ, 2002), unbiased
estimates of the tissue volumes can be obtained (Fig. 9). The CT images are organized in
sections based on the equipment settings and method, and the total volume of the segmented
tissue will be the area of tissue in the CT images, multiplied by the section distance.
Dissection seemed to be a choice between accuracy and number of samples; full tissue
separation vs. commercial dissection. CT can offer a combination of both, providing a high
number of “low-cost” estimates of full tissue separation. Dissection using CT is sometimes
nicknamed “virtual dissection”, where live animals or carcasses can be dissected in virtual
space using a computer. For industrial on-line use, it has been stated that CT would be too
slow, even if it is cost-effective (Stanford et al., 1998). Advances in CT technology since
1998, has proven that CT can operate during high speed in hospital environments. Single
scans of selected anatomical sites can in theory be obtained in 0.8 seconds (scan time;
protocol). High-speed dual-source computed tomography scanning (DSCT) of human hearts
have been performed with mean scan times of 8.58 seconds (Weustink et al., 2007). CT
scanners may be able to predict lamb carcass composition on-line at chain speed; it is just a
matter of designing a CT scanner for abattoir environments.
Figure 9. Cavalieri estimation and visualization of lamb carcass side using CT (left). Fat (yellow), muscle (red) and bone (light gray) segmented using windows presented by (Kvame et al., 2004).
20
Summary of methods and economical considerations
Table 2. Summary of different methods or technologies (systems) for prediction of lamb carcass tissues presented, with respect to explained variance and prediction error. System (independent) Tissue reference
(dependent)
Explained
variance
RSD
RMSE Reference
Live weight Muscle (kg) R2 = 0.96 (Teixeira et al., 2006) HCW Muscle (g) R2 = 0.92 RSD = 69.94 (Diaz et al., 2004) Leg fat (%) Carcass fat (%) R = 0.93 RSD = 1.55 (Kirton and Barton,
1962) Loin fat (%) Carcass fat (%) R = 0.97 RSD = 1.07 (Kirton and Barton,
1962) Specific gravity (hind saddle)
Carcass fat trim % R2 = 0.51 (Adams et al., 1970)
Linear carcass measures Total dissected lean (%) R2 = 0.72 RMSE = 2.55 (Berg et al., 1997) Linear carcass measures Total dissected lean (kg) R2 = 0.86 RMSE = 0.78 (Berg et al., 1997) Linear carcass measures Muscle (%) R2 = 0.63 RSD = 1.55 (Diaz et al., 2004) Linear carcass measures Fat (%) R2 = 0.84 RSD = 1.83 (Diaz et al., 2004) EUROP classification Fat (%) R2 = 0.57 RSD = 2.35 (Einarsdottir, 1998) EUROP classification Lean meat (%) R2 = 0.23 RSD = 2.54 (Einarsdottir, 1998) GR Carcass fat (%) R2 = 0.57 -
0.58 RSD = 2.97 (Kirton et al., 1995)
Ultrasound Total dissected lean (%) R2 = 0.26 RMSE = 4.46 (Berg et al., 1996) Ultrasound Total dissected lean (kg) R2 = 0.54 RMSE = 1.31 (Berg et al., 1996) Ultrasound Fat (%) R2 = 0.06 -
0.41 (Olesen and Husabø,
1992) HC Fat (%) R2 = 0.73 RSD = 2.06 (Einarsdottir, 1998) ICEMEAT Lean meat (%) R2 = 0.28 RSD = 2.53 (Einarsdottir, 1998) HC + EUROP Fat (%) R2 = 0.80 RSD = 1.80 (Einarsdottir, 1998) HC + EUROP Lean meat (%) R2 = 0.38 RSD = 2.46 (Einarsdottir, 1998) Electronic probe Carcass fat (%) R2 = 0.47 -
0.58 RSD = 2.99 - 3.48
(Kirton et al., 1995)
BIA Fat-free soft tissue (kg) R2 = 0.94 RSD = 0.43 (Jenkins et al., 1988) BIA + linear carcass measures
Fat-free soft tissue (kg) R2 = 0.96 RSD = 0.34 (Jenkins et al., 1988)
HCW + VIA (color + shape)
Saleable meat yield (%) R2 = 0.71 RSD = 1.43 (Stanford et al., 1998)
VIA + HCW Saleable meat yield (%) R2 = 0.64 RMSE = 3.30 (Brady et al., 2003) TOBEC Dissected lean (%) R2 = 0.62 RMSE = 2.97 (Berg et al., 1997) TOBEC Dissected lean (kg) R2 = 0.83 RMSE = 0.85 (Berg et al., 1997) CT Primal weight (kg) R2 = 0.85 -
0.98 RSD = 0.02 - 0.37
(Kvame et al., 2004)
CT Primal lean (kg) R2 = 0.80 - 0.98
RSD = 0.01 - 0.32
(Kvame et al., 2004)
CT Primal fat, subcutaneous and intermuscular (kg)
R2 = 0.82 - 0.98
RSD = 0.004 - 0.09
(Kvame et al., 2004)
CT Fat (kg) R2 = 0.80 - 0.84
(Junkuszew and Ringdorfer, 2005)
CT Muscle (kg) R2 = 0.63 - 0.65
(Junkuszew and Ringdorfer, 2005)
BIA = Bioelectrical impedance CT = Computer Tomgraphy GR = fat thickness, grading rule site (mm) HC = Icelandic Manual GR meter (hot carcass) HCW = hot carcass weight ICEMEAT = ICEMEAT GR probe (cold carcass)
Rack = lamb loin with ribs RMSE = Root Mean Square Error RSD = Residual Standard Deviation SE = Standard Error TOBEC = total body electrical conductivity VIA = Video Image Analysis
21
The usefulness of different measurements or methods from previous studies was compared in
table 2, with respect to explained variance (R2) and residual standard deviation (RSD) or root
mean square error (RMSE), when available. The table spans from live or carcass weight,
subjective appraisal and linear measurements, electronic probing and bioelectrical impedance,
and finally computer tomography (CT).
The usefulness for tissue composition in weights (kg) seems to be more accurate than those
for tissue proportion in percentage. For practical purposes, the most accurate solution seem to
be to estimate the carcass tissue in weight, then, an estimate of the proportion can be obtained
as a proportion of carcass weight; tissue (kg) * carcass weight-1 (kg). The results in Table 2
show that live or carcass weight is a very good single predictor of both fat and muscle weight
in kg. The best measuring systems in Table 2 with respect to explained variance, RSD or
RMSE seem to be Computer Tomography (CT). The authors used single scans from selected
anatomical sites (Junkuszew and Ringdorfer, 2005) or sequential scanning using 50 mm
section distances, with an average of 18 images per animal (Kvame et al., 2004). By using
denser scans with smaller section distances or spiral scanning, the accuracy may be improved.
Results from spiral scanning of pig carcasses have shown that the predictions were very good
and provided a fast volumetric scanning method of the entire carcass (Dobrowolski et al.,
2004; Fuchs et al., 2003; Kalender, 1994; Romvari et al., 2006). Using tissue proportions
obtained from primals have shown to be very well correlated with carcass tissue proportion
(Kirton and Barton, 1962). However, primal dissection used as predictor of carcass
composition is a laborious process, which has little relevance in a practical setting. The error
of determining the tissue reference (i.e. by dissection) has not been quantified in any of the
previous studies. A significant error in the reference will inevitably have an effect of the
precision of the measuring method. This can be solved by repeated measurements, i.e.
estimating paired differences between repeated measurements, depending on how costly or
time consuming the measurements are (Esbensen, 2000).
22
Multivariate calibration
The aim of calibration is to establish explanatory power and correlation between the different
classification, grading and measurement systems, and the “true“ quantity of muscle, fat and
bone in carcasses (Fig. 10). In addition, regression coefficients can be used to study the
impact (i.e. windowing of CT values) of the variables in the measurement system. The
different calibration models are validated using leave-one-out cross validation, test set
validation or a combination of both. The calibration models are evaluated in terms of
explained variance, prediction error and bias. The modeling is usually done by linear
regression, where the response y is the quantity of muscle, fat or bone from dissection or the
value of cuts, and Xi are the different classification, grading and measurement systems
variables i, b is the regression vectors of the i measuring system variables, and e are the
residuals. In matrix notation, the linear regression equation (1) can be written:
y = Xb + e (1)
where X=[1, x1, x2,….,xi] and b = [b0, b1,b2,…,bi]T
X
Classification
Grading
Measurement
systems
Y
Fat
Muscle
Bone
Value
Figure 10. Calibration of different measurement methods or technologies (X), and weights or proportions (quantity) of carcass tissues (fat, muscle and bone) and value (Y).
23
Table 3. Classification of data by their tensorial properties, and typical methods for data analysis (Escandar et al., 2006). Instrument data examples, regression method and second order advantage. Classification Order of
data
Sample
data set
Instrument
data
Typical
method
Second
order
advantage
Univariate Zeroth-order One-way - Fat thickness - EUROP fat score
OLSR No
Multivariate First-order Two-way - Set of fat thickness (GP probing) - CT histogram
PCR, PLSR No
Higher-order unfolded to first-order
Two-way CT histogram
Unfold PCR Unfold PLSR
No
Second-order Three-way CT histogram
PARAFAC NPLSR
Yes
CT = Computer Tomography
GP = visible light reflectance probing
NPLSR = N-way PLSR
OLSR = Ordinary Least Squares Regression
PARAFAC = Parallel Factor Analysis
PCR = Principal Component Regression
PLSR = Partial Least Squares Regression
Many instrumental measurements produce one, two or multidimensional arrays of data. The
different dimensions of data is called the order of data (Escandar et al., 2006). The different
dimensions of data produced by classification, grading or other measurement are seen as the
components of a first-, second- or nth-order tensor, respectively (Sanchez and Kowalski,
1987). The univariate case or zeroth-order of data can be exemplified by fat thickness
measured at a singe site as a single vector x and total fat from a carcass in kg as a y. This is
handled by Ordinary Least Squares regression (OLSR) (Tab. 3). Univariate calibration or
modeling using estimates to predict the quantity of carcass tissues are sometimes called direct
estimation. Another example of univariate calibration can be tissue estimates from CT
scanning using windowing. In this case, single estimates (vector x) from CT scanning is
calibrated against a cutting reference y. When introducing a set of measurement variables
such as EUROP conformation and fat classes, carcass weight and several fat thicknesses
probed by GP, we enter the multivariate domain with several variables in X. This is best
handled by multivariate calibration methods such as Principal Component Regression (PCR)
24
or Partial Least Square Regression (PLSR). The original sets of sampled responses within
these variables are transformed into scores by latent variable selection, and regression is
performed on these scores. Higher order data has recently been applied to a number of
different fields within analytical chemistry and food science (Andersen and Bro, 2003; Bro,
1996; Escandar et al., 2006; Huang et al., 2003). These data are provided by i.e. sampling
using multi-component instruments and cross-section images from CT. The data are
recognized by each sample providing a data array (multi-way) instead of a vector (2-way).
This multi-way data array can be handled in two different ways; either by unfolding the
higher order (I * K * L) data set to a first-order (two-way) data set by rearranging the data
across a higher order mode (IK * L) (Chiang et al., 2006). There are several advantages of
keeping the higher order data structure in the previous example, called the second-order
advantage. The second-order advantage makes it possible to utilize the multi-way structure,
like in the previous example, and extracting valuable information concerning the higher order
structure, i.e. cross section from CT images.
One of the requirements of linear regression is that the variables X should preferably be
independent or orthogonal (Martens and Martens, 2001). In measuring systems, the variables
are often correlated, and calibration and prediction may suffer from collinearity when using
OLSR. OLSR has a number of assumption, for example that the errors are independently
distributed and that the independent variables are not to strongly correlated or collinear
(Esbensen, 2000; Martens and Martens, 2001). When collinearity is high, it is almost
impossible to obtain reliable estimates of regression coefficients. It does not affect the ability
of the regression to predict the response; however, the estimates or contribution of the
individual regression coefficients bi becomes unstable. The main purpose of regression is to
seek the largest explanation of variance in y as a function of X. The obvious solution seems to
be removal of one or more of the correlated variables in X. Instead of looking at collinearity
as a problem, some multivariate calibration methods utilize the correlation between variables,
and construct a set of latent variables which are orthogonal (independent). The latent variables
are estimated as linear functions of both original input variables and the observations, and is
often called bilinear modeling (BLM) (Esbensen, 2000; Martens and Martens, 2001), as
shown in Figure 11. Principal Component Analysis (PCA) or Principal Component
Regression (PCR) and Partial Least Square Regression (PLSR) are some bilinear methods
which handle collinearity and construct a set of orthogonal latent variables called principal
components for further calibration. The goal of PCR and PLSR is to fit as much variation as
25
possible using as few PCs possible (Martens and Martens, 2001). The first latent variable or
PC explains the largest amount of variation, the 2nd the second largest, and so on. The original
variables are projected down to the PCs space, and are called loadings. The measurements or
information carried by the original variables are also compressed and projected down on the
PC space, and are called scores. Each sample has a score along each PC (Esbensen, 2000).
For each PC, we have loadings and scores which reflect the compression of the original data
structure with samples and variables (Fig. 11). The number of latent variables is always
smaller than the original data set; especially for spectroscopic studies, where the number of
variables (i.e. wavelengths) is very large. PCR focus on obtaining PCs from the X data array,
followed by regression of Y using the scores obtained from the PC. For PLSR, the modeling
of PCs is done by seeking the largest covariance between X and y or ensuring y-relevant PCs
from X (Martens and Martens, 2001). The result is that the PLSR models are simpler and
more compact models, and in most cases uses fewer PCs compared to PCR.
X t
l
=
Figure 11. Bilinear modeling. Latent variable decomposition of a data set X. Scores (t) and loadings (l).
The performance of a multivariate calibration model is quantified by validation. The purpose
of validation is two-fold (Esbensen, 2000): (1) to make sure that the calibration model will
work in the future, on new data sets and (2) to find the optimal dimensionality of the model to
avoid under- or overfitting. The overall aim of validation is to obtain the lowest prediction
error possible using the optimal dimensionality of the model. The calibration modeling error
is defined as the Root Mean Square Error of Cross Validation (RMSECV). The cross-
validated model is tested using a separate test set, and the prediction error is found using the
Root Mean Square Error of Prediction (RMSEP).
The bilinear modeling handles first-order data structures (samples*variables). For higher-
order data structures, i.e. second-order or three way data matrices, two original input spaces of
26
variables and the observations are modeled, and this is often called trilinear modeling. A set
of scores and two sets of loadings are estimated from the trilinear modeling (Fig 12). NPLSR
is PLSR for multi-way or higher order data, where trilinear modeling estimates a set of scores
and n set of loadings, where n is larger than 1. PARAFAC or Parallel Factor Analysis was
introduced in two parallel papers by (Carroll and Chang, 1970; Harshman, 1970) for
psychometric studies, and has been further developed for Chemometrics by Bro (Bro, 1997).
PARAFAC is a generalization of PCA into higher order data arrays, but is somewhat different
from the bilinear PCA (Bro, 1997). PARAFAC yields n number of loadings when there are n
modes or dimensions in the data, and often the first mode is named scores and represent the
information in samples or objects (Rinnan, 2004). The decomposition of data using
PARAFAC differs from PCA by providing unique solutions (Bro, 1997), calculating all
components simultaneously, different from PCA which calculates one component at a time.
The components in PARAFAC will represent the unique solution in X, while PCA will seek
the largest covariance in X. If the optimal number of components is selected, and the data is
trilinear or higher order in nature and a global optimum is achieved, PARAFAC is a robust
and strong tool for decomposition and modeling of multi-way data. While PCR, PLS and N-
PLS for multi-way data require reference samples for modeling (y), the uniqueness of
PARAFAC makes it able to estimate the true underlying profiles in the multi-way data set
(Khayamian, 2007). The optimal number of components can be found by different validation
techniques, like core consistency and split-half analysis (Trevisan and Poppi, 2003). If the
PARAFAC model is correct, then it is expected that the superdiagonal elements will be close
to one and the off-diagonal elements close to zero, and core consistency is achieved (Trevisan
and Poppi, 2003). In an optimal PARAFAC model, the core consistency should be as close to
100% as possible (Bro and Kiers, 2003). Another validation tool is split-half analysis. The
idea of this analysis is to divide the data set into two halves and make a PARAFAC model on
both halves. Due to the uniqueness of the PARAFAC model, one will obtain the same result
on both data sets, if the correct number of components is chosen (Christensen et al., 2005).
27
X t
l1
l2
=
Figure 12. Trilinear modeling. Latent variable decomposition of a data set X. Scores (t) and loadings (l1) for mode 1 and loading (l2) for mode 2.
Multivariate calibration methods have been successfully applied to a number of areas, but
spectroscopic measurements are typically used. In the meat industry, multivariate data
analysis can be helpful in analyzing, monitoring and modeling new measuring systems. Bro et
al. (2002) listed some main areas where multivariate data analysis can be a useful tool for
food production: visualization, optimization and calibration (Bro et al., 2002). All these areas
which can be utilized for the assessment of lamb carcass composition in relation to the
quantity of fat, muscle and bone, and the value of cuts obtained from the carcass, especially
for CT measurements sampling from cross-sections.
28
Main results of papers I-V and future perspectives.
This thesis focuses on reliable prediction and determination of lamb carcass composition
using different methods or techniques.
The objective of Paper I was to study the repeatability and accuracy of the EUROP
classification system applied in Norway. The assessors were highly reliable, achieving high
correlation between repeated measurements and between assessors. There were some
differences between abattoir operators and EU commission assessors, but these differences
were within limits accepted by the EU commission. The EUROP prediction of lean meat
percentage was poor, achieving relatively high prediction error and low explained variance.
The prediction of bone and fat percentage was somewhat better, especially for fat. This
showed that EUROP does not predict lean meat in carcasses very well, but is somewhat
accetable for prediction of fat.
The precision and reliability of lamb carcass dissection as the reference method for lamb
carcass classification and grading has never been quantified. In paper II, an estimate of the
reliability and precision of the reference butcher panel used for calibration of lamb carcass
classification and grading in Norway was obtained from a sample set of Norwegian lambs.
The goal was to develop a methodical framework to study the accuracy of lamb carcass
dissection in Norway; describe and obtain estimates of the precision and reliability of the
reference dissection in Norway for calibration of lamb carcass classification. The overall
precision and reliability was acceptable (reliability > 0.80) for carcass composition traits,
however, the results for sub-primal yield and composition were somewhat poorer. The sub-
primal breast seemed to be difficult for the butchers to dissect, and needs special attention
when setting up a dissection of lamb carcasses.
In paper III, the objective was to find the best prediction model for carcass soft tissues (fat
and muscle) using Computer Tomography (CT). The digital image data from CT scanning
was organized according to histograms of CT value and anatomical direction, yielding a
multi-way data array. Two strategies of modeling were tested. The first, direct estimation was
based on a priori thresholds of fat and muscle tissue in CT images or scores from PARAFAC
modeling of the multi-way data array. The second strategy was based on multivariate
calibration using 2-way PLS or n-way NPLS against a commercial dissection reference. The
29
results showed that multivariate calibration using NPLS gave the best results for fat and
muscle tissue with respect to prediction error (RMSEP). There were some biases between
measured (dissection) and predicted (CT) fat and muscle, and bias corrections proved to be
advantageous for the models.
In paper IV, the objectives were: (1) to obtain estimates of precision and reliability using
virtual dissection by CT scanning of lamb carcass, and (2) to test different equidistances or
section distances using sequential CT scanning with respect to correlation between manual
commercial and virtual dissection. The precision and reliability of virtual dissection was
higher (reliability > 0.95) compared to manual commercial dissection in paper II. Increasing
section distances gave poorer accuracy, which is an effect of poor modeling of irregular 3D
structures (i.e. bone cartilage) in carcasses. There were some biases between manual and
virtual dissection, especially for bone and muscle. This may be a combination of butcher error
and modeling by sequential scanning. Spiral scanning may solve the bias problem and
modeling of 3D structures, and may prove CT to be a more accurate reference compared to
manual commercial dissection.
In paper V, a number of different technologies for measuring carcass soft tissues (fat and
muscle) and carcass value were tested with respect to accuracy and prediction. Four
technologies were tested on the same data set, spanning from manual EUROP classification to
Computer Tomography (CT) scanning of carcasses. CT yielded the highest overall accuracy
and most unbiased predictions, both for fat and muscle tissue. Currently, CT may be too slow
and expensive for on-line, however, recent developments of CT scanners may operate at chain
speed in the near future. The chain speed at Norwegian abattoirs during lamb slaughter season
is approx. 300-400 animals per hour. The most practical solution at the time for prediction of
carcass soft tissues and value, seem to be optical probing of carcass side thickness calibrated
against a CT virtual dissection reference.
The calculation of costs when introducing new measuring systems for lamb carcass
composition needs further attention.
� Does the increased accuracy and reliability of an alternative or new measuring system,
relate to the running, development and training costs?
� Are some types of measuring systems more relevant for larger abattoirs than for
smaller ones?
30
In this work, different technologies for prediction of carcass composition and value have been
tested, and current and alternative reference methods used for prediction have been evaluated
with respect to accuracy and reliability. The speed and cost of maintaining the current
EUROP classification system must be compared with the development costs and maintenance
of new technologies. A cost-benefit study beyond this work will determine the future
developments of technologies for prediction of carcass composition and value. In addition,
CT images, once available may provide many other relevant data in slaughter houses:
intramuscular fat, abnormal water to protein ratios of lean meat. Palatability traits such as
tenderness, juiciness etc., have not been addressed in this thesis. These traits must be
considered in future work in development of new systems for carcass evaluation. CT scanning
has proven throughout this work as the most accurate and reliable tool for prediction of
carcass composition. Spiral scanning of carcasses was not applied in this work; however it
may prove to be the best solution, covering variation in complex 3D structures (i.e. bone
cartilage) in carcasses. For future work using CT scanning, spiral scanning is therefore highly
recommended. Whether the methods or technologies presented in this thesis are dependent on
size of abattoirs or plants may be discussed, but it seems obvious that smaller plants with a
smaller turnover of carcasses and meat will not be able to benefit as much as larger plants, i.e.
fixed costs of expensive equipment. The size of plants is a major concern when trying to
harmonize the classification or grading methods between and within countries or regions. This
emphasises the need of an objective and reliable reference, in which the plants can use as a
measure. New methods or technologies needs to be measured and validated against this
reference, in order to obtain solid risk assessment, both in terms of accuracy and cost.
During this work, an application using CT as a reference method for carcass composition and
value has shown to be more accurate, cheaper and reliable compared to manual dissection
performed by butchers. In terms of measuring systems, smaller plants can to a large degree
utilize carcass weight or simple linear measures, and still obtain an accuracy close (or
sometimes better) to more computer-intensive systems like VIA and BIA. When investing in
new technology for prediction of lamb carcass composition and value; the easiest solution is
most often the best one, and it all depends on the reference method used for prediction.
31
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Validation of the EUROP system for lamb classification inNorway; repeatability and accuracy of visual assessment and
prediction of lamb carcass composition
Jørgen Johansen a,b,*, Are H. Aastveit b, Bjørg Egelandsdal b, Knut Kvaal c, Morten Røe a
a Norwegian Meat Research Centre, P.O. Box 396, Økern, N-0513 Oslo, Norwayb Norwegian University of Life Sciences, Department of Chemistry, Biotechnology and Food Science, N-1432 As, Norway
c Norwegian University of Life Sciences, Department of Mathematical Sciences and Technology, N-1432 As, Norway
Received 10 October 2005; received in revised form 14 April 2006; accepted 14 April 2006
Abstract
The EUROP classification system is based on visual assessment of carcass conformation and fatness. The first objective was to test theEUROP classification repeatability and accuracy of the national senior assessors of the system in Norway. The second objective was totest the accuracy of the trained and certified abattoir EUROP classifiers in Norway relative to EU Commission’s supervising assessors.The third and final objective was to test the accuracy of the EUROP classification system, as assessed by the National senior assessors,for prediction of lean meat, fat and bone percentage and lean meat in relation to bone ratio. The results showed that the repeatability andaccuracy of the national senior assessors was good, achieving high correlations both for conformation and fatness. For the abattoir asses-sors, there were some systematic differences compared to EU Commission’s assessors, but these differences were within limits accepted byEU Commission. The relationship between abattoir and national senior assessors was good, with only small systematic differences. Thismay suggest that there also is a systematic difference between the national senior assessors of the system and EU Commission’s assessors.The EUROP system predicted lean meat percentage poorly (R2 = 0.407), with a prediction error for 3.027% lean. For fat and bone per-centage, the results showed a fairly good prediction of fat percentage, but poorer for bone percentage, R2 = 0.796 and R2 = 0.450, respec-tively. The prediction error for fat and bone percentage was 2.300% and 2.125%, respectively. Lean: bone ratio was predicted poorly(R2 = 0.212), with a prediction error of 0.363 lean: bone ratio.Ó 2006 Elsevier Ltd. All rights reserved.
Keywords: Lamb; Carcass; Classification; Subjective assessment; Commercial cutting
1. Introduction
Carcass classification of ruminants in Norway, as in theEuropean Union, is based on the EUROP carcass classifi-cation system (Commission Regulation (EEC) No 461/93,1993; Council Regulation (EEC) No 2137/92, 1992). Theoverall aim of the EUROP classification system is to sortcarcasses according to their value for further processingand to ensure fair payment to farmers. The EUROP classi-
fication system in Norway makes use of four carcass cate-gories or maturity groups for sheep; mutton, yearlingmutton, lamb and suckling lamb. For ruminants in Nor-way, EUROP classification is carried out by human assess-ment of conformation and fat class in addition to carcassweight. Conformation class describes carcass shape interms of convex or concave profiles and is intended to indi-cate the amount of flesh (meat) in relation to bone, whereflesh or meat is regarded as the sum of fat and lean (Fisher& Heal, 2001). Fat class describes the amount of visible fat(subcutaneous) on the outside of the carcass (Fisher &Heal, 2001). Carcasses are given classes from 1 to 15, wheregrade 1 is Pÿ for conformation class and 1ÿ for fat class.
0309-1740/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.meatsci.2006.04.017
* Corresponding author. Tel.: +47 2209 2246; fax: +47 2222 0016.E-mail address: jorgen.johansen@fagkjott.no (J. Johansen).
www.elsevier.com/locate/meatsci
Meat Science 74 (2006) 497–509
MEATSCIENCE
Grade 15 is E+ conformation class and 5+ for fat class.High value for conformation class indicates a carcass withwell to excellent rounded muscles. High value on fat classindicates a carcass with a high degree of external fat (sub-cutaneous), and utilizes the relationship between externalfat and total fat content of carcass.
In Norway, human assessors carry out EUROP classifi-cation of lamb carcasses (manually) by sensory evaluationof carcasses. Classification of ruminant carcasses is tradi-tionally done by trained assessors because of the difficultyof identifying appropriate instrumental methods. The Nor-wegian Meat Research Centre (NMRC) (national seniorassessors) has been given the responsibility by the Norwe-gian classification board (Røe, 2002) to train and certifyabattoir assessors, using EU Commission photographicstandards. Abattoir assessors are supervised after they havefinished their training and certification, and are validatedseveral times annually by the national senior assessors.Certification is withdrawn from abattoir assessors if theyfail supervision and validation tests. The approval limitsfor certification and validation of assessors for the EUROPclassification system are described by the EU Commissionregulation (EC) No. 1215/2003. National senior assessorsare also supervised and validated annually by the EU Com-mission assessors. The foundation of the EUROP carcassclassification system is a 5-class system, legislated by theEU Commission (Regulation (EEC) No 2137/92, No461/93, 1992/1993; Commission Regulation (EEC) No461/93, 1993; Council Regulation (EEC) No 2137/92,1992). In Norway, EUROP carcass classification of lambcarcasses is carried out using 15 classes (5 classes with +and ÿ for each class), both for conformation and fat.The rules laid down by the EU Commission states thatabsolute maximum deviation (bias) between EU Commis-sion and abattoir assessors should not be larger than 0.3and 0.6 for conformation and fat class, respectively (Com-mission Regulation (EC) No 1215/2003, 2003). The slopeof a linear regression line (fitted) between EU Commissionand abattoir assessors should not deviate more than ±0.15and 0.30 from 1 for conformation and fat class, respectively(Commission Regulation (EC) No 1215/2003, 2003).
Pig carcasses are not classified, but graded instrumen-tally by measuring backfat and muscle depth as a predictorof lean meat percentage. At the same level of overall bodyfat, pigs have 68% of the dissectible fat subcutaneous, whilesheep and dairy cattle have 43% and 24%, respectively(Warriss, 2000). Beef cattle have a somewhat higher pro-portion of subcutaneous fat than dairy cattle. The greaterproportion of subcutaneous fat in pig carcasses makesgrading using instruments measuring backfat more accu-rate for pigs than for sheep and cattle. There is however,a lot of interest (Allen, 2003; Allen & Finnerty, 2001; Berg,Neary, Forrest, Thomas, & Kauffman, 1997; Cunha et al.,2003; Du & Sun, 2004; Fisher, 1990; Garrett, Edwards,Savell, & Tatum, 1992; Hopkins, Anderson, Morgan, &Hall, 1995; Kempster, Chadwick, Cue, & Granley-Smith,1986; Kirton, Mercer, & Duganzich, 1992; Stanford, Jones,
& Price, 1998; Swatland, Ananthanarayanan, & Golden-borg, 1994), both industrially and scientifically, to lookfor instrumental methods for ruminant species, i.e. opticalprobes and video image analysis (VIA). It can be arguedthat the use of the EUROP assessment scheme involvingtraining of assessors and the use of photographic standardsas reference points result in an evaluation system which isobjective in nature. However, since instrumental methodsusually are calibrated against known references for a givenset of parameters, visual assessment may be less stable dueto differences between operators plus the season-based nat-ure of lamb slaughtering. This is a major concern, evenwhen assessors are well trained, supervised and calibratedagainst photographic standards.
The main objectives of this study were to:
1. Study and identify the accuracy of the national seniorassessors using the EUROP classification system photo-graphic standards for lamb.
2. Study the abattoir EUROP classification accuracy inNorway compared with EU Commission’s assessorsusing the EUROP classification system photographicstandards for lamb.
3. Compare national senior vs. abattoir assessors withrespect to EUROP classification, and study the accuracyof the EUROP classification system for prediction oflean meat, fat and bone percentage and lean meat inrelation to bone ratio.
The first two objectives will identify the accuracy ofvisual assessment before the EUROP system is testedagainst carcass composition end-points.
2. Materials and methods
2.1. Trials
Three separate trials were carried out (Table 1).The assessors that participated in the different trials
were allocated into three levels: (1) Abattoir assessors, (2)national senior assessors (NMRC) and (3) EU Commissionassessors (Fig. 1). The abattoir assessors were trained andapproved assessors available and working at the selectedplants during the time of the study. National senior asses-sors were a group of three highly skilled assessors workingat the Norwegian Meat Research Centre. The EU Commis-sion assessors were a group of four highly skilled interna-tional assessors from Great Britain, France, Iceland andNorway. The photographic standards of the EU Commis-sion were used as the main reference point for lamb carcassclassification in all trials. The first trial was carried out inautumn of 2000 to check the repeatability of the nationalsenior assessors. The second trial was carried out inautumn of 2004 to validate the abattoir classification levelin Norway. The third trial was carried out in autumn of1999 to check the accuracy of the EUROP classificationsystem carried out by the national senior assessors for pre-
498 J. Johansen et al. / Meat Science 74 (2006) 497–509
diction of lean meat, fat and bone percentage and leanmeat: bone ratio. The reason that the verification and val-idation of the different levels of assessors was done after thecutting trial (trial 3), was related to the fact that the actualresults brought up the issue of improved documentation ofthe actual global validity of the results.
2.2. EUROP classification routines
In all the experiments, categories of animal, conforma-tion and fat class were assessed according to the EUROPguidelines made effective in Norway in 1996 (Røe, 2002).
The flow of supervision and validation is shown in Fig. 1.A 5-group system was introduced to compare the accuracyof 15 groups versus 5 groups. Both the 5-group and15-group systems are shown in Table 2 for comparison.
2.3. Raw data
2.3.1. Trial 1: EUROP classification repeatability
(accuracy) of national senior assessors
Forty lamb carcasses were sampled from a single Nor-wegian abattoir in the southeast part of Norway (GildeHed-Opp Rudshøgda). The carcasses were selected froma population classified by abattoir assessors (Table 3).
Three national senior assessors classified the 40 car-casses three times (triple test). The carcasses were arrangedin random order for each repetition.
2.3.2. Trial 2: Accuracy of abattoir EUROP classification in
Norway compared to EU Commission supervising assessors
Five hundred lamb carcasses (trial 2) were sampled fromfive different abattoirs distributed according to EUROPconformation and fat class during a 5-day trial in theautumn of 2004 (Table 4).
The carcasses were distributed as evenly and as practi-cally possible across a 15*15 grid (Table 5) of EUROPconformation and fat classes following the allocation ofabattoir assessors. Four EU Commission assessors classi-fied the 500 carcasses during the 5-day trial. Three out ofthe four assessors were regarded as more experienced,and their assessment was weighed with 2 compared to thelast, less experienced assessor, which was weighed with 1.All the assessments were averaged for further analysisaccording to the weighing of assessor into a ‘‘Gold Mean’’.
2.3.3. Trial 3: Accuracy of abattoir EUROP classification in
Norway compared to national senior assessors. Accuracy of
the EUROP classification system for prediction of lean meat,
fat and bone percentage and lean meat: bone ratio
Three hundred ninety five lamb carcasses from six differ-ent abattoirs distributed geographically across Norwaywere sampled for cutting at the NMRC pilot plant duringautumn of 1999. The largest abattoir (Gilde Hed-Opp Rud-shøgda) provided 298 lamb carcasses. The experimental
Fig. 1. Order of EUROP assessors. EU Commission assessors superior to
NMRC national (senior) assessors, and NMRC national (senior) assessors
superior to abattoir assessors.
Table 2
15-Point scale and 5-point scale used for EUROP conformation and fat class
Conformation class-15 E+ E Eÿ U+ U Uÿ R+ R Rÿ O+ O Oÿ P+ P PÿScale-15 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
Fat class-15 5+ 5 5ÿ 4+ 4 4ÿ 3+ 3 3ÿ 2+ 2 2ÿ 1+ 1 1ÿ
Conformation class-5 E U R O P
Scale-5 5 4 3 2 1
Fat class-5 5 4 3 2 1
Table 1
Description of trials
Trial Number of
animals (n)
Time of trial Objective
1 40 Autumn 2000 EUROP classification
repeatability (accuracy) of
national senior assessors
2 500 Autumn 2004 Accuracy of abattoir EUROP
classification in Norway
compared to EU Commission
supervising assessors
3 396 Autumn 1999 Accuracy of abattoir EUROP
classification in Norway
compared to national senior
assessors. Accuracy of the
EUROP classification system for
prediction of lean meat, fat and
bone percentage and lean meat to
bone ratio
J. Johansen et al. / Meat Science 74 (2006) 497–509 499
design was set up so that a minimum of 10 carcasses wasselected for each conformation class and fat class. EUROPclassification by abattoir assessors was used for selectingsamples for the distribution given in Table 6. For confor-mation, class 13 to 15 and fat class 12 this was not feasible
in practice due to lack of available carcasses (Table 6). Sexwas recorded for 299 lambs of trial 3, where 185 were ramlambs, and 114 were ewe lambs. The carcasses were classi-fied both by abattoir and national senior assessors to com-pare EUROP assessments.
Intact carcasses were transported to the NMRC pilotplant. After cold weighing, lamb carcasses were cut intofive primal cuts (Fig. 2): main roast or leg + rump = longleg (1), mid-part (rack) was divided into loin (2) and side(3), shoulder (4) was removed at 5th rib to contain thescapula, humerus, ulna and radius, leaving the anterior ribsand cervical and anterior thoracic vertebrae as breast withneck (5) (Swatland, 2000).
The primal cuts were cut and separated into lean meat(LM), fat (F), connective tissue (C) (=not fat; not leanmeat and not bones) and bone (B). A trained team of sevenskilled butchers participated in the cutting of the lamb car-casses. The lean meat was allocated into two groups; high-and low-value meat. High-value meat (HQM) is regarded ahigher standard mostly because of higher tenderness and/or lower fatness and consist of boneless retail cuts suchas steaks and filets (Coopman, Van Zeveren, & De Smet,2004). Low-value meat (LQM) is of a lower standard dueto lower tenderness and/or higher fatness, and consists oflow-value boneless retail cuts (LQM1) and diced meat usedfor stewing or minced meat (LQM2). The nomenclature ofboth high- and low-value retail cuts and their respectiveanatomic names are shown in Table 7 (Calkins et al.,2006). The two groups of meat were sorted and weighedseparately. Carcass fat was separated and defined as subcu-taneous (SF) and intermuscular (IF) from the lambcarcasses.
2.4. Estimated data
The high-value retail cuts were estimated to be 100%lean (lean being a mixture of protein, water, fat and
Table 3
The number of carcasses in each conformation and fat class allocated to a
15*15 grid (trial 1)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Sum
1 3 3
2
3 4 4
4 1 3 1 1 6
5 2 1 1 4
6 2 2 2 1 1 8
7 2 2
8 1 3 2 1 2 1 10
9 1 1 2
10 1 1
11
12
13
14
15
Sum 3 1 7 6 1 7 5 2 5 2 1 40
Conformation classes (rows top to bottom) and fat classes (columns left to
right). EUROP classification assessed by abattoir assessors.
Table 4
Number of lamb carcasses and distribution between abattoirs (trial 2)
Date Abattoir Number of
carcasses
11th October 2004 Gilde Hed-Opp Rudshøgda 20
11th October 2004 Fatland Oslo/Helle Abattoir 50
12th October 2004 Gilde Hed-Opp Rudshøgda 130
13th October 2004 Gilde BS Oppdal 100
14th October 2004 Gilde NNS Mosjøen 100
15th October 2004 Gilde Vest Forus 100
Table 5
The number of carcasses in each conformation and fat class allocated to a 15*15 grid (trial 2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Sum
1 1 1 1 3
2 6 5 2 2 2 1 18
3 2 12 2 1 1 18
4 1 4 6 8 5 9 2 35
5 2 3 12 9 17 15 5 3 66
6 1 2 5 15 20 21 3 1 68
7 1 3 5 9 19 24 27 8 6 1 103
8 3 5 10 29 41 15 6 4 113
9 1 2 4 9 8 11 3 5 1 44
10 1 3 7 3 2 2 18
11 3 6 2 1 12
12 1 1 2
13
14
15
Sum 1 13 29 33 41 74 114 117 46 19 12 1 500
Conformation classes (rows top to bottom) and fat classes (columns left to right). EUROP classification assessed by abattoir assessors.
500 J. Johansen et al. / Meat Science 74 (2006) 497–509
ash). The lean percentage of the low-value retail cuts(lm1%) were estimated by chemical analysis (2006b),and the lean meat percentage of diced and minced meatproducts (lm2%) were estimated using AnylRay (ScanioA/S, 1997) at-line in the NMRC pilot plant. Total leanmeat content (LMC) was estimated using the weight ofall high-value retail cuts, and the lean percentage estima-tions of the low-value meat. Lean meat percentage(LM%) was estimated by dividing the total lean meat con-tent by the cold carcass weight, expressed as relative pro-portions (%):
LMC ¼ HQMþ lm1% � ðLQM1Þ þ lm2% � ðLQM2Þ;
LM% ¼ LMC=CCW � 100%:
Subcutaneous fat (SF) and manually separated intermuscu-lar fat (IF) was estimated to be 70% fat (30% mainly pro-teins and water). The fat percentage of LQM1 (f1%) wasestimated by the residue proportion from chemical analy-sis, and the fat percentage of LQM2 (f2%) by the residueproportion from AnylRay measurement. Total fat content(FC) was estimated using weight of SF + IF, and the fatestimations of LQM1 and LQM2. Fat percentage (F%)
Table 6
The number of carcasses in each conformation and fat class allocated to a 15*15 grid (trial 3)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Sum
1 19 5 19 24
2 5 16 7 3 5 31
3 9 12 8 29
4 2 23 14 4 1 44
5 6 14 9 3 6 5 43
6 1 1 12 11 10 9 6 3 2 1 56
7 1 4 5 2 8 3 1 2 1 27
8 3 5 5 6 3 1 23
9 1 2 4 4 1 3 15
10 4 3 8 9 9 4 2 2 41
11 1 1 5 3 9 9 3 1 32
12 1 2 1 3 2 1 10
13 3 1 1 2 1 3 8
14 2 2 2 2 2 1 2 11
15 1 1
Sum 29 37 56 72 49 44 59 29 9 7 4 29 395
Conformation classes (rows top to bottom) and fat classes (columns left to right). EUROP classification assessed by abattoir assessors.
Fig. 2. Lamb carcass primal cuts used in Norway.
Table 7
High-value and low-value meat; retail cuts
Name of retail cut Standard Major muscles
Lamb tenderloin High-value Psoas major, Psoas minor
Lamb loin High-value Longissimus, Complexus, Multifidus, Spinalis dorsi
Lamb inside round High-value Semimembranosus
Lamb leg roast (chump) High-value Semitendinosus, Biceps femoris, Rectus Femoris, Vastus lateralis, Vastus medialis,
Vastus intermedius
Lamb rib skin + flank for manufacturing Low-value Intercostal + abdominal muscles (flank)
Lamb shoulder clod roast Low-value Serratus ventralis, Subscapularis, Infraspinatus, Supraspinatus, Triceps brachii, Teres major
Lamb chuck Low-value Triceps brachii, Teres major, Longissimus
J. Johansen et al. / Meat Science 74 (2006) 497–509 501
was estimated by dividing the total fat content by the coldcarcass weight, expressed as relative proportions (%):
FC ¼ 0:7% � ðSFþ IFÞ þ f1% � ðLQM1Þ þ f2% � ðLQM2Þ;
F% ¼ FC=CCW � 100%:
All the soft tissues (LM, F, and C) were removed fromthe bones, and the total bone content (BC) was estimatedby weighing all the bones (B). Bone percentage (B%) wasestimated by dividing the total bone content by the coldcarcass weight, expressed as relative proportions (%):
BC ¼ B;
B% ¼ BC=CCW � 100%:
Lean meat to bone ratio (LM:B) was estimated by divid-ing the total lean content by the total bone content,expressed as a ratio between lean and bone:
LM : B ¼ LMC=BC:
2.5. Chemical analysis
Chemical analysis to estimate fat content in low-valueretail cuts was carried out by the Buchi-CaviezelÒ method(Norwegian Accreditation, 2006), NMKL23.
2.6. Statistical data analysis
Prediction of lamb carcass composition was carried outusing partial least square regression, PLS, modeling oneY-variable at the time (PLS1) (Martens & Martens, 2001),using the PLS_Toolbox for MATLAB (Wise et al., 2004).Cold carcass weight (CCW), EUROP conformation andfat class assessed by national senior assessors was chosenas predictors (X). Lean, fat and bone percent in additionto lean: bone ratio was chosen as dependent variables (Y).Validation of the prediction models was carried out usingfull leave-one-out cross validation, using root mean squareerror of cross validation (RMSECV) as diagnostic tool tofind a representative regression model. All the models werecentered to remove offset and reduce rank in the models.
3. Results
3.1. EUROP classification repeatability and accuracy of
national senior assessors
Table 8 shows the mean value and standard deviationsfor EUROP conformation and fat class for the national
senior assessors. There seemed to be some difference inassessment of conformation class between assessors. Asses-sor 3 tended to have a lower standard deviation than theother assessors. For fat class (Table 8), a larger differencebetween mean values was identified, both within andbetween assessors. However, there were no significant dif-ferences at 5% significance level.
From the correlation matrix (Table 9), assessor 3seemed to achieve a somewhat lower correlation coefficientfor conformation class than the other assessors did. For fatclass, the different national senior assessors were almostidentical with respect to variation in correlation betweenassessor and within tests (Table 10).
3.2. Accuracy of abattoir EUROP classification in Norway
compared to EU Commission supervising assessors
The agreement between the EU Commission assessorsshowed that 67.48% and 70.63% of the conformation andfat class assessments, respectively, were identical. The meanvalues and standard deviations in Table 11 show onlyminor differences and high conformity between EU Com-mission assessors.
Table 12 compared the mean values and standard devi-ations of abattoir and EU Commission assessors. The val-ues showed a difference in the classification level; theabattoir assessors seemed to over-classify (give a higherclass) the conformation class, and under-classify the fatclass (farmer-friendly classification). The standard devia-tions for conformation class between abattoir and EUCommission assessors are very similar. For fat class,
Table 8
Mean EUROP conformation and fat class (SD in parenthesis) of the three separate tests on 40 carcasses
Assessor n Conformation class Fat class
Test 1 Test 2 Test 3 Test 1 Test 2 Test 3
1 40 5.80 (2.64) 5.78 (2.56) 5.63 (2.55) 8.15 (3.08) 7.75 (2.94) 7.88 (3.01)
2 40 5.90 (2.71) 5.78 (2.61) 5.88 (2.71) 8.25 (3.07) 8.03 (3.20) 8.25 (3.26)
3 40 5.38 (2.54) 5.40 (2.57) 5.13 (2.41) 8.30 (3.14) 7.90 (3.19) 8.03 (3.09)
Three national senior assessors evaluated 40 lamb carcasses (n = 40; trial 1).
Table 9
Correlation coefficients (Pearson r) between three repeated tests (T1–T3)
of EUROP conformation class
Test n A1 A2 A3
T1 T2 T3 T1 T2 T3 T1 T2 T3
A1–T1 40 97.9 97.4 95.6 95.6 96.7 93.2 95.6 93.8
A1–T2 40 97.8 95.6 94.2 96.2 93.8 95.4 94.5
A1–T3 40 96.7 96.3 96.2 95.3 96.3 93.4
A2–T1 40 96.6 96.8 92.8 96.0 93.5
A2–T2 40 98.0 92.0 95.5 91.5
A2–T3 40 93.9 96.5 95.0
A3–T1 40 95.9 94.0
A3–T2 40 94.4
A3–T3 40
Three national senior assessors (A1–A3). 40 lamb carcasses (n = 40). Trial
1.
502 J. Johansen et al. / Meat Science 74 (2006) 497–509
standard deviations are smaller for abattoir than EU Com-mission assessors. Studying each abattoir with respect toconformation class assessment, one of them seemed toover-classify the carcasses significantly, one abattoir over-classified to some extent, two abattoirs where on target,while the last abattoir was under-classifying the carcasses.For fat class, only one abattoir was on target, three abatt-oirs were under-classifying and one abattoir was over-clas-sifying. Fig. 3 show the scatter plot between the averageabattoir and EU Commission assessments. The correla-tions (r) were 0.888 and 0.868 for conformation and fatclass, respectively. In this trial, the absolute deviationsbetween EU Commission and abattoir assessors were0.20 and 0.26 for conformation and fat class, respectively.
For the slope of the linear regression (fitted) line, the resultsshowed an offset of ÿ0.099 and ÿ0.184 from 1 for confor-mation and fat class, respectively.
3.3. Trial 3: Accuracy of abattoir EUROP classification in
Norway compared to national senior assessors. Accuracy of
the EUROP classification system for prediction of lean meat,
fat and bone percentage and lean meat: bone ratio
There were no significant differences between sexes forconformation class, carcass weight and lean meat percent-age. For fat class, fat and bone percentage there were clearsignificant differences between sexes. Ewe lambs were fatterthan ram lambs, and ram lambs had more bone than ewelambs. There seemed to be some difference in standarddeviations for conformation and fat class, where ram lambsshowed higher variability than ewe lambs for conformationclass, and ewe lambs showed higher variability than ramlambs for fat class. Correlation analysis was carried outwith or without sex as a factor. There was little or no dif-ference between the two alternatives, and it was decidednot to include sex in the following data analysis, in orderto achieve as many samples as possible from all abattoirs.
Fig. 4 shows the scatter plot between the national seniorand abattoir assessor with respect to EUROP classifica-tion. The correlations between national senior and abattoirassessors were 0.96 and 0.92 for conformation and fatclass, respectively. From the scatter plot, outliers, bothfor conformation and fat class were observed. These werecarcasses where abattoir assessors disagreed with thenational senior assessors, under-classifying both for con-formation and fat class. Otherwise, the abattoir andnational senior assessment seem to be synchronized bothfor conformation and fat class, with deviations spreadevenly across the scale. No significant bias or offset wasidentified.
Mean carcass weight (Table 13) for the samples washigher than for the whole population of 1999 (Røe,2000). Mean conformation class was also higher than thepopulation of 1999, but fat class was almost similar.
The correlation between carcass weight (CCW), confor-mation and fat class is shown in Table 14. The largest
Table 10
Correlation coefficients (Pearson r) between three repeated tests (T1–T3)
of EUROP fat class
Test n A1 A2 A3
T1 T2 T3 T1 T2 T3 T1 T2 T3
A1–T1 40 97.9 97.5 97.6 97.2 97.3 95.9 97.1 96.4
A1–T2 40 97.9 97.5 97.3 98.0 95.9 96.6 95.5
A1–T3 40 97.7 97.0 97.2 96.3 95.8 96.0
A2–T1 40 96.5 97.5 97.7 97.0 96.5
A2–T2 40 98.7 95.2 96.0 95.4
A2–T3 40 96.0 96.9 96.1
A3–T1 40 96.2 97.7
A3–T2 40 96.2
A3–T3 40
Three national senior assessors (A1–A3). 40 lamb carcasses (n = 40). Trial
1.
Table 11
Mean and standard deviation of EUROP conformation and fat class of
lamb carcasses
EU Commission
assessor
n Mean EUROP
conformation
class (SD)
Mean EUROP
fat class (SD)
1 500 6.68 (2.16) 6.73 (2.01)
2 500 6.66 (2.06) 6.75 (2.01)
3 500 6.68 (2.21) 6.64 (2.08)
4 500 6.69 (2.09) 6.69 (1.99)
EU Commission assessors. Trial 2.
Table 12
Mean and standard deviation of EUROP conformation and fat class of lamb carcasses
Date n EUROP conformation class EUROP fat class
Abattoir Gold mean Abattoir Gold mean
11th October 2004a 100 5.88 (1.45) 5.99 (1.66) 6.27 (1.46) 6.57 (1.56)
12th October 2004 100 7.34 (1.63) 7.32 (1.54) 7.44 (1.73) 7.49 (1.59)
13th October 2004 100 7.47 (1.72) 7.14 (2.00) 5.60 (1.72) 6.86 (1.68)
14th October 2004 100 8.44 (2.09) 7.42 (2.34) 7.46 (2.41) 7.05 (2.29)
15th October 2004 100 5.58 (2.14) 5.55 (2.13) 4.75 (1.84) 5.61 (2.22)
Total 500 6.88 (2.07) 6.68 (2.10) 6.31 (2.12) 6.57 (2.43)
Abattoir assessors vs. EU Commission assessors. Trial 2.a For 11th of October 30 carcasses from the Rudshøgda abattoir the 12th of October was included in addition to the 70 original carcasses form the 11th
of October.
J. Johansen et al. / Meat Science 74 (2006) 497–509 503
correlations were found between bone and lean: bone ratio(r = ÿ0.87), fat class and fat percentage (r = 0.86) and con-formation class and CCW (r = 0.84).
The heaviest carcasses both have high conformation andfat class. One exemption was a group of lean, highly con-formed (class 12 to 15) carcasses, yielding a somewhat neg-ative correlation between CCW and conformation classesfor high conformation carcasses (Table 15).
The results from prediction of lean meat percentage(Fig. 5) showed that 40.7% of the variation was explainedusing CCW and EUROP conformation and fat class aspredictors. The prediction error (RMSECV) was 3.027%lean. For 5 EUROP classes for conformation and fat
instead of 15 classes, RMSECV was 3.253. The followingprediction equation was extracted for lean percentage (15classes) from the regression analysis:
LM% ¼ 63:71ÿ 0:111 � CCW
þ 0:533 � EUROP Conformation Class
ÿ 1:158 � EUROP Fat Class:
EUROP fat class seemed to be the largest predictor,whereas high fat class yielded a lower lean percentage.
For fat percentage, Fig. 6 showed that 73.7% of thevariation was explained using the selected predictors. Theprediction error was 2.395% fat. Using 5 groups instead
Fig. 3. Scatter plot. Average assessment EU Commission assessors (y-axis) vs. average abattoir assessors (x-axis) (n = 500). Conformation and fat class.
Fig. 4. Scatter plot. Average assessment NMRC assessors (y-axis) vs. average abattoir assessors (x-axis) (n = 395). Conformation and fat class.
Table 13
Descriptive statistics, trial 3, n = 395
Parameter n Mean SD Min. Max.
CCW 395 19.20 5.55 7.22 33.50
Conformation class 395 6.51 3.50 1 15
Fat class 395 5.77 2.57 1 12
Lean percent 395 63.71 3.93 45.10 73.50
Fat percent 395 13.88 4.67 6.59 33.21
Bone percent 395 22.41 2.87 15.70 35.38
Lean: bone ratio 395 2.89 0.41 1.64 4.31
1. National senior assessors, EUROP classification.
2. Cold carcass weight, kg.
3. Percentage tissue of CCW (%).
Table 14
Correlation coefficients (Pearson) EUROP classification and cutting
variables
Confa Fata CCWb Leanc Fatc Bonec
Fata 0.63
CCWb 0.84 0.74
Leanc ÿ0.15 ÿ0.59 ÿ0.31
Fatc 0.51 0.86 0.65 ÿ0.79
Bonec ÿ0.63 ÿ0.60 ÿ0.63 ÿ0.08 ÿ0.54
Lean: bone 0.47 0.22 0.38 0.53 0.09 ÿ0.87
a National senior assessors, EUROP classification.b Cold carcass weight, kg.c Percentage tissue of CCW (%).
504 J. Johansen et al. / Meat Science 74 (2006) 497–509
of 15, the prediction error was larger (2.735). The followingprediction equation was extracted for fat percentage fromthe regression analysis:
F% ¼ 13:88þ 0:217 � CCW
ÿ 0:318 � EUROP Conformation Class
þ 1:472 � EUROP Fat Class:
EUROP fat class was the largest predictor, where highfat class yielded high fat percentage. Due to a non-lineartrend between predicted and measured values (Fig. 6), dif-ferent fat transformations of fat percent data were tested.The natural logarithmic transformations seemed toimprove predictions better than other types of transforma-tions (square root, logarithmic, polynomic).
By transforming data, the explained variance increasedfrom 73.7% to 79.6% of the variation in fat percentage
(Fig. 7) and the predicted vs. measured fit became more lin-ear. Transformation of data yielded a prediction error of2.30, which was smaller than the non-transformed data.The following prediction equation was extracted for ln(natural logarithm) fat percentage from the regressionanalysis:
ln F% ¼ 2:58þ 0:0163 � CCW
ÿ 0:0192 � EUROP Conformation Class
þ 0:0993 � EUROP Fat Class:
For bone percentage, Fig. 8 showed that 45.0% of thevariation was explained using the selected predictors. Pre-diction error was 2.125% bone. Fat class seemed to bethe largest predictor, whereas high fat class yielded lowbone percentage. High level of all the predictors yieldedlow level of bone percentage. Some non-linearity seemed
Table 15
Mean carcass weight (kg) per conformation and fat class (trial 3)
C F
1 2 3 4 5 6 7 8 9 10 11 12 Avg.
1 9.83 9.87 12.39 10.07
2 11.81 13.32 12.67 14.16 12.44
3 12.30 14.25 13.23 14.05 13.63
4 14.81 14.76 15.83 16.62 14.70 15.83
5 18.00 17.92 17.64 18.14 19.30 17.92 18.03
6 19.82 15.36 19.30 19.67 18.46 18.58 25.62 18.92 19.20
7 18.10 17.08 21.09 19.45 21.35 21.62 24.21 24.59 20.78
8 20.73 21.10 22.96 21.86 23.64 24.65 21.72 28.05 22.53
9 21.92 20.14 22.99 22.39 23.95 23.74 24.92 22.00 22.94
10 21.82 24.38 23.88 24.69 23.90 24.41 27.23 28.39 25.55 24.93
11 28.48 22.58 24.02 23.72 26.29 26.11 28.82 29.41 29.29 25.65
12 19.02 23.98 24.05 23.36 26.73 27.62 25.40 24.63
13 25.10 17.44 25.36 25.09 23.62
14 27.98 23.29 33.50 27.21
15 20.59 21.60 20.93
Avg. 9.83 12.63 15.62 15.44 18.15 20.17 21.63 23.18 24.62 24.78 25.28 28.67 19.20
Conformation class (C) (rows) and fat class (F) (columns).
45 50 55 60 65 70 7556
58
60
62
64
66
68
70
72
74
Y Measured 1
Y C
V P
redic
ted 1
Lean percentage
CCW CONF CLASS FAT CLASS-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Variable
Re
gr.
Co
eff.
Lean percentage
Fig. 5. Regression plot predicted vs. measured (left). Regression coefficients (right) for carcass weight (CCW), EUROP conformation and fat class. Lean
percentage.
J. Johansen et al. / Meat Science 74 (2006) 497–509 505
to be present between predicted and measured values, butdifferent transformations did not improve predictions.The following prediction equation was extracted for bonepercentage from the regression analysis:
B% ¼ 22:41ÿ 0:075 � CCW
ÿ 0:257 � EUROP Conformation Class
ÿ 0:333 � EUROP Fat Class:
For lean: bone ratio, Fig. 9 showed that 21.2% of thevariation was explained using the selected predictors. Pre-diction error was 0.363 lean: bone ratio. Conformationclass seemed to be the largest predictor, whereas high con-formation class yielded a higher lean: bone ratio. Differenttransformations of data did not improve predictions. Thefollowing prediction equation was extracted for lean: boneratio from the regression analysis:
L : B ¼ 2:89þ 0:161 � CCW
þ 0:445 � EUROP Conformation Class
ÿ 0:185 � EUROP Fat Class:
4. Discussion
The overall aim of the EUROP classification system isto sort carcasses according to their value presently definedby the variables CCW, fat, lean and lean: bone ratio, andthereby ensure fair payment to farmers. In addition, highrepeatability and reproducibility between classifiers ensurefair payment to farmers. The classifiers are expected to dothe same job, independent of geographical location andtime. The EU Commission assessors concluded that devia-tions were present between and within Norwegian abatt-oirs, but the deviations were within EU Commissionlimits for validation and approval of EUROP classificationcertification, especially for fat class in spite of the observedsystematic bias. The EU Commission limits for fat classifi-cation are less restricted than for conformation class. Thisis due to prognosis indicating that fat class is more difficultto assess than conformation class. Nevertheless, the valida-tion of abattoir classification against EU level in Norwayshowed that the average Norwegian abattoir assessor weresomewhat farmer-friendly, over-classifying conformation
5 10 15 20 25 30 355
10
15
20
25
Y Measured 1
Y C
V P
redic
ted 1
Fat percentage
CCW CONF CLASS FAT CLASS-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Variable
Re
gr.
Co
eff.
Fat percentage
Fig. 6. Regression plot predicted vs. measured (left). Regression coefficients (right) for carcass weight (CCW), EUROP conformation and fat class. Fat
percentage.
1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.42
2.5
3
3.5
Y Measured 1
Y C
V P
red
icte
d 1
Ln Fat percentage
CCW CONF CLASS FAT CLASS-0.02
0
0.02
0.04
0.06
0.08
0.1
Variable
Re
gr.
Co
eff.
ln Fat percentage
Fig. 7. Regression plot predicted vs. measured (left). Regression coefficients (right) for carcass weight (CCW), EUROP conformation and fat class.
Natural logarithmic transformation (ln) of fat percentage.
506 J. Johansen et al. / Meat Science 74 (2006) 497–509
class and under-classifying fat class. For fat class, the aver-age abattoir assessor seemed to squeeze their classificationassessment towards the mean value (lower standard devia-tion than EU Commission assessors). This could be due tothe fear of assessing extreme values caused by lack of train-ing, infrequent number of extreme carcasses during workhours or biased training by the national senior assessors.There was also some variation between abattoirs, wheresome abattoirs performed poorer than others. This demon-strated the different level and lack of accuracy betweenabattoirs with respect to carcass classification despite thefact that Norway has a good certification system in accor-dance with the EU Commission. There was a good correla-tion within national senior assessors, and the correlationwith respect to abattoir assessment was also good. The sys-tematic bias and offset between abattoir and EU Commis-sion may also be valid for the relationship between EUCommission and national senior assessors, since nationalsenior assessors are responsible for training and supervis-
ing abattoir assessors. This may be interpreted that abat-toir assessors have adapted the bias and offset from theirtraining and supervision from the national senior assessors.
The main predictor of lean meat percent seems to be fatclass; high fat class yields low lean percentage. This is dueto the negative correlation between fat and lean meat per-centage. EUROP predicted fat percent well, with fat classas the main predictor. Transformation with natural loga-rithm seemed to improve predictions, mainly due to theassessors’ uncertainty for high fat classes. Bone percentagewas poorly predicted by EUROP, but the main trend wasthe negative correlation between bone percentage andEUROP + CCW variables; increasing weight, higher con-formation and fatness yields a lower bone percentage.Fat class was the most important predictor, which maybe due to the large difference in growth rate of fat andbone, as a function of carcass weight. Lean: bone ratio isa derivative of muscle: bone ratio, which describes the rela-tionship between lean and bone percentage. Lean: bone,
15 20 25 30 35 4018
19
20
21
22
23
24
25
26
27
Y Measured 1
Y C
V P
red
icte
d 1
Bone percentage
CCW Conf class Fat class-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
Variable
Re
gr.
coe
ff.
Bone percentage
Fig. 8. Regression plot predicted vs. measured (left). Regression coefficients (right) for carcass weight (CCW), EUROP conformation and fat class. Bone
percentage.
1.5 2 2.5 3 3.5 4 4.52.5
2.6
2.7
2.8
2.9
3
3.1
3.2
3.3
3.4
3.5
Y Measured 1
Y C
V P
red
icte
d 1
Lean:bone
CCW Conf class Fat class-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
Variable
Re
gr.
co
eff.
Lean:bone
Fig. 9. Regression plot predicted vs. measured (left). Regression coefficients (right) for carcass weight (CCW), EUROP conformation and fat class. Lean:
bone ratio.
J. Johansen et al. / Meat Science 74 (2006) 497–509 507
however, describes the leanness, not muscle, of the carcassrelated to bone. The major problem with muscle: boneratio is that it does not take into consideration the fat con-tent of the carcass. All carcasses contain fat in varying pro-portions primarily dependent upon degree of maturity ofthe animal from which the carcass came. And, in mostcases, carcasses with high muscle: bone ratios also havehigh fat: bone ratios so that they may have lower percent-age of carcass muscle than carcasses with low muscle: boneratios (Thonney, 2006). A high level of lean: bone ratio issomewhat correlated with conformation, and may serveas a measurement of muscularity of the carcass adjustingfor the level fatness. A high lean: bone ratio generally yieldsa high-muscled lean carcass. Compared to previous studies(Warriss, 2000), the lamb carcasses in this trial were rela-tively lean (13.88% fat). The estimated lean percentagewas similar to Warriss (2000) but bone percentage was sig-nificantly larger. The difference in fat and bone percentagesbetween this trial and other studies may be because Norwe-gian lamb carcasses are lower in carcass weight than thoseof Warriss (2000) yielding carcasses with higher percentageof bone and lower percentage of fat. There may also besome influence from cutting error, where the butchers lefttoo much tissue on the bones, but it is not expected thatthis error should be any larger than in any other study sim-ilar to this trial.
5. Conclusion
For abattoir assessors, there were some systematic dif-ferences compared to EU Commission’s assessors carryingout EUROP classification, but these differences were withinEU Commission limits. In average, the abattoirs seemed toover-classify conformation class and under-classify fatclass (farmer-friendly classification). The relationshipbetween abattoir and the national senior assessors wasgood with no systematic bias or offset. This suggests thatthe same bias and offset is to be found between nationalsenior and EU Commission assessors as for abattoir vs.EU Commission’s assessors. For prediction of the carcasscomponent parameters, lean meat and bone percentagewas not very accurate by the EUROP system. Predictionof fat percentage was fairly accurate, and yielded the mostaccurate prediction of the carcass component parameters.Lean: bone ratio was predicted poorly, and yielded thepoorest predictions of all parameters.
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1
The reference butcher panel’s precision and reliability of dissection for
calibration of lamb carcass classification in Norway
J. Kongsroa,b*, B. Egelandsdalb, K. Kvaalc, M. Røea, A.H. Aastveitb
a Animalia – Norwegian Meat Research Centre, P.O. Box 396 Økern, N-0513 Oslo, Norway
b Dept. of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
c Dept of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
Abstract
Dissection of lamb carcasses is used as a reference method for maintenance and development
of lamb carcass classification and grading. The accuracy in terms of precision and reliability
of lamb carcass dissection as a reference method has never been quantified. The reference
method in Norway is performed by a skilled butcher panel at Animalia – Norwegian Meat
Research Centre. Lamb carcass dissection was studied with respect to precision and reliability
of repeated measurements using splitting of lamb carcasses (left-right). The carcass dissection
traits yield and carcass composition were estimated for both sides, and the precision and
reliability was quantified for the butcher panel. The overall precision and reliability was
acceptable for carcass composition traits, however the results for sub-primal yield and
composition was somewhat poorer. The precision (CV %) of the carcass tissues fat, muscle
and bone were 4.34, 2.27 and 3.19 for tissue weights, and 4.11, 1.19 and 3.00 for tissue
proportions, respectively. The reliabilities of the carcass tissues were 0.98, 0.96 and 0.85 for
tissue weights, and 0.93, 0.80 and 0.90 for tissue proportions, respectively. The muscle tissue
was most precise, while the fat tissue was most reliable. With respect to sub-primal
dissection, the lamb breast seem to be difficult for the butchers to dissect, and needs special
attention when setting up a dissection of lamb carcasses.
Keywords: Dissection, lamb carcass classification, precision, reliability
* Corresponding author. Phone: +4722092246; Fax: +4722220016.
E-mail address: jorgen.kongsro@animalia.no (Jørgen Kongsro)
2
Introduction
The main purpose of dissection of lamb carcasses in Norway is to evaluate carcass
composition and slaughter maturity, and to act as a reference method for classification and
grading methods for lamb (i.e. visual appraisal, visible light probing, Video Image Analysis
(VIA) and ultrasound) (Røe, 1998). Although international reference methods had been
developed for pork and beef carcasses in the late seventies and early eighties, no such method
had been agreed for sheep carcasses until (Fisher and de Boer, 1994) presented their EAAP
standard method of sheep carcass assessment in the nineties. However, like the other
reference methods for pork and beef, the EAAP standard method was not quantified with
respect to precision and reliability. The EUROP classification system for lamb carcasses is a
classification scale laid down by Council Regulation (EEC) No 2137/92, where the
classification must be made on the basis of conformation and the degree of fat cover and the
combination of these two criteria enables carcasses of ovine animals to be divided into classes
(Council Regulation (EEC) No 2137/92, 1992). The regulation provided for a Community-
wide carcass classification standard with the object of improving market transparency in the
sheepmeat sector. The details for the classification system were laid down in Commission
Regulation (EEC) No 461/93, where the market price for ovine carcasses are to be established
on the basis of the EUROP scale, and conformation and fat classes are described in the Annex
(Commission Regulation (EEC) No 461/93, 1993). After the implementation of EUROP in
Norway in 1996, dissection is still used as an objective measure for carcass quality in addition
to EUROP conformation and fat class, and is regularly used as a calibration method to check
for relationship between EUROP classes and carcass composition (muscularity, lean meat
yield and fat content). In addition, dissection is important for breeding traits, which rely solely
on the dissection as an objective measure for selection. The lamb carcasses are dissected by a
reference butcher panel at the pilot plant at Animalia – Norwegian Meat Research Centre
(Johansen et al., 2006). The butcher panel’s dissection of lamb carcasses in Norway involves
the dissection of 5 primal cuts (leg, loin, side, breast and shoulder) from a lamb carcass.
For pig carcass classification, the estimated accuracy of the EU reference dissection
method was presented quite recently (Nissen et al., 2006). The authors found variations
between butchers from different EU countries with respect to lean meat percentage and
jointing of carcasses. The maximum difference of Lean Meat Percentage (LMP) between two
butchers was found to be 1.9 %. The authors also found that even if the EU reference
dissection method for pig carcasses was well described by (Walstra and Merkus, 1996), some
deviations was observed during their experiment, especially the lack of description of
3
anatomical lines in the forepart or shoulder. For dissection of lamb carcasses, the jointing is
somewhat different from pork dissection. The forepart of lamb is jointed into two primal cuts;
shoulder and breast. The distribution of fat in lamb carcasses is also somewhat different
compared to pig carcasses. Some deviations and lack of description was also found in the
EAAP standard method for sheep carcass assessment described by Fisher and de Boer (1994),
especially for neck and thorax (breast) jointing and dissection. A more detailed description
and methodical framework is needed for the dissection of lamb carcasses.
As a reference for carcass classification and grading, it is important that the precision and
reliability of dissection are as high as possible. Dissection or determination of carcass
composition is the basis for development and maintenance of carcass grading and
classification, and the accuracy of the reference dissection method for lamb and sheep needs
to be described and quantified.
The objective of this study was to describe and quantify the precision and reliability of
lamb carcass dissection as a reference method for lamb carcass classification in Norway
performed the Animalia reference butcher panel. In addition, a methodical framework was put
forward for future studies and regular inspection of the accuracy of lamb carcass dissection in
Norway.
4
Materials & methods
Slaughter and experimental design
Sixty (60) half carcasses (30 whole carcasses) were selected from cold storage (-18oC)
according to EUROP fat class to ensure as much variation in fatness as possible. The lambs
were slaughtered during October 2006 at one large abattoir in central Norway (Rudshøgda),
and were frozen and stored after conditioning for a maximum of 5 days post-slaughter (4oC).
The carcasses were thawed one week prior to cutting, in batches of 10 carcasses per week, in
a refrigerated room (0-4oC) during January 2007. The weight-loss from thawing was closely
monitored, and the mean weight loss from warm carcass weight to thawed carcass was 3.95
%. The carcass weight used to obtain and estimate dissection traits in this study, is the cold
carcass weight before dissection, after thawing. The 30 carcasses were distributed evenly
among five skilled reference butchers at the Animalia (Norwegian Meat Research Centre)
pilot plant during January 2007, according to an experimental design using EUROP fat class
(Tab. 1).
Table 1. Experimental design; butcher panel dissection of lambs. Cold carcass weight (CCW), EUROP conformation and fat class given butchers A-E. Number (n) of whole (1/1) carcasses and mean values (standard deviations in parenthesis). Butcher -> A B C D E Total n 6 6 6 6 6 30 CCW (kg) 17.10
(2.62) 16.88 (1.94)
17.13 (2.27)
15.92 (2.77)
16.40 (1.05)
16.69 (2.21)
Conformation class
5.50 (1.31)
5.83 (1.75)
6.17 (1.40)
5.33 (0.98)
5.17 (0.72)
4.80 (1.92)
Fat class 5.50 (2.15)
4.67 (1.97)
5.33 (2.15)
4.50 (1.88)
4.00 (1.04)
5.60 (1.30)
The reference butcher panel
The pilot plant at Animalia acts as a reference dissection panel for carcass classification and
grading in Norway. The reference butcher panel consists of 5 butchers, who are employed at
the Animalia pilot plant. The butchers are skilled professionals who perform dissection
regularly for industry and scientific use. The total numbers of carcasses dissected annually are
approximately 2800 pig, 70 beef and 200 lamb carcasses.
Dissection and cutting traits
The Norwegian dissection of lambs is based on guidelines supervised by Gunnar Malmfors,
SLU, Sweden, exemplified in a Swedish Master Thesis (Einarsdottir, 1998), and the
guidelines presented in the EAAP standard method by Fisher and de Boer (1994). The
5
carcasses were split into 2 halves along the spinal column and the halves were jointed into 3
primal cuts; forepart, midpart and backpart (legs). The mid-part (lumbar region) of the carcass
was divided into sub-primals loin (rack) and side (flank) (Fig. 1). The sub-primal shoulder
(proximal thoracic limb) was removed at the 5th rib to contain the large anterior (forepart)
bones (scapula, humerus, ulna and radius), leaving the anterior ribs and cervical and anterior
thoracic vertebrae as sub-primal breast with neck (Swatland, 2000) (Fig. 1). The backpart
(proximal pelvis limb) was cut into lamb legs, long style with sirloin. For all primals and sub-
primals, the first operation in the dissection process was the removal of subcutaneous fat.
Muscles were then removed from bones, either singly or in groups. Finally, intermuscular fat
was trimmed from large muscles (steaks and filets), and bones and items such as tendons,
lymph nodes etc. are separated from the major tissues (Fisher and de Boer, 1994). The smaller
muscles or trimmings containing some fat were classified as manufacturing meat. Jointing
and dissection of both halves from each carcass was performed by the same butcher (A – E),
and 6 (12 halves) carcasses were dissected by each butcher (Tab. 1). In average, one butcher
dissected two carcasses (four halves) per day. Detailed figures of dissection of sub-primals are
shown in Figure 2.
6
1
2
3
4
5
Figure 1. Norwegian lamb cuts. Sub-primals shoulder (proximal thoracic limb) (1), breast (neck and thorax) (2), side (lumbar, ventral side) (3), loin (lumbar, dorsal side) (4) and leg (proximal pelvic limb) (5). Surrounding pictures: Different retail products derived from lamb carcass sub-primal cuts.
7
Breast
Loin
Leg
Shoulder
Side Figure 2. Dissection of a lamb carcass; sub-primal cuts. The five sub-primal cuts from left to right: breast, loin (top), shoulder and leg (middle) and side (bottom). The sub-primals are dissected into steaks and filets (large muscles), manufacturing meat, fat, bone and waste (tendons, lymph nodes etc.).
The fat content of manufacturing meats was estimated using AnylRay (Scanio A/S, 1997)
at-line in the NMRC pilot plant. Estimates of soft tissues were obtained by calculating the fat-
trimmed (lean) muscles from filets and steaks, fat content in manufacturing meats and the
separable fat (subcutaneous and intermuscular fat) from dissection. The carcass composition
8
data was reported as weights (kg or g) or as proportions (%), either as sums of the ½ carcass,
or as sub-primal yield or composition.
Statistical analysis
All data analysis were performed using MATLAB Version 7.4.0.287 (R2007a), January 29,
2007, Copyright 1984-2007, The MathWorks, Inc (The MathWorks, 2007). The difference in
dissection traits were calculated using absolute difference between the two sides (left-right).
The precision of cutting was measured by using the relative standard deviation (RSD) of the
difference between the two carcass halves (Breidenstein et al., 1964), using the ratio of the
standard deviation of the difference between the two sides (left-right) and the average carcass
side (left-right), sub-primal or tissue weight / proportion. The RSD was expressed as a fraction,
but more usually as a percentage and was then called coefficient of variation (CV) (van
Reeuwijk and Houba, 1998) (1):
%100..
xMean
differenceofdsCV = (1)
The reliability (REL) of dissection was defined as the correlation (r) between the repeated
measurements (the two carcass sides) (2):
222
22),cov(
σσσ
σσ
σσ ++
+==
CB
CB
rl
rl XXREL (2)
where l is left, and r right side.
The effect carcass side (left-right) on carcass and dissection traits, and carcass weight on
differences in carcass traits were analyzed using one-way ANOVA in MATLAB (ANOVA1).
9
Results
Table 2. Dissection results for cutting traits; Yield, fat, muscle and bone of carcass, primals and sub-primals; in weight and proportions (%). Mean, standard deviation (s.d.) and coefficient of variation (CV); n=30 carcasses (60 ½ carcasses).
Mean
(kg)
s.d. Diff
(g)
CV
(%)
REL Mean
(%)
s.d. Diff
(%)
CV
(%)
REL Dissection
traits
½ carcass, primal and sub-primal weights Primal and sub-primal proportions ½ Carcass 8.02 1.12 153.3 1.56 0.98 Primals Leg (backpart) 2.71 0.31 52.3 1.80 0.97 33.90 1.56 0.86 2.16 0.74 Mid 2.22 0.41 101.0 3.63 0.95 27.60 1.98 1.11 3.09 0.76 Forepart 3.09 0.48 140.3 4.14 0.92 38.47 2.21 1.43 2.89 0.67 Sub-primals Shoulder 1.46 0.23 48.3 3.66 0.95 18.16 1.18 0.56 3.75 0.72 Breast 1.54 0.26 130.0 6.73 0.79 19.21 1.76 1.47 5.99 0.45 Side 1.08 0.21 74.3 5.13 0.90 13.40 1.30 0.90 4.37 0.66 Loin 1.09 0.19 56.3 5.52 0.92 13.54 0.94 0.94 4.49 0.62 Leg 2.66 0.30 46.0 1.73 0.98 33.36 1.58 0.86 1.98 0.77 Fat weights Fat proportions ½ Carcass 0.96 0.28 47.8 4.34 0.98 11.72 2.13 0.57 4.11 0.93 Sub-primals Shoulder 0.11 0.03 6.3 6.71 0.94 7.35 0.91 0.36 5.54 0.84 Breast 0.21 0.11 58.7 23.89 0.76 12.91 5.12 2.94 20.83 0.72 Side 0.15 0.04 14.9 12.25 0.82 13.43 1.51 0.82 10.53 0.44 Loin 0.13 0.06 17.0 9.43 0.95 11.11 4.26 1.51 9.60 0.91 Leg 0.09 0.03 13.9 18.63 0.79 3.36 0.80 0.49 17.13 0.62 Muscle weights Muscle proportions ½ Carcass 4.97 0.67 39.1 2.27 0.96 62.06 1.68 0.83 1.19 0.80 Sub-primals Shoulder 1.00 0.18 42.4 4.86 0.94 68.20 2.26 1.18 1.32 0.80 Breast 0.82 0.12 44.2 6.34 0.87 53.54 4.27 4.04 5.76 0.30 Side 0.72 0.14 44.9 4.67 0.92 66.61 2.61 1.89 1.71 0.65 Loin 0.61 0.10 21.4 3.20 0.96 55.95 3.68 3.15 5.01 0.38 Leg 1.95 0.23 34.5 1.68 0.98 73.20 1.43 0.83 0.86 0.76 Bone weights Bone proportions ½ Carcass 1.92 0.23 92.8 4.48 0.85 24.13 2.40 0.90 3.00 0.90 Sub-primals Shoulder 0.34 0.04 14.6 3.19 0.89 23.90 2.66 1.08 3.70 0.87 Breast 0.47 0.08 70.0 11.72 0.44 30.69 3.91 2.72 7.13 0.63 Side 0.19 0.04 30.4 10.33 0.68 18.06 3.24 1.99 8.65 0.72 Loin 0.32 0.06 57.3 16.94 0.24 29.44 4.43 3.93 11.70 0.34 Leg 0.60 0.06 27.5 3.31 0.85 22.45 1.84 0.78 2.85 0.85
Thirty lamb carcasses were split into two halves (left-right), giving a total of sixty carcass
sides subject for dissection. There were no significant differences between carcass weight,
EUROP conformation and fat class between butchers, which indicates that the selection of
data was in accordance with the experimental design (Tab. 1) distributing carcasses evenly
among the 5 reference butchers in the butcher panel.
The left and right carcass sides were nearly equal in weights, with an average difference of
153 g. The standard deviation of the weight difference (coefficient of variation, CV %) was
about 1.56 % of the average side weight (left-right) (Tab. 2). No effect of carcass side (left-
10
right) was found for any of the carcass dissection traits. There were found significant effects
of carcass weight, for sub-primal side weight, muscle weight in sub-primals side and leg,
bone weight in sub-primal leg, and fat percentage in sub-primal shoulder. The difference in
the dissection traits between sides (left-right) increased with increasing carcass weight.
For primal or sub-primal yield in kg, precision (CV, %) varied from 1.73 to 6.73 %. The
poorest precision (6.73 %) for yield in kg was found for breast and the highest (1.73 %) for
leg. With respect to yield as proportions (%), the precision spanned from 1.98 to 5.99 %, for
sub-primal leg and breast proportions, respectively. In general, the reliability was high for
primal and sub-primal weights (r = 0.90 – 0.98), except for breast, which had a somewhat fair
reliability (r = 0.79). For primal and sub-primal yield as proportions, the reliability was fair (r
= 0.62 – 0.77), except for breast which was somewhat unreliable (r = 0.45).
For fat, the mean differences between the two carcass sides were 47.8 g and 0.57 % for fat
weight and proportion, respectively. The largest difference (130 g) was found for breast fat.
The precision of breast fat in kg was somewhat poor (CV = 23.89 %). Carcass fat was the
most precise (CV = 4.34 %). With respect to reliability, the dissection was fairly reliable for
breast, side and leg fat (r = 0.76 – 0.82), and high for carcass, shoulder and loin fat (r = 0.94 –
0.98). The precision for fat as proportions (%) spanned from 4.11 to 20.83 %, for carcass and
breast fat, respectively. The reliability varied from 0.44 to 0.93 for side and carcass fat,
respectively. For muscle, the mean differences between the carcass sides were 39.1 g and 0.83
% for muscle weight and proportion, respectively. The largest sub-primal differences were
found for shoulder, breast and side (nearly similar) for muscle weight, and for breast with
respect to muscle proportion (%). The overall precision for muscle dissection in kg was
somewhat high, spanning from 1.68 to 6.34 %, for leg and breast muscle, respectively. The
reliability was also somewhat high, 0.87 for breast muscle to 0.98 for leg muscle. For muscle
as proportions (%), the precision was somewhat high; varying from carcass muscle (%) CV =
1.19 % to breast muscle CV = 5.76 %. The reliability was low for breast muscle, and the
reliability of muscle proportions varied from 0.30 for breast muscle to 0.80 for carcass and
shoulder muscle. For bone, the mean differences between the carcass sides were 92.8 g and
0.90 % for bone weight and proportion, respectively. The largest bone difference was found
for loin with respect to bone weight and proportion (%). The precision (CV) of bone
dissection weights varied from 3.19 % for shoulder bone to 16.94 % for loin bone. The
reliability varied from 0.24 to 0.89 for loin and shoulder bone, respectively. For bone as
proportions, precision varied from 3.00 % for carcass bone and 11.70 % for loin bone. The
11
reliability was somewhat fair for bone, except for loin, which seemed to be somewhat
unreliable (r = 0.34).
Fat and bone traits seem to have an overall poorer precision compared to the other cutting
traits (muscle and yield). Weights in kg were more reliable than proportions (%), but the level
of precision seemed to be somewhat similar. Breast seemed to have a lower overall precision
and reliability compared to other sub-primals.
12
Discussion
Carcass side (left-right) had no significant effect on any of the dissection traits. It seemed that
the differences in dissection traits between carcass sides were due to butcher error or carcass
weight, and not due to asymmetric splitting of the lamb carcasses. The effect of carcass
weight on the carcass side (left-right) difference may be due to increased fatness with
increasing carcass weight (Kirton et al., 1999). It appeared more difficult to dissect and
separate tissues in high-fat carcassses and sub-primals compared to low-fat. The anatomical
lines may be easier to identify in low-fat carcasses compared to high-fat.. All significant
differences found in some of the dissection traits (Table 2) between carcass sides (left-right)
seemed to increase with increasing carcass weight. The difference in leg bone may be due to
increased residue fat and muscle left on bones due to increased size and fatness, especially for
the Ischium and Pubis. The effect of carcass weight may also be influenced by the butchers,
due to buthcer A tended to dissect carcasses with somewhat higher carcass weight and fat
class (however, not significant different) than butcher E (Tab. 1). There may therefore have
been some confounding between carcass weight and butcher, however, this may not be a
major concern, since no significant differences were found between butchers for carcass
weight, conformation and fat class.
The precision and reliability of the lamb carcass reference dissection were given as CV (%)
values and correlations (r) between the carcass sides (left-right). Precision and reliability is
critical during splitting and primal jointing, since the errors will aggregate during processing
or finer dissection of sub-primal cuts. The results showed that the jointing of primals and sub-
primals was very precise and accurate, except for the sub-primal breast. The source of error
may be the trimming of fat from the sub-primal, which may not be clearly defined in the
dissection specification (Fisher and de Boer, 1994). The weight difference of 130 g was
almost twice as high as the second largest weight (sub-primal, side) difference between sub-
primal sides (left-right) and some of this weight difference may be due to inaccurate trimming
of fat. Another source of error may be jointing of breast and shoulder by band saw, which
may be inaccurate; however no significant effects were found for sub-primals breast or
shoulder yields between the carcass sides (left-right). The anatomical lines in the breast are
hard to identify, which makes the operation even more difficult.
The overall precision and reliability of carcass dissection traits (fat, muscle and bone) were
acceptable, according to Nissen et al. (2006), who stated that as rule of thumb reliability
above 0.8 is considered acceptable accuracy for pig dissection. The reliabilities for all carcass
dissection traits were above 0.8 in this study, ranging from 0.80 to 0.98 for muscle proportion
13
(%) and fat weight (kg), respectively. It seemed like the lamb carcass dissection method
presented was suitable as a reference method for carcass traits, especially carcass tissue (fat,
muscle and bone) weights (kg) and carcass fat proportion (%). Overall, the reliabilities were
somewhat higher for primal, sub-primal and carcass tissue weights than proportions, which to
some extent agreed with previous studies done on suckling lambs (Diaz et al., 2004), where
prediction equations for tissue composition in grams were found to be more accurate (R2 >
0.91) than those for tissue proportion. The overall precision in this study seemed to be
somewhat similar or slightly better for carcass and sub-primal tissue proportions compared to
weights. In this study, the butchers tended to be more precise in allocating tissues as
proportions; however, the weights of tissues were more reliable. This may be due to the size
of the standard deviations of carcass and sub-primal tissue weight differences (left-right) in
relation to carcass and sub-primal tissue weights, compared to proportions (Tab. 2).
Cutting inaccuracies between sub-primals (left-right) used for dissection had no direct
influence on the estimated carcass traits, whereas it had an influence on the estimation of
tissues in the sub-primals themselves. Thus, the variation in precision and reliability of sub-
primals weights or proportions did not seem to have a large influence on the carcass tissue
weights or proportions. Concerning the proportions of primals and sub-primals, the results for
reliability showed that there were inaccuracies in jointing of carcasses, especially for breast.
This indicated that jointing of carcasses or cutting of sub-primals was sometimes more
difficult for the butchers than the actual dissection procedure (separation of lean muscle, fat
and bone) itself, as shown for EU dissection of pig carcasses by Nissen et al. (2006). Further
attention must be made, especially for jointing and cutting of forepart to get more reliable
estimates of carcass sub-primals for carcass classification. Breidenstein et al. (1964) stated
that splitting errors usually would affect most the weight difference between the left and right
wholesale loins because of unequal division of vertebra column. Even though no significant
difference between carcass sides (left-right) were found for loin muscle weight or proportion
in this lamb carcass study, the poor reliability for loin muscle proportion (r = 0.38) may be
due to splitting error, however, it seemed most likely that the reliability is due to inaccurate
separation of muscle, fat and bone by butchers. The tenderloin (m. psoas major), m.
longissimus and manufacturing meat were not cut or trimmed accurately enough, and may
reflect the poor reliability.
The training of butchers, both on a national and international level, is very important, both
to maintain the skillls and to open up for new, innovative thoughts regarding dissection
patterns and cuts. The work presented involved butchers from the Norwegian reference panel
14
of butcher, thus within a country. Butchers within a country will be trained together and
should be more uniform in their work compared to butchers from different countries as Nissen
et al. (2006) compared for pig carcass dissection. The methodical framework presented in this
work can be used for future studies of precision and reliability and as a supplement for
standard dissection methods for lamb carcasses. It can also be used as a tool for the meat
industry, i.e. as a quantitative tool for supervision, training or payment systems for industry
butchers.
Since carcass dissection is both laborious and expensive, recent advanced have been made
towards new instrumental methods for determination of carcass composition, i.e. X-ray
absorptiometry, bioelectrcal impedance or computer tomography (CT). It was stated that for
research conditions, X-ray absorptiometry is a simple an accurate alternative to carcass
dissection (Mercier et al., 2006). For bioelectrical impedance, the authors concluded that the
impedance contribution to accuracy of carcass disseciton was relatively small, and the
impedance method is not suitable for the prediction of carcass composition, neither in lambs
of similar weight nor in heterogeneous animals (Altmann et al., 2005). Johansen et al. (2007),
found that computer tomography is a accuracte and reliable tool for prediction of lamb carcass
composition. The fixed instrument costs of the instrumental methods are somewhat high,
however they are expected to pay off over time due to minimal labour costs, if they provide
similar or (hopefully) better precision and reliabilty than the reference dissection method for
lamb carcasses.
Conclusion
The precision and reliability of lamb carcass dissection as a reference method for lamb
carcass classification and grading in Norway were acceptable for carcass composition traits,
all achieving reliabilities higher than 0.8, both for weights and proportion of primal and sub-
primal yield and tissues. The results for sub-primals were not as accurate, varying both in
precision and reliability, and was especially poor for the sub-primal breast. Special attention
is needed for the sub-primal breast and side, due to large variation in fatness with increasing
carcass weight. Overall, the precision and reliability of carcass composition traits shows that
carcass dissection can be used as a reference method for carcass classification and grading.
The muscle tissue was most precise, while the fat tissue was most reliable. The precision was
somewhat similar for tissue weights and proportions, while the reliability was higher for
tissue weights. New instrumental methods (i.e. Computer Tomography) can provide a more
cost-effective alternative to butcher dissection.
15
Acknowledgements
This study was sponsored by grant 162188 of the Research Council of Norway, as part of a
Ph.D. study program. The pilot plant butchers at Animalia are acknowledged for their
professionalism and skills concerning dissection of lamb carcasses.
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Calibration models for lamb carcass composition analysis using
Computerized Tomography (CT) imaging
J. Johansen a,b,⁎, B. Egelandsdal b, M. Røe a, K. Kvaal c, A.H. Aastveit b
a Animalia – Norwegian Meat Research Centre, P.O. Box 396 Økern, N-0513 Oslo, Norwayb Dept. of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
c Dept of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
Received 17 October 2006; received in revised form 9 February 2007; accepted 29 March 2007
Available online 13 April 2007
Abstract
Dissection of carcasses is a costly, laborious and time-consuming method of assessing carcass tissue composition, and is often inaccurate due to
human measurement errors (i.e. cutting error). The need for accurate, non-invasive and objective measurements, both scientifically and
industrially, have introduced CT (Computerized Tomography) as a robust, cheaper and less time-intensive tool. Digital images from CT can be
used to model carcass tissue composition, introducing direct estimation (Otsu thresholding and Parallel Factor Analysis (PARAFAC)) and
multivariate calibration methods (Partial Least Square Regression (PLS).and multi-way PLS (NPLS)). 15 anatomical sites on 120 lamb Norwegian
carcasses were CT scanned before they were commercially dissected. The data was separated into calibration (n=84) and test set (n=36). The
results showed that multivariate calibration using NPLS gave the best results for fat and muscle tissue with respect to prediction error (RMSEP).
© 2007 Elsevier B.V. All rights reserved.
Keywords: Lamb carcass composition; Computerized tomography; Otsu; PARAFAC; Multivariate calibration; Prediction; Dissection
1. Introduction
Dissection of carcasses is a common reference method for
assessing carcass composition of farmed animals, worldwide. The
goal of dissection is to measure the composition of carcass tissues
such as fat (adipose) and muscle. Dissection is a costly and time-
intensive method, and the accuracy and repeatability may vary
between countries, operators and type of animal scheduled for
dissection (lamb, pig, cattle etc). Traditionally, the alternative to
dissection is chemical analysis of carcasses or primal cuts,
yielding standard chemical solutions as a reference for carcass
composition, such as protein (nitrogen), water, fat and ash [1]. The
need for cost-efficient and non-invasive assessment of carcass
composition, has introduced Computerized Tomography (CT) as
an alternative for dissection of carcasses.
The development of CT scanning methods and technology
can be divided into phases assigned to single decades [2],
ranging from whole body scanning (1970's), fast single slice
scanning and sequential CT (1980's) to fast volume scanning
and spiral CT (1990's). For medical purposes, most of
sequential CT is replaced by spiral CT, due to time-demanding
table-feed procedures and patient movements such as breathing.
For studies of inanimate objects such as animal carcasses, object
or “patient” movements, are not considered a problem.
For human body composition, CT has been introduced as an
indirect method to replace or as a supplement to traditional
methods for body density and volume measurements (underwater
weighing, air-displacement plethysmography), dilution methods
(total body water, extra cellular and intracellular water), total body
potassium, urinary creatine excretion, densitometry and anthro-
pometry [3,4]. Reconstruction of total body mass and organ
separation are of excellent accuracy (b1%). The CT images can
also separate adipose tissues (subcutaneous vs. visceral fat), lean
and muscle tissue (skeletal muscle vs. organ mass). Both single
sequential and spiral scanning are applied for these purposes [5–7].
The principle of CT is based on the attenuation of X-rays
through an object or tissue. Larger density of object or tissue
yields larger attenuation of X-rays. This direct relationship
Chemometrics and Intelligent Laboratory Systems 87 (2007) 303–311
www.elsevier.com/locate/chemolab
⁎ Corresponding author. Animalia – Norwegian Meat Research Centre, P.O.
Box 396 Økern, N-0513 Oslo, Norway. Tel.: +4722092246; fax: +4722220016.
E-mail address: jorgen.johansen@animalia.no (J. Johansen).
0169-7439/$ - see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.chemolab.2007.03.009
between attenuation and density can be used to separate tissues,
due to the different densities i.e. for fat and muscle tissue. Fat
tissue has somewhat lower density than muscle tissue. From a
CT image, the tissues will appear with different gray values,
depending on the density of the tissue. Darker gray represents
lower density than lighter gray values. This gray scale can be
utilized to separate tissues or objects in the CT image.
Most animal applications are on live pigs or pig carcasses
[8–11] due to the magnitude and abundance of pigs in meat
production. During the EU Project “EUPIGCLASS” [12], the
results showed that CT holds great potential as an indirect
method for predicting pig carcass composition such as lean meat
(muscle) content, and weights of cuts. For lamb carcasses, the
main focus has been to assess breeding values on live animals,
using a small number of anatomically defined scans [13–15].
Otsu thresholding [16] is one method widely used to segment
different tissues or segments in CT or other types of gray-scale
images. For carcass tissues, pixel value segments may vary be-
tween and within animals depending on the density and mixture
of tissues (intramuscular fat within muscle). Dobrolowski et.al.
[9] reported a problem with adapting certain pixel values as
estimates for various body components in grading. This was due
to non-exact delimitation ofmuscle tissue ranges on the gray scale
range due to influence of intramuscular fat. Using multivariate
calibration of dissected carcass tissue, against the intensity histo-
grammay deal with these problems, yielding more correct ranges
for tissues and more exact estimations. Principal Component
Analysis (PCA) and Partial Least Square (PLS) [17] is based on a
bilinear decomposition of two-dimensional (samples⁎variables)
data into scores and loadings [18]. The CT data sets in this trial are
three-dimensional (samples⁎variables⁎ length/anatomy). Three-
dimensional data sets have to be unfolded, averaged or sum-
marized into 2D data set to be handled by bilinear PCA or PLS.
On the other hand, there are multivariate techniques designed to
handle multidimensional data sets, like the Parallel Factor
analysis (PARAFAC) and multidimensional PLS (NPLS) [19–
21]. These techniques use in this case, trilinear decomposition of
the data, yielding scores for one mode in sample space, and
loadings for two modes or variables spaces. PARAFAC provides
unique solutions for components in the data sets, using the
optimal number of components in the dataset found via valida-
tion. The unique solutions based on the optimal number of
components, can be used as direct estimates of the different
components or tissues in the dataset. NPLS uses the same
decomposition principle as PLS, except that multidimensional (n-
way) data matrices are used instead of two-way (samples⁎vari-
ables) data matrices. By validating these methods against a
separate test set, the precision and accuracy for a real world
application will be tested.
The aim of the study was to find the best calibration models
for prediction of fat and muscle tissue in lamb carcasses with
respect to prediction accuracy (error and bias).
2. Experimental
2.1. Sampling
One hundred and twenty (120) lambs from a single Norwegian
abattoir were sampled according from August to September in
2005. The experimental designwas set up to cover the variation in
all levels of fatness in the carcasses based on the principle of over-
sampling at the extremes [22]. The carcasses were sampled in
three groups; low, intermediate and high level of fatness.
Selection was made using fatness score from the EUROP carcass
grading system for lamb in Norway [23]. Low fatness equals –2
standard deviations (std) below mean fatness score value High
fatness equals +2 std above mean value. Forty (40) % of all the
samples were selected from each of the groups with low and high
fatness, and 20% were selected from the group with intermediate
fatness (Table 1), yielding a 40–20–40 grouping of the designed
samples. The data was split into two sets; calibration (84 samples)
and test (36 samples). The calibration set was selected using the
first seven samples for every ten samples (1–7, 11–17,…,111–
117), and the test set was selected using the last three samples for
every ten samples (8–10, 18–20,…,118–120) The similarity of
the two data sets (calibration and test set) was visualized using
multi-way PCA (Fig. 1).
2.2. Computerized Tomography (CT)
The lamb carcasses were scanned at the Norwegian
University of Life Sciences using a Siemens Somaton Emotion
Table 1
Sampling and experimental design chosen for the investigation, n=120
N=120 Low fatness Intermediate
fatness
High fatness
% n % N % n
Design 40 48 20 24 40 48
Calibration set 39 33 19 16 42 35
Test set 42 15 22 8 36 13
Number and percentage of samples in each group. Calibration set (n=84) and
test set (n=36).
Fig. 1. Multiway Principal Component Analysis (MPCA) score plot for a 2-
component model. 72.68% explained variation in X. Space of calibration (●)
(black) and test set (□) (red). 95% confidence level for detection of outliers
(dashed, blue). Sample #20 suspected outlier.
304 J. Johansen et al. / Chemometrics and Intelligent Laboratory Systems 87 (2007) 303–311
CT Scanner. The measurements from the X-ray detectors were
reconstructed by the instrument software into an image
(tomogram). A tomogram is a [512×512] image matrix,
where each element in the matrix represents a pixel with a
given gray value (black to white). The level of gray scale values
in a CT image is measured by using Hounsfield Units (HU)
[24]. The purpose of the HU scale is to center gray values in the
area of biological tissues, where water is assigned HU values 0.
The HU and gray value scale are parallel, where HU=0 equals
gray value=1024. The protocol of CT scanning is given in
Table 2.
2.3. Import and pre-processing of images
The CT scanner generated images in DICOM format. The
images were imported into MATLAB Version 7.3.0.67
(R2006b) © The MathWorks, Inc, using the Image Processing
Toolbox Version 5.3 (R2006b) routine dicomread.
2.4. Commercial dissection, end point reference (Y)
A team of 7 highly skilled butchers at the Norwegian Meat
Research Centre dissected the 120 lamb carcasses. The lambs
were dissected according to Norwegian Meat Industry com-
mercial standards, as a whole carcass (not split in two halves).
The carcasses were cut into five major cuts: leg, loin, side,
shoulder and breast. Each of the major cuts was separated and
sorted into fat, muscle and bone tissue, and the mass (kg) of the
different tissues were estimated for the entire carcass. Dissected
fat (kg) and muscle (kg) was used as end point reference (Y-
vector) for calibration.
2.5. Anatomical sites selection
Fifteen (15) anatomical scanning sites (discrete sequential
scan) spanning the entire carcass were selected using dorsal
vertebras as fixing points (Fig. 2). A color code represented
different anatomical sections; cervical (neck), thoracic (shoul-
der and breast), lumbar (mid-part, side), sacral (pelvic region)
and caudal (tail) and leg. The anatomical sites were selected to
span the entire length of the carcass. A high X-ray dose
(170 mAs) was selected to increase the resolution of the tomo-
grams. Most of the sites were selected from the mid-section of
the carcass, used for grading of lamb carcasses [25–30]. In
addition to grading sites from mid-section of the carcass,
additional sites on the leg, shoulder, breast and neck were
selected to cover as much variation as possible. For each lamb,
15 images were generated, generating a 3-way array
[1×400×15], yielding a [120×400×15] data array for the
entire samples. Calibration models using only one anatomical
site at a time was applied to find the “best” anatomical site for
prediction of fat and muscle tissue (kg).
Table 2
CT protocol used for scanning of lamb carcasses
Topogram Sequence
100 mA 170 mAs
130 kV 130 kV
Slice width: 2.0 mm Scantime: 0.8 s
Width: 1024.00 mm (512) Slicewidth: 3 mm
Height: 1024.00 mm (512) Width: 400.00 mm (512)
Resolution: 0.500 pixels per mm Height: 400.00 mm (512)
Tube position: AP Resolution: 1.280 pixels per mm
Direction: Caudiocranial Number of scans: 15
Kernel: T80s (sharp) Direction: Caudiocranial
Window: 256–64 Kernel: B50M
HU[0]=Gray value[1024] Window: 100–50
HU[0]=Gray value[1024]
Fig. 2. 15 pre-processed CT images acquired on all scanning sites, from neck (1) to knee joint of leg (15).
305J. Johansen et al. / Chemometrics and Intelligent Laboratory Systems 87 (2007) 303–311
2.6. Modeling
The calibration models were constructed using PLS_Toolbox,
Eigenvector Research Inc. 3.5.1b [31]. The histograms of the CT
images were used for calibration (X-data), yielding 2280 gray
level values fromblack (0) towhite (2280). Since CT images have
a storage capacity of 12 bits [32], the possible range of gray values
per pixel is [0, 4096]. No pixels were detected above 2280, so the
range was limited to [0, 2280]. The corresponding range of HU
values were [-1024, 1256]. Two types of histograms were
generated, (1) Two-dimensional histogram of each sample using
the sum of the 15 anatomical sites and (2) Three-dimensional
histogram of each sample using the 15 anatomical sites. Carcass
samples of the different histograms are shown in Figs. 3 and 4.
Only ranges of HU values that are relevant to fat and muscle
tissues are visualized in the figures [24]. The range shown is HU
value from -200 to 300. The histograms were mean-centered and/
or scaled beforemodeling to test for the effect of pre-processing of
the histograms.
2.6.1. Direct estimation - OtsuTo find the optimal threshold to separate fat, muscle and
bone tissue in the CT histograms, a threshold method presented
by Otsu [33,34] was used. This algorithm is an implementation
of the Otsu thresholding technique. The histograms are divided
in two classes and the inter-class variance is minimized. This
method selects the optimal threshold to separate objects from
their background. The optimal threshold (k) to separate object in
class 1 and 2 is calculated maximizing the between-class vari-
ance. The thresholding was performed in three steps: (1) sepa-
rating bone from soft tissue, (2) separating soft tissue from dark
background, (3) separating fat and muscle in soft tissue. The
algorithm was carried out using ‘graythresh - global imagethreshold using Otsu's method’ using the Image Processing
Toolbox. The sum of pixels within these thresholds was used as
estimates for fat and muscle tissue.
2.6.2. PARAFACPARAFAC [20] was used to estimate unique solutions for fat
and muscle tissue using the 3D gray value histograms. The
decomposition of the 3D data array (2) was made into triads or
trilinear components, but instead of one score vector and one
loading vector as in PCA, each component consisted of one
score vector (samples) and two loading vectors (CT histograms
and anatomical sites) (trilinear) [20]. The optimal number of
components for the PARAFAC model was selected using the
core consistency test, seeking core consistency as close to 100%
as possible [20]. A low or negative core consistency (%) may
indicate an over fitted and unstable PARAFAC model. To
stabilize the PARAFAC solutions, different constraints like non-
negativity and unimodality were applied to the model [20]. The
Fig. 3. Carcass sample of 2D Gray value histogram, CT images. Sum of all 15
anatomical sites.
Fig. 4. Carcass sample of 3D gray value histogram. Gray value histogram per anatomical site.
306 J. Johansen et al. / Chemometrics and Intelligent Laboratory Systems 87 (2007) 303–311
PARAFAC models were tested with and without (raw) applying
constraints.
2.6.3. Multivariate calibrationThe gray value histograms from the CT images were used for
multivariate calibration. The 2D histograms were modeled using
Partial Least Square Regression (PLSR) [18] and the 3D histo-
grams using multi-way PLS; NPLS [19]. All histograms were
mean-centered before modeling. The models were calibrated
against commercially dissected fat and muscle (kg). In addition,
PLSRmodelswere also fitted to each anatomical site, using the 2D
histogram from each site, seeking the “best” single anatomical site.
The accuracy of the predictive ability of the PLS and NPLS
calibration models were validated using full leave-one-out
cross-validation. The differences in predictive ability of models
were tested seeking lowest Root Mean Square Error of Cross-
Validation (RMSECV). RMSECV is regarded as a measure of
model quality, and is defined by:
RMSECV ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
n
X
n
i¼1
ycvi yi" #2
s
ð1Þ
where n is the number of samples in the calibration set, the yi'sare the real (measured) responses and the y i
cv's are the estimated
responses found via cross-validation [35].
The optimal number of latent components in the PLS and
NPLS models were determined using the minimum prediction
residual sum of squares (PRESS).
2.7. Prediction
When the performance of the calibration set was tested and
the optimal number of latent components using RMSECV and
PRESS was found, the predictive ability of the calibration
models was validated using a test set. The test set validation was
applied using Root Mean Square Error of Prediction and
systematic errors in predictive values (BIAS):
RMSEP ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
n
X
n
i¼1
y pi yið Þ2
s
ð2Þ
where n is the number of samples in the test set, the yi's are thereal (measured) responses and the yi
p's are the estimated
responses found via cross-validation
BIAS ¼1
n
X
n
i¼1
y pi yið Þ ð3Þ
where n is the number of objects tested, the yi's are the real(measured) responses and the yi
p's are the estimated responses
found via cross-validation
3. Results & Discussion
3.1. CT histograms
One carcass sample of the 2D and 3D gray value histograms are
shown in Figs. 3 and 4. The highest peaks in the 2D histograms are seen
with HU value interval [-64, -54] and [61, 71], separated by a valley
with gray value interval [-9, 1]. The first peak was identified as a fat
tissue peak, the second peak as a muscle tissue peak, separated by a
valley (Fig. 3). These values corresponds to some degree with previous
intervals identified for fat and muscle tissue [5,24,36]. The frequency
for muscle is larger than for fat, which is in accordance with the amount
of fat and muscle that is present in the CT images and carcass.
The peaks in the 2D histogram are found as ridges is the 3D histogram
(Fig. 4), where the two ridges are identified as fat (smallest) and muscle
(largest). The frequency of the 3D histogram varies between ana-
tomical sites, where the largest intensity for fat tissue is found in the
shoulder and mid-section of the animal. This corresponds with dis-
section results, where the largest amount of fat tissue is found in these
anatomical regions of the carcass. For muscle tissue, the largest
intensity is found in the shoulder region and leg region. This also
corresponds with dissection results, where the largest amount of
muscle tissue is found in these anatomical regions of the carcass.
Overall, pre-processing using mean-centering provided the best
results for prediction. Scaling of CT histograms did not improve the
results. Scaling may disturb the smoothness and shape of the CT
histograms, which seem to be an important feature for estimating of
fat and muscle tissue.
3.2. Modeling
3.2.1. Direct estimationFrom the Otsu thresholding, 3 thresholds were identified. First,
bone was separated from the soft tissue, yielding an estimated threshold
(C) with HU value of kC=296. Second, soft tissue was separated from
the background noise, yielding an estimated threshold (A) with HU-
value kA=-156. Finally, a threshold separating fat and muscle (B)
tissue was estimated, yielding a HU value of kB=10. The sum of pixels
within these thresholds was used as estimates of fat and muscle tissue.
The results for estimation of fat and muscle tissue using Otsu
thresholds are shown in Table 3. Otsu threshold estimates explained
95.5% and 94.3% of the variation in fat and muscle tissue, respectively,
yielding a RMSE of 0.463 kg and 0.657 kg fat and muscle tissue,
respectively.
For the PARAFAC estimation, a PARAFAC model of the 3-way CT
data matrix was fitted. If the CT data is trilinear by nature, the true
underlying histograms will be found if the right number of components is
used and the signal-to-noise ratio is appropriate [20]. The loadings for CT
histograms should represent the decomposition of CT histograms into
histograms of true carcass tissues when an optimal solution is found. The
scores for each of the components may then serve as estimates of carcass
tissues (fat & muscle). Models were estimated with one to four factors.
Based on core consistency, the PARAFAC model with two components
were considered optimal. More than 3 components yielded negative
core consistency values, which indicates over fitting and instability of
the PARAFAC models. This also reflects the characteristics of the two
Table 3
Direct estimation of fat and muscle tissue (kg)
Model Fat tissue Muscle tissue
R2 RMSE (kg) R2 RMSE (kg)
Otsu 0.9549 0.4630 0.9432 0.6571
PARAFAC 0.9413 0.5282 0.9342 0.7072
PARAFAC - non-negative 0.9429 0.5208 0.9060 0.8455
PARAFAC - unimodality 0.9432 0.5193 0.9087 0.8330
Explained variance and RMSE values for calibration.
307J. Johansen et al. / Chemometrics and Intelligent Laboratory Systems 87 (2007) 303–311
tissue types; fat and muscle. Different constraints like unimodality and
non-negativity were applied to the model. For fat tissue, unimodality
constraints seemed to give the best fit, explaining 94.3% of the variation
in fat tissue; RMSE of 0.519 kg fat. The unimodality constraint seeks
single modes or peaks (histogram) of each component (unimodal) and
this seem yield the best fit for fat tissue. For muscle tissue, the
unconstrained (raw) model seemed to give the best fit, explaining 93.4%
of the variation in muscle tissue; RMSE of 0.707 kg muscle. A single
peak may not be the best solution for muscle tissue, showing that there
may be several modes or peaks (multimodality) in the muscle component
in PARAFAC.
If reference values from dissection (Y) are not available or of poor
quality for calibration, direct estimation may be applied directly for CT
scanned carcasses. However, these will be virtual estimates of CT
attenuation, and accuracy and bias related to real-world data should
always be checked.
3.2.2. Multivariate calibrationThe optimal number of latent components in the PLS model was 2
components for fat tissue, and 5 components for muscle tissue, using
RMSECV and PRESS as criteria for optimal number of components
(Fig. 5). The models indicate that the relationship between muscle and
dissection is somewhat more complex than for fat and dissection. This
may be related to the phenomena revealed in the PARAFAC analysis,
where muscle tissue could consist of several modes in the CT
histogram. Explained variance (RMSEC) for the PLS models were
95.6% and 95.8% for fat and muscle tissue, respectively, using the
optimal number of components. RMSECV values for the models were
Fig. 5. PRESS (blue) and RMSECV (green) values for latent components 1 to 10, PLS and NPLS modeling. Fat (left) and muscle (right) tissue modeling.
Table 4
Multivariate calibration of fat and muscle tissue (kg)
Model Fat tissue Muscle tissue
R2 RMSEC (kg) RMSECV (kg) # comp R2 RMSEC (kg) RMSECV(kg) # comp
PLS 0.9560 0.4573 0.4895 2 0.9578 0.5666 0.6601 5
NPLS 0.9564 0.4553 0.4920 2 0.9641 0.5221 0.6049 4
PLS modeling of 2D summarized CT histograms. NPLS modeling of 3D CT histograms. Explained variance, RMSEC, RMSECV, and optimal number of latent
components.
308 J. Johansen et al. / Chemometrics and Intelligent Laboratory Systems 87 (2007) 303–311
0.490 kg and 0.660 kg for fat and muscle tissue, respectively, using the
optimal number of components (Table 4).
The optimal number of latent components in the NPLS model was 2
components for fat tissue, and 4 components for muscle tissue, using
RMSECV and PRESS as criteria for optimal number of components
(Fig. 5). The RMSECV and PRESS values seem to increase slightly
after 4 components, then drop from 5 to 8 components (Fig. 5). This
small increase after 4 components may indicate the optimal number of
components is selected. Due to risk of over fitting, even though
8 components seem optimal, 4 components are considered optimal.
Explained variance (RMSEC) for the NPLS model was 95.6% and
96.4% for fat and muscle tissue, respectively, using the optimal number
of components. RMSECV values for the models were 0.492 kg and
0.605 kg for fat and muscle tissue, respectively, using the optimal
number of components (Table 4).
In addition, PLS models were fitted to each anatomical site (15 sub-
models), to find the “best” single anatomical site for prediction of fat
and muscle tissue. The best anatomical site was selected using
RMSECV value for each model (15 sub-models) as selection criteria.
The result for fat tissue is shown in Fig. 6 and for muscle tissue in
Fig. 7. For fat tissue, the best predictor seems to be anatomical site F or
#6. This is located in the side region next to the 10th rib (Fig. 2). This is
in accordance with previous publication locating the best predictor for
industrial prediction of fat tissue by probing side or back fat thickness.
The optimal number of components or complexity of the model was
also lowest in the side region (F to K; #6 to #12). For muscle tissue, the
best predictor seems to be site N or #14. This is the central part of the
leg region, where the large muscles are located. The number of
components seems to vary between anatomical sites, but was lowest for
site J, K, L and N. These are large single muscles, which are more
uniform in CT images compared to muscles in the shoulder region. One
exception was site M, which yielded optimal number of components
11. When zooming in on the images in Fig. 2, there seem to be some
noise in the muscle-bone borderline, and the number of components
may be affected by this.
Multivariate calibration is dependent on highly reproducible reference
values to yield robust models. The reproducibility of commercial
dissection has not been tested in this paper. Since commercial dissection
is performed manually by operators, an error in Y is highly probable.
From a practical point of view, this is a risk which is a consequence of
sampling commercial data. For future calibrations, a measure of the
reproducibility (error) of commercial dissection should be analyzed.
3.3. Prediction
The calibration models were tested for predictive ability using a test
set. From this test set, bias and prediction error (RMSEP) were obtained.
Table 5 show the results from the test set validation. The RMSEP values
for fat tissue were similar or slightly lower than the RMSECV values
from the calibration models. For muscle tissue, the RMSEP values were
larger than the RMSECV values. This means that the models for fat
tissue are very robust and in accordance with results found in modeling
Fig. 6. RMSECV for each anatomical site (A-neck, O leg). Fat tissue (kg).
Fig. 7. RMSECV for each anatomical site (A-neck, O leg). Muscle tissue (kg).
Table 5
Prediction of fat and muscle tissue (kg)
Model Fat tissue Muscle tissue
R2 RMSEP
(kg)
Bias
(kg)
R2 RMSEP
(kg)
Bias
(kg)
Otsu 0.9637 0.4843 0.2376 0.9234 0.9773 0.6108
PARAFAC 0.9593 0.6567 -1.17e-15 0.8921 0.9068 1.47e-15
PARAFAC –
non-neg
0.9526 0.5428 2.18e-15 0.8020 1.1412 -1.44e-15
PARAFAC -
unimod
0.9555 0.5048 5.50e-14 0.8365 1.0408 -1.14e-14
PLS 0.9695 0.4480 0.2271 0.9528 0.8051 0.5497
NPLS 0.9700 0.4423 0.2205 0.9607 0.7718 0.5301
Explained variance, RMSEP and bias.
Fig. 8. Predicted vs. measured, Fat (kg). Otsu, PARAFAC and NPLS model.
Target line (X=Y) shown as solid black. Otsu (◇) thresholding, PARAFAC (□)and NPLS (△) prediction models. Test set (n=36).
309J. Johansen et al. / Chemometrics and Intelligent Laboratory Systems 87 (2007) 303–311
or calibration. For muscle tissue, the models were not so robust, yielding
a RMSEP value 0.2 kg larger than the RMSECV value found in
modeling. NPLS models for fat and muscle had the lowest RMSEP
values. For both fat and muscle tissue, there was a systematic error (bias)
between predicted and measured values using Otsu estimation and
multivariate calibration. For fat tissue, bias was between 0.22 kg to
0.23 kg fat. For muscle tissue, the bias was between 0.53 kg and 0.61 kg.
The PARAFAC estimations showed no systematic errors (very low bias),
both for fat and muscle tissue. With respect to RMSEP values for the
PARAFAC models, the result was somewhat poorer for fat and muscle
than for NPLS. However, the difference in RMSEP was not large,
especially for fat prediction. The predicted vs. measured values for the
different models are shown in Figs. 8 and 9. From the figures, the
systematic bias seems to be constant along the range of values, both for
fat and muscle tissue (kg). Bias correction was performed on the
predicted values for Otsu estimation and multivariate calibration. The
results showed that the models were improved after bias correction
(Table 6), yielding lower RMSEP values than before bias correction. In
this case, bias correction proved to be advantageous for the models, but
this may not necessarily be the case for all types of models. The benefit
from bias correction in this case, proved to be a result from the systematic
error which was constant for the whole range of fat and muscle tissue
(kg).
4. Conclusion
Computer Tomography images can be a useful tool for
predicting fat and muscle tissue in lamb carcasses. Using ana-
tomical scanning (anatomical sites repeated each time); anatom-
ical sites can be compared between animals. 3D calibration using
NPLS seem to give the best model fit and lowest RMSEP values.
Using the 3D structure of data proved to be advantageous.
Acknowledgments
This study was sponsored by grant 162188 of the Research
Council of Norway, as part of a Ph.D. study program. Engineer
Knut Dalen at the Norwegian University of Life Sciences is
acknowledged for his technical contribution to this paper. The
butchers at the pilot plant at Animalia are acknowledged for
their skills in dissection. Professors Chris Glasbey at BioSS and
Rasmus Bro at the Faculty of Life Sciences, University of
Copenhagen are acknowledged for fruitful discussions and
valuable contributions to this paper.
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Table 6
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R2 RMSEP
(kg)
R2 RMSEP
(kg)
R2 RMSEP
(kg)
Otsu 0.9637 0.4221 -3.57e-05 0.9234 0.7630 -1.33e-05
PARAFAC 0.9593 0.6567 -1.17e-15 0.8921 0.9068 1.47e-15
PARAFAC –
non-neg
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PARAFAC -
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PLS 0.9695 0.3862 -1.25e-05 0.9528 0.5882 -2.31e-06
NPLS 0.9700 0.3834 3.67e-05 0.9607 0.5610 -1.01e-05
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311J. Johansen et al. / Chemometrics and Intelligent Laboratory Systems 87 (2007) 303–311
1
Virtual dissection of lamb carcasses using computer tomography (CT) and its
correlation to manual dissection
J. Kongsroa,b,*, M. Røea, A.H. Aastveitb , K. Kvaalc, B. Egelandsdalb
a Animalia – Norwegian Meat Research Centre, P.O. Box 396 Økern, N-0513 Oslo, Norway
b Dept. of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
c Dept of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
Abstract
119 lambs from a single abattoir in Norway were scanned using Computer Tomography
(CT) at different equidistances (40, 80, 160 and 320 mm) to perform a virtual dissection of the
carcasses, separating muscle, fat and bone tissue. The population of sheep used covered the
commercial range of breeds and body composition in Norway and the full range of fat and
conformation scores. After CT scanning, the carcasses were manually dissected by trained
butchers. The volume and weight of all carcasses were estimated using Cavalieri estimation of
the different equidistances of CT slices. The precision and reliability of virtual dissection
were estimated from repeated measurements using splitting of carcasses into 2 halves. The
results showed that virtual dissection (r > 0.95) was more precise and reliable than manual
dissection (r > 0.80), both for carcass tissue weights and proportions. The correlation between
virtual and commercial dissection was high for carcass weight and muscle tissue weight,
however, lower for fat and bone tissues. The precision and reliability of virtual dissection, and
the correlation between virtual and manual dissection were highest using low equidistance CT
scanning (40 mm). There were some biases between virtual and manual dissection, especially
for bone tissue. The source of bias can be explained by inaccurate manual dissection
performed by the butchers and underestimation of bone using Cavalieri estimation.
Keywords: Virtual dissection, lamb carcass, computer tomography, precision, reliability, density, estimation, carcass tissues
* Corresponding author. Phone: +4722092246; Fax: +4722220016.
E-mail address: jorgen.kongsro@animalia.no (Jørgen Kongsro)
2
1. Introduction
Tools to predict carcass composition for grading and classification of carcasses generally
use dissected composition as a reference. This reference is usually obtained by manual
dissection performed by skilled butchers. However, the labour and economic costs of
dissection have introduced new technologies for estimation and prediction of animal carcass
tissues, i.e. Computer Tomography (CT), Magnetic Resonance Imaging (MRI) and visible
light imaging (Dobrowolski et al., 2004; Romvari et al., 2006; Szabo et al., 1999). These
technologies have been nicknamed “Virtual Dissection”, due to the handling and dissection of
carcass samples in virtual space by image analysis and computer programming. These
methods allow carcass and animal tissues (in vivo) to be studied and predicted in a non-
destructive way.
Computer Tomography has been used for human diagnostics since the 1970s. During the
1980s CT was applied to predict animal carcass tissues (Skjervold et al., 1982; Standal, 1984;
Szabo et al., 1999). The work from the 1980’s focused on pigs, however, later work was done
for sheep and lamb (Sehested, E., 1986). The results showed that the correlation between CT
and manual dissection performed by butchers or chemical analysis was very good, and the
standard error of predictions was lower compared to previous methods such as ultrasound.
However, no error estimate or repeatability test was carried out for the reference (manual
dissection) used in these studies. The repeatability of chemical analysis is regarded to be
better than for manual dissection, since it is objective and not sensitive to human errors. Since
then, several studies have been carried out on pig and lamb carcasses using manual dissection
as reference (Dobrowolski et al., 2004, Johansen et al., 2007, Jones et al., 2002, Lambe et al.,
2003, Lambe et al., 2006, Navajas et al., 2007, Szabo et al., 1999). The work by Nissen et al.
(2006) presented new information concerning the accuracy of the manual dissection reference
of pig carcasses. Nissen et al. (2006) found that pig carcass dissection was highly accurate
with respect to lean meat percentage; however, some significant effects between butchers with
respect to lean meat weight and percentage were found. Kongsro et al. (2008) found that lamb
carcass dissection was acceptable with respect to precision and reliability of manual carcass
tissue dissection. Manual and CT (virtual) dissection can be compared with respect to
precision and reliability by using repeated measurements of carcasses; left and right halves of
carcasses, utilizing the symmetry of animals along the spinal column. Calibrations of new
technologies like CT using carcass dissection as reference are completely dependent on the
accuracy of the reference dissection. The direct relationship between CT attenuation and
3
tissue density may prove carcass dissection to be redundant for future applications. Direct
estimation using CT attenuation (CT values; HU) has proven to yield accurate, robust and
unbiased estimates of carcass composition in lamb carcasses (Johansen et al., 2007).
Estimation of volume and mass in biological samples using CT can be performed by
scanning single slices (sequential CT) or spiral scanning (spiral CT) (Kalender, 2005). By
using sequential CT, several scans are taken using a fixed distance (sequence length) (i.e.
40mm) between positions zn-1 to zn. A CT image (tomogram) is provided at each position. The
object is transported (table feed) for the defined distance between the positions. The number
of positions (n) will depend on the length of the object at a selected resolution along z. An
object of 1 m or 1000 mm will need 25 positions along the length of the carcass when using a
40 mm sequence (1000 mm/ 40 mm). Each sequential scan will be a discrete sample from the
entire object, and the accuracy of sequential scanning will depend on the sequence length
(distance between zn-1 and zn). For spiral CT, the scanning procedure is continuous, using one
single scan with rotating scanners from position zn-1 to zn. A CT image is provided for each
rotation. Spiral scanning is regarded as faster and more accurate than sequential scanning,
since images are provided from continuous sampling (spiral), rather than discrete (sequential)
sampling. In this study, the carcasses were scanned using sequential CT. In addition to
comparison of manual and virtual dissection with respect to accuracy, repeatability and
reliability, different section distances using sequential CT scanning were applied to study the
effect of section distance on the prediction of carcass tissues. A study by Thompson and
Kinghorn (1992) suggested how many scans or minimum section distances were required to
accurately predict the volume and subsequently the weight of any body component for
prediction.
The objective of this study was (1) to describe and quantify the precision and reliability of
virtual lamb carcass dissection using Computer Tomography (CT) sequential scanning using
different equidistances (section distances), and (2) study the correlation between manual and
virtual dissection at different equidistances.
4
2. Materials & methods
2.1. Experimental samples
The samples were collected from an abattoir in the central part of southern Norway during
autumn 2004, during a classification and grading study of Norwegian lambs. One hundred
and nineteen carcasses were collected during the slaughter season and classified using the
EUROP system (Johansen et al., 2006). The carcasses were chilled for 24 hours, then
transported to the Norwegian University of Life Sciences, where they were CT scanned. After
CT scanning, the carcasses were transported to the Norwegian Meat Research Centre’s pilot
plant, where they were dissected (Johansen et al., 2006; Kongsro et al., 2008). The population
of sheep used covered the commercial range of body composition in Norway and the full
range of fat and conformation scores. The balance of sexes was approx 65 % male and 35 %
female lambs. Norwegian White breed was in majority among the breeds (58 %), Spæl breed
26 % and Dala breed 10 %. The rest were Old Norwegian Spæl, Steigar and Rygja breeds (< 6
%). The distribution of breeds reflected the Norwegian sheep population.
2.2. CT scanning
The lamb carcasses were scanned at the Norwegian University of Life Sciences using a
Siemens Somaton Emotion CT Scanner. The measurements from the X-ray detectors were
reconstructed by the instrument software into an image (tomogram) (Johansen et al., 2007).
An equidistance of 40 mm was used as a basis (Figure 1), generating an image stack of 23 to
28 CT images depending on carcass length. The CT images from the CT scanner were
processed by the Siemens computer software, and transferred to a CD-rom. In average, 20
carcasses were scanned each week, during the 6 week period.
5
Figure 1. Lamb carcass, CT sequential scanning. Equidistance of 40 mm. Carcass were split using virtual dissection along the spine column.
2.3 Image analysis and computer programming
The CT images were imported and analyzed using MATLAB (Version 7.3.0.267
(R2006b), August 03, 2006, Copyright 1984-2006, The MathWorks, Inc) and the Image
Processing Toolbox (V5.3 (R2006b)). Artefacts (CT feed table couch); kidneys and internal
fat were removed using region of interest (ROI) and binary masking. The carcass tissues (fat,
muscle and bone) were segmented from the CT images using reference HU values
representing the different tissues (Kvame and Vangen, 2007; Jopson et al., 1995). Figure 2
shows the relationship between frequency distribution of HU values and tissue thresholds
(T1-T3), and the mean tissue density within each tissue threshold (M1-M3). Bone tissue was
segmented using HU range of 147 (T3) to 3072, creating a binary mask representing bone
tissue. The bone marrow was included in the bone tissue, and the bone density was corrected
for marrow (bone density = osseous tissue density + marrow density) to make the bone
fraction more comparable to the bone fraction identified through dissection. The marrow was
included using a flood-fill operation on the binary bone tissue images. The muscle tissue was
segmented using HU range of -22 to 146 (T2), creating a binary mask representing muscle
tissue. Fat tissue was segmented using HU range of -194 (T1) to -23, creating a binary mask
representing fat tissue.
6
Figure 2. Frequency distribution of fat, muscle and bone in one single carcass (all slices). Segment thresholds (T1-T3) and mean HU values (M1-M3) for each carcass tissue (1=fat, 2=muscle, 3=bone).
2.4. Estimation of tissue density
The average densities of tissues (fat, muscle and bone) were estimated using the mean HU
value for all segmented tissue images in the stack. The estimated density is a function (1) of
true density (Campbell et al., 2003), where:
True density = HU * 0.00106 + 1.0062 (1)
2.5. Virtual dissection. Estimation of volume and mass.
Table 1. Section distance and number of images per carcass (depending on carcass length). Section distance (resolution)
Number of images per carcass
40 mm (1/1) 23-27 80 mm (1/2) 12-14 160 mm (1/4) 6-7 320 mm (1/8) 3-4
When no dissection data are available to establish prediction equation for carcass tissue
weights, the Cavalieri stereological method can be applied to estimate tissue volumes and
weights (Gundersen et al., 1988; Lambe et al., 2007). In this study, all carcasses were
7
dissected, and dissection data made available for comparison of virtual and manual dissection.
The Cavalieri method involved the stack of 23 to 28 CT images (tomograms), depending on
carcass length, starting at a random point and moving from the lamb carcass neck to leg at an
equidistance of 40 mm. The volume of the tissues was estimated using the equation (2)
(Roberts et al., 1993):
Volume (cm3) = total area of carcass tissue (cm2) x section distance (cm) (2)
The total area of carcass tissue was calculated from the tissue image stack, where the
number of pixels was multiplied by the area of each pixel. The grid based on 40 mm
equidistance was split into 1/2; 80 mm, 1/4; 160 mm and 1/8; 320 mm to test the effect of
resolution of the sequential grid (Table 1). Tissue volumes (dm3) were converted to weights
(kg) using the average estimated tissue density CT image HU values (3):
Mass (g) = Tissue volume (cm3) x average tissue density (g/cm3) (3)
2.6. Manual dissection performed by butchers
The carcasses were transported from the CT scanner at the University of Life Sciences to
the Animalia pilot plant in Oslo, where the fat, muscle and bone were separated by dissection
(Johansen et al., 2006). Dissection traits (carcass fat, muscle and bone tissues) in weight (kg)
and as proportions (%) were estimated according to guidelines presented in previous studies
of lamb carcass dissection (Johansen et al., 2006; Kongsro et al., 2008).
2.7. Statistical data analysis
To test the precision and reliability of the virtual dissection, the carcasses were split in two
halves using ROI and binary masking, using the spinal column as the fixing point. This split
utilizes the symmetry of animals along the spine column, where the left and right side can be
treated as repeated measurements of each other. Each image in the tissue stacks was split
using binary masking on each image, splitting the carcasses perpendicular along the spine.
The split was done according to guidelines presented by Kongsro et al. (2008) to simulate real
carcasses at time of cutting.
All data analysis were performed using MATLAB Version 7.4.0.287 (R2007a), January
29, 2007, Copyright 1984-2007, The MathWorks, Inc (The MathWorks, 2007). The
8
difference in dissection traits were calculated using absolute difference between the two sides
(left-right).
The precision of virtual dissection was estimated by the relative standard deviation (RSD)
of the difference between the two carcass halves, using the ratio of the standard deviation of
the difference between the two sides (left-right) and the average carcass side (left-right), sub-
primal or tissue weight / proportion (Kongsro et al., 2008). The RSD was expressed as a
fraction, but more usually as a percentage and was then called coefficient of variation (CV)
(4):
%100..
xMean
differenceofdsCV = (4)
The reliability (REL) of dissection was defined as the correlation (r) between the repeated
measurements (the two carcass sides) (Kongsro et al., 2008) (5):
222
22),cov(σσσ
σσ
σσ ++
+==
CB
CB
rl
rl XXREL (5)
where l is left, and r right side.
9
3. Results and discussion
3.1. Sampled data
Table 2. Descriptive statistics, carcass, sampled data (n=119). N Mean S.d. Min Max Carcass weight1 119 18.17 3.25 8.90 27.90 Carcass weight2 119 17.89 3.16 8.70 27.15 Carcass length (cm) 119 96.03 3.90 80 104 EUROP conformation class 119 6.56 (R-) 2.11 1 12 EUROP fat class 119 5.98 (2+) 1.78 2 12 1 Cold carcass weight at time of cutting
2 Virtual dissection using Computed Tomography (CT)
The average carcass weight for all Norwegian lambs in 2004 (year of sampled data) was
18.39 kg (Røe, 2005). Average EUROP conformation and fat class were 6.26 (O+) and 5.68
(2+), respectively (Røe, 2005). The sampled data were close to or a little lower than the lamb
population average in Norway in 2004 for carcass weight, with somewhat higher mean and
std. value for the sampled data for conformation and fat class (Table 2).
10
3.2. Precision and reliability of the virtual dissection method using CT scanning on the two
carcass halves. Effect of section distance.
Table 3. Estimated dissection traits yield and carcass tissues fat, muscle and bone for each ½ carcass (n=119 carcasses; 238 halves). Mean value in kg and %, standard deviation (s.d.), absolute side (left-right) difference, coefficient of variation (CV, %) and reliability (REL) correlation coefficient (r) in kg and % for all section distances using sequential scanning (equidistance). Dissection
traits
Mean
(kg)
s.d. Diff
(kg)
CV
(%)
REL Mean
(%)
s.d. Diff
(%)
CV
(%)
REL
½ Carcass weight
8.95 1.58 0.14 1.21 0.99
Fat 0.99 0.40 0.04 3.14 0.99 10.86 3.41 0.30 2.12 0.99 Muscle 6.45 1.17 0.10 1.26 0.99 72.10 2.74 0.56 0.60 0.97
40 mm
Bone 1.51 0.22 0.05 2.62 0.96 17.05 1.83 0.47 2.16 0.95 ½ Carcass weight
8.96 1.60 0.15 1.37 0.99
Fat 1.00 0.40 0.04 3.48 0.99 10.97 3.52 0.38 2.83 0.99 Muscle 6.48 1.22 0.13 1.71 0.99 72.22 3.10 0.91 1.10 0.92
80 mm
Bone 1.49 0.26 0.08 4.49 0.92 16.81 2.62 0.88 4.62 0.90 ½ Carcass weight
0.91 1.66 0.23 2.04 0.99
Fat 1.14 0.46 0.07 4.53 0.99 12.45 3.97 0.63 3.74 0.98 Muscle 6.38 1.25 0.19 2.28 0.99 70.47 4.25 1.41 1.80 0.90
160 mm
Bone 1.53 0.36 0.13 8.73 0.86 17.08 3.49 1.38 7.68 0.85 ½ Carcass weight
8.91 1.96 0.26 2.86 0.98
Fat 1.04 0.45 0.08 8.30 0.97 11.52 3.70 0.79 5.59 0.97 Muscle 6.33 1.43 0.15 8.38 0.87 71.13 4.58 1.58 1.70 0.91
320 mm
Bone 1.53 0.44 0.18 8.38 0.87 17.35 3.72 1.72 7.36 0.83
The precision and reliability of the virtual dissection method was defined by the coefficient
of variation (CV, %) and the correlation (r) between repeated measurements (carcass sides;
left-right), respectively. Table 3 shows the estimated mean value and standard deviation (s.d.)
of the dissection traits, the absolute difference between repeated measurements, the precision
and reliability for section distances (equidistance) from 40 mm to 320 mm using sequential
CT scanning. The carcass side difference increased with increasing equidistance. When using
40 mm section distance, the precision and reliability for ½ carcass weight was 1.21 % and
0.99, respectively. For fat weight and proportions, the precisions and reliabilities were 3.14
and 2.12 %, and 0.99, respectively. For muscle weight and proportions, 1.26 and 0.60 %, and
0.99 and 0.97, respectively. For bone weight and proportions, 2.62 and 2.16 %, and 0.96 and
0.95, respectively. For the other equidistances (80 mm, 160 mm and 320 mm), the precisions
and reliabilities were gradually lower (Table 3) by increasing equidistance.
11
The geometry of fat and muscle tissues was considered to be less irregular compared to
bone (unidirectional) throughout the carcass, i.e. the complex geometry of the rib cage,
having a curved shape which was difficult to model using sequential scanning. This was
especially valid for high section distance (low resolution, 320 mm), where the Cavalieri
estimation was not as precise and reliable compared to lower section distances (40 – 160
mm).
All section distances showed acceptable reliability according to the definition by Nissen et
al. (2006), where reliability above 0.8 was considered an acceptable reference method. For
section distance of 40 mm, the results were excellent, where all reliabilities were above 0.95.
Compared to the precision and reliability of manual dissection presented by Kongsro et al.
(2008), the virtual dissection was both more precise and reliable. For ½ carcass weight, the
results for manual dissection (Kongsro et al., 2008) were 1.56 % and 0.98 for precision and
reliability. For fat, muscle and bone weight, the results were 4.34 % and 0.98, 2.27 and 0.96,
and 4.48 % and 0.85, for precision and reliability, respectively. For fat, muscle and bone
proportion, the results were 4.11 % and 0.93, 1.19 % and 0.80, and 3.00 and 0.90, for
precision and reliability, respectively. For fat and muscle, the results for virtual dissection
using section distances of 40 mm and 80 mm seem to be more precise and reliable compared
to manual dissection. For bone, only the 40 mm section distance seems to be better than
manual dissection.
3.3. Virtual vs. manual dissection
Table 4. Descriptive statistics, manual and virtual dissection (n=119).
Dissection
traits n Mean Std Min Max
Fat (kg) 119 2.45 0.93 0.72 6.34 Muscle (kg) 119 11.07 1.96 5.70 17.56 Bone (kg) 119 3.97 0.58 2.27 5.71 Fat (%) 119 13.23 3.36 8.14 26.09 Muscle (%) 119 61.02 2.75 50.73 67.51 M
anua
l1
Bone (%) 119 22.06 2.05 16.54 25.96 Fat (kg) 119 1.97 0.80 0.44 4.93 Muscle (kg) 119 12.90 2.35 6.44 20.47 Bone (kg) 119 3.02 0.44 1.81 4.34 Fat (%) 119 10.86 3.42 4.20 20.23 Muscle (%) 119 72.10 2.75 65.17 79.39 V
irtu
al2
Bone (%) 119 17.05 1.83 12.74 20.89 1 Manual dissection performed by 5 trained butchers
2 Virtual dissection using Computed Tomography (CT) equidistance 40 mm
12
The correlations between manual and virtual dissection for fat and muscle tissue seemed to
be relatively stable with increasing equidistance, and correlation of bone drops with
increasing equidistance (Figure 3). The most plausible explanation for this phenomenon is the
irregular nature of bone shapes (i.e. complex geometry of rib cage) which is described less
efficiently with greater equidistance. From 40 mm to 160 mm, fat and muscle tissue only
showed minor changes with respect to correlation, which may be indicative that 6 to 7 scans
per lamb carcass or 160 mm is enough to cover variation in total fat and muscle tissue in the
lamb carcasses. For bone, 40 mm or 23 to 27 scans per lamb carcass seemed insufficient to
cover the variation in total bone tissue in lamb carcasses.
The descriptive statistics from the sampled data, both virtual and manual dissection,
showed some differences between the two dissection methods (Table 4). The amount of
muscle tissue was higher for virtual dissection both in kg and %, compared to manual
dissection. This may be due to that manual dissection underestimates the muscle content by
leaving too much meat on bones and the sorting of manufacturing meat by fat content
(Kongsro et al. 2008). The Cavalieri estimation overestimates muscle content by extrapolating
the void between section distances using section images, where the bone structure is not
completely covered, compromising bone and replacing the void with muscle. The difference
in carcass weight between carcass weighing and virtual dissection (Table 2) may also be the
results of underestimation of bone and overestimation of muscle. Bone has higher density
than muscle, and the carcass weight will therefore be somewhat lower when muscle is
overestimated. The amount of fat was smaller compared to manual dissection, both in kg and
%. The reason for this difference may be due to the practise of sorting meat into manufactured
meat. The inaccuracy of butchers separating fat from lean muscle was reflected in the work by
Kongsro et al. (2008). There may also be an effect of Cavalieri estimation, where fat depots
are not well modelled or extrapolated.
13
40 mm 80 mm 160 mm 360 mm0.4
0.5
0.6
0.7
0.8
0.9
1
Equidistance
Co
rrela
tio
n (
r)
Fat (kg)
Muscle (kg)
Bone (kg)
Fat (%)
Muscle (%)
Bone (%)
Figure 3. Correlation between manual dissection and virtual (CT) dissection (equidistance 40, 80, 160 and 320 mm).
The relationship between true carcass weight and CT estimated carcass weight is shown in
Figure 4. Virtual dissection with 40 mm equidistance is used for comparison with manual
dissection and true carcass weight. The correlation is very high (r=0.99) and there is only a
minor bias (difference between red target line (Y=X) and blue predicted line) for large
carcasses, where carcass weight is slightly underestimated. This underestimation is reflected
in the descriptive statistics, and may be the results of Cavalieri estimation, overestimating
muscle at the sacrifice of bone (higher density).
14
5 10 15 20 25 305
10
15
20
25
30
CCW (kg)
CT
(kg
)
Figure 4. Relationship between true carcass weight (kg) and CT carcass estimated carcass weight (kg).
For manual vs. virtual dissection, the correlation of fat in kg is good (r=0.90) (Figure 5).
There is some bias for fat in kg, where fat is underestimated; i.e. for 5 kg dissected fat, the
difference is approx. 1 kg (4 kg for virtually dissected fat).
0 1 2 3 4 5 6 70
1
2
3
4
5
6
7
Dissected fat (kg)
CT
fa
t (k
g)
Figure 5. Relationship between manual dissected fat (kg) and CT carcass estimated fat (kg).
For muscle in kg, the correlation (r=0.98) is higher than for fat (Figure 6), but the bias and
drift from red target line is the opposite from fat; muscle in kg is overestimated and the
overestimation increases with increasing amount of muscle in kg; i. e. 12 kg of dissected
muscle corresponds to 14 kg of virtual dissected muscle.
15
4 6 8 10 12 14 16 18 20 224
6
8
10
12
14
16
18
20
22
Dissected muscle (kg)
CT
muscle
(kg)
Figure 6. Relationship between manual dissected muscle (kg) and CT carcass estimated muscle (kg).
For bone in kg, the relationship is good (r=0.92) (Figure 7), but there is a large drift and
bias from the red target line where bone is highly underestimated, i. e. 4 kg of dissected bone
corresponds to 3 kg of virtually dissected bone.
1 2 3 4 5 61
2
3
4
5
6
Dissected bone (kg)
CT
bon
e (
kg
)
Figure 7. Relationship between manual dissected bone (kg) and CT carcass estimated bone (kg).
The bias and drift from target line Y=X were reflected in Table 4, where differences in
manual and virtual dissection are shown. The standard deviations (s.d.) between the different
16
dissection methods are almost identical, however, the std. for muscle tissue in kg, seemed to
be somewhat larger for virtual dissection. The butchers seem to shrink the scale of muscle
tissue, due to poorer separation of muscle, fat and bone tissue. For bone, the std. seems to be
somewhat larger for manual dissection, which may be a direct result of the combination
between the assumptions used in virtual dissection, and muscle residue left on bone by the
butchers. Adjustment of tissue thresholds within reasonable limits using the peaks and valleys
in Figure 2 was tested to reduce the bias between manual and virtual dissection. The tissue
estimates or the bias did not change significantly when adjusting thresholds back and forth for
all tissues, indicating that the thresholds are robust, flexible and inelastic. Another reason for
the bias in fat, muscle and bone between the two dissection methods may be the scanning
method, where the section distance using sequential scanning is not accurate enough to detect
the variation in tissues, especially for bone shape and size. The underestimation of bone using
Cavalieri estimation of the void between the sections has an effect on the estimation of the
other tissues and the volume and mass of the entire object (lamb carcass). The solution for
this problem is to scan with smaller section distances (< 40 mm) or use spiral scanning. With
new CT scanners, spiral scanning is part of the standard operating protocol, and will probably
be the fastest and best option available. The bias between manual and virtual dissection
seemed to be a sum of two sources of error; butcher and Cavalieri estimation, where the
Cavalieri estimation error increases with increasing section distance.
Formulas for tissue density, like the one used by Campbell et al. (2003) for estimation of
tissue density, have been suggested to be instrument dependent (Håseth et al., 2007).
Differences between CT scanners or units must be accounted for by testing the validity of the
tissue density formula. Such test was not performed in this study; however, it is recommended
in future studies.
17
4. Conclusion and application
Virtual dissection using sequential scanning in this study has proved to be an alternative to
manual dissection, despite some bias between manual and virtual ones. The high values for
the correlations suggest that prediction of fat, lean and bone would be accurate using CT.
It is assumed that the butchers cannot cut with the same accuracy as CT, and this needs to
be accounted for in the virtual value assessment. The precision of virtual dissection in this
study was higher compared to manual dissection and was highest with dense sequential
scanning (40 mm), and lower with increasing equidistance (80 to 320 mm). Complex bone
structures and irregular 3D structures could have led to some bias between virtual and manual
dissection, especially for bone, which was highly underestimated. The bias in carcass tissue
seems to be a combination of inaccurate butcher dissection and overestimation of muscle
tissue by Cavalieri estimation and sequential scanning for virtual dissection. By correcting for
bias and assessing size of butcher error, virtual dissection can be put into practical use.
Introducing spiral scanning or reducing sequential section distance, to cover the more
variation in irregular components of the skeleton and reducing void between sections, may
also prove advantageous.
Acknowledgements
This study was sponsored by grant 162188 of the Research Council of Norway, as part of a
Ph.D. study program. Engineer Knut Dalen is acknowledged for operating the CT Scanner at
UMB, Ås and for valuable discussions that has contributed to this paper. The pilot plant
butchers at Animalia are acknowledged for their practical skills and knowledge.
18
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21
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1
Prediction of fat, muscle and value in Norwegian lamb carcasses using EUROP
classification, carcass shape and length measurements, visible light reflectance
and computer tomography (CT)
J. Kongsroa,b*, M. Røea, K. Kvaalc, A.H. Aastveitb and B. Egelandsdalb
a Animalia – Norwegian Meat Research Centre, P.O. Box 396 Økern, N-0513 Oslo, Norway
b Dept. of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
c Dept of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
Abstract
84 carcasses were sampled to compare different techniques or methods for prediction of
lamb carcass composition and value. Four methods that are used at the Norwegian Meat
Research Centre, Animalia, were selected. These were basic EUROP classification, advanced
EUROP classification using carcass shape and length measurements, visible light reflectance
probing (GP) and Computer Tomography (CT). Multivariate Calibration models were
developed for all techniques against dissected fat and muscle, carcass value and carcass value
per kg. The calibration models were validated using a separate test set of 36 sampled
carcasses. The best prediction models were obtained using CT, with respect to explained
variance, prediction error and bias. Basic EUROP classification seemed to be the most biased
technique of the four types selected in this trial. EUROP assessors seemed to underestimate
large carcasses, especially with respect to muscle in kg and carcass value. Due to high cost
and low operating speeds of CT, optical probing (GP) may be the second best solution of the
technologies used in this study, combined with a CT dissection reference as an alternative to
manual dissection.
Keywords: Lamb, carcass composition, prediction, multivariate calibration, computer tomography, visible light reflectance, probing, EUROP
classification, carcass shape and length measurements
* Corresponding author. Phone: +4722092246; Fax: +4722220016.
E-mail address: jorgen.kongsro@animalia.no (Jørgen Kongsro)
2
1. Introduction
A number of different technologies for measuring composition of carcasses on-line
(automated sampling or analysis) exists, i.e. visual classification, linear measurements using
human or computer vision, visual, IR or NIR reflectance, conductivity, bio-impedance and
computer tomography (CT). The most common technology used in the meat industry is visual
classification, either by use of the EUROP (Europe) or the USDA classification system (North
America). Lamb production in Norway, and in many countries worldwide, is based on season
slaughter, where a large number of animals are slaughtered during a limited period of time
during autumn. Season-based slaughter presents a challenge with respect to on-line
measurements of carcasses. The first challenge is speed, where a large number of carcasses
pass through the abattoirs during slaughter. The second challenge is calibration and
repeatability, where it is difficult for operators to be accurate from year to year when the
assessment is carried out during a short period each year.
Accurate assessments and prediction of carcass tissue and value are of great importance for
suppliers of meat to consumers. In a review by Stanford et al. (1998) of methods for
predicting lamb carcass composition, a large set of in and ex vivo methods for prediction of
lamb carcass composition based on previous published or unpublished trials are described, but
they did not do a comparison of methods based on real data. This study compares a smaller
set of ex vivo methods using real data for comparison. In Norway and the rest of Europe, the
carcass classification system, EUROP is used on-line, and is practiced by trained operators or
classifiers by visual appraisal (Johansen et al., 2006). Their evaluation time is relatively short
(5 to 10 seconds), but the accuracy has proven to be poor, especially for carcass muscle
content and value (Johansen et al., 2006). An advanced version of the EUROP system
(Commission Regulation (EC) No 823/98, 1998; Commission Regulation (EEC) No 461/93,
1993), which is used by national inspectors (Johansen et al., 2006) make use of specific
carcass shape and length measurements, and is regarded to be more accurate, but the
evaluation time is higher than the standard EUROP procedure. Grading probes using visible
light reflectance are used in the meat industry for pig carcasses in Norway, but is not currently
used for lambs or sheep. The evaluation time is fast, and a trained operator only needs 5
seconds for carcass evaluation. The accuracy of probes is acceptable, especially for pig
carcasses, but the subcutaneous fat is more difficult to assess for lamb and sheep compared to
pig, due to the lack of rind to support the probe and more heterogeneous distribution of fat in
lamb and sheep carcasses. Pigs have a higher percentage of body subcutaneous fat compared
3
to ruminants such as sheep and cattle. Recent versions of grading probes can also assess meat
quality, i.e. marbling, colour and water-holding capacity. Computer Tomography (CT) has
been shown to hold great potential for early and accurate assessment, both for live animal and
carcass fatness, muscularity and weight (Cross and Belk, 1994; Kvame et al., 2004; Lambe et
al., 2003; Macfarlane et al., 2004; Macfarlane et al., 2006; Stanford et al., 1998). However,
CT is regarded as an expensive tool and the carcass evaluation time required is somewhat
higher than that of other on-line methods. Recent advances on development of CT scanners
have introduced multi-slice scanning combined with spiral scanning that have achieved
speeds from 4 to 16 images per second (Lewis et al., 2006). With this speed, carcasses can be
evaluated and assessed according to carcass composition at chain speed, on-line in abattoirs.
The repeatability, reproducibility and reliability of visual classification using EUROP
classification are found to be good (Johansen et al. 2006) using classes to classify carcasses,
however, there were some bias between the Commission standards assessed by National
assessors and industry assessors. The operators are consistent; however, the system has
proven to predict carcass composition poorly, especially for lean meat or muscle content. For
visible light reflectance using probes, a number of factors influencing the accuracy in addition
to calibration were reported by (Fisher, 1997; Olsen et al., 2007). These include maintenance
of instruments, training of operators and working conditions. The authors found that the
variations between operators were more important than variations between the probe
instruments from different manufacturers. For CT, the results from Johansen et al. (2007)
showed that CT was a useful tool for predicting fat and muscle tissues in lamb carcasses.
The most common reference method used for calibration of technologies for carcass
composition is carcass dissection. For visual classification like EUROP (Johansen et al. 2006;
(Commission Regulation (EC) No 823/98, 1998; Commission Regulation (EEC) No 461/93,
1993) and USDA classification (Fisher, 1997; Olsen et al., 2007; USDA, 1992), regulations
between or within countries for carcass conformation class and fat class are determined and
used as reference method. The visual classification methods are most often supervised by
national assessors using the regulations as reference method (Johansen et al. 2006). Dissection
has proven to be accurate for lean meat content in pig carcasses (Nissen et al., 2006), but
somewhat poorer with respect to repeatability and reliability with respect to fat and muscle
(lean meat) for lamb carcasses (Johansen et al., 2007). New reference methods using
Computer Tomography (CT) has proven to be more accurate, faster and more cost-effective
for estimating lamb carcass composition (Kongsro et al., 2008). The results showed that CT
was more precise and reliable compared to manual dissection, however there were some
4
biases between manual and CT dissection, especially for estimation of bone, which could be
explained by butcher and CT estimation error.
The main objective of this study was to compare the accuracy and prediction of carcass
soft tissues (fat and muscle) weights and carcass value using different on-line technologies for
lamb carcasses; EUROP, advanced EUROP using linear measurements, visible light grading
probe and computer tomography (CT).
2. Materials and methods
2.1 Experimental data
One hundred and twenty (120) lambs from a single Norwegian abattoir were sampled
during autumn of 2005. The experimental design was set up to cover the variation in all levels
of fatness in the carcasses based on the principle of over-sampling at the extremes (Engel et
al., 2003; Johansen et al., 2007b). The carcasses were sampled in three groups; low,
intermediate and high level of fatness (Johansen et al., 2007b). The data was split into two
sets; calibration (84 samples) and test sets (36 samples).
2.2 EUROP classification
The lamb carcasses were classified using the European system EUROP, according to
conformation and fat group (Johansen et al., 2006). In this study, the EUROP classification
system was assessed by trained National assessors in a cool storage room at Animalia pilot
plant. This was done to ensure the best possible accuracy and repeatability of measurements.
2.3. EUROP advanced – carcass shape and length measurements
In addition to ordinary EUROP classification and carcass weight, several carcass shape and
length measurements were sampled from the carcasses (Fig. 1). These measurements were
sampled by Animalia assessors using manual rulers. The measurements were based on the
detailed rules laid down by the EU commission concerning the classification of ovine animals
(Commission Regulation (EEC) No 461/93, 1993).
5
Figure 1. EUROP advanced carcass shape (white or gray L1-L4, R1 and F1-F2) and length / width (black) measurements based on the detailed rules laid down by the EU commission concerning the classification of ovine animals. In addition, carcass length from 1st anterior rib to carcass steel hook was measured. Measurement sites for Hennessy grading probe; GP-1 (backfat thickness, loin thickness and total thickness of back) and GP-2 (side thickness).
2.4 Optical probe using visible light reflectance
The carcasses were probed using Hennessy Grading Probe ® (model GP4; Hennessy
Grading Systems Ltd, Auckland, New Zealand). Two probing sites (GP-1 and GP-2) were
used (Fig. 1); the 1st point measured total tissue, fat and lean thickness over rib eye between
the last and 12th rib 2 cm from the midline of the lamb carcass (Einarsdottir, 1998). The 2nd
point measured total tissue thickness in the side, located between the mid-line and rib-end,
between 10th and 11th rib (Malmfors, 1988). In addition, carcass weight was included in the
GP model. Repeated measurements were done randomly during this trial for training
purposes.
2.5. Computer Tomography (CT)
The lamb carcasses were scanned at the Norwegian University of Life Sciences using a
Siemens Somaton Emotion ® CT scanner. The data collected by the X-ray detectors were
reconstructed by the instrument software into an image (tomogram). A tomogram is a [512 x
512] image matrix, where each element in the matrix represents a pixel with a given gray
value (black to white). The level of gray scale values in a CT image is measured by using
Hounsfield Units (HU) (Hounsfield, 1979). The purpose of the HU scale is to center gray
values in the area of biological tissues, where water is assigned HU values 0. The HU and
gray value scale are parallel, where HU = 0 equals gray value = 1024. The frequency
GP-1
GP-2
6
distributions of pixel HU values (histogram) for were used for calibration (X-data), yielding
2280 gray level values from black (0) to white (2280). 15 anatomical scans were taken across
the entire carcass (Johansen et al., 2007), and the histograms of HU values were generated for
the sum of pixels from all anatomical sites.
2.6. Commercial dissection, end point reference (Y)
A team of 5 highly skilled butchers at Animalia (Norwegian Meat Research Centre) pilot
plant dissected the 120 lamb carcasses. Whole carcasses from lambs were commercially
dissected according to Norwegian Meat Industry commercial standards (Johansen et al.,
2006). The carcasses were cut into five major cuts: leg, loin, side, shoulder and breast. Each
of the major cuts was separated and sorted into fat, muscle and bone tissue, and the mass (kg)
of the different tissues were estimated for the entire carcass. Dissected fat (kg), muscle (kg)
and value in NOK (Norwegian kroner) and NOK / kg was used as end point reference (Y-
vector) for calibration. The reference prices for the major cuts are retrieved from Norway
Meat commercial industry prices for lamb cuts in autumn of 2006. The currency was
approximately 1 EUR = 8 NOK (Oct. 2007).
2.7. Statistical data analysis – modelling
2.7.1. Multivariate calibration
All calibration models were constructed using a calibration set of 84 samples (Tab. 1).
The EUROP data matrix contained EUROP conformation, fat class and carcass weight. The
EUROP advanced data matrix contained linear measurements, EUROP conformation and fat
class, and carcass weight. The optical probe data matrix contained GP fat cover (GP-1), GP
muscle thickness (GP-1), GP total thickness (GP-1), GP side thickness (GP-2) and carcass
weight. The CT data matrix contained the frequency distribution (histograms) of HU values
range from [-200,200] (Johansen et al., 2007) in addition to carcass weight. The data for the
different models were mean-centered and scaled using the autoscale option in PLS_Toolbox
4.1, Copyright 2006 Eigenvector Research, Inc. for use with MATLAB R2007a, Version
7.4.0.287 (R2007a), Copyright 1984-2007, The Mathworks, Inc. All the data matrices were
calibrated against the end-point reference Y (dissected fat and muscle (kg) and value in NOK
and NOK/kg). The calibration models were constructed using Partial Least Square Regression
(PLSR) (Martens and Martens, 2001) and were modelled using PLS_Toolbox function
analysis. The calibration models were validated using full leave-one-out cross-validation. The
optimal number of PLS components in the calibration models were decided seeking the
7
lowest Root Mean Square Error of Cross-Validation (RMSECV) (Esbensen, 2000). RMSECV
is regarded as a measure of model quality, and is defined by:
∑=
−=n
i
i
cv
i yyn
RMSECV1
2)ˆ(1
(1)
where n is the number of samples in the calibration set, the i
y ’s are the real (measured)
responses and the i
y ’s are the estimated responses found via cross-validation (Cederkvist et al., 2005).
During multivariate calibration, important predictors in each model were found using the
PLS_Toolbox 4.1, Variable Importance in Projection (VIP) (Chong and Jun, 2005). VIP
estimates the importance of each variable used in a PLS model. A variable with VIP score
larger than 1 can be considered to be important in a given model.
2.7.2. Prediction
When the performance of the calibration set was tested and the optimal number of latent
components using RMSECV was found, the predictive ability of the calibration models was
validated using a test set. The test set validation was applied using Root Mean Square Error of
Prediction (RMSEP) and systematic errors in predictive values (BIAS):
∑=
−=n
i
i
p
i yyn
RMSEP1
2)ˆ(1
(2)
where n is the number of samples in the test set, the yi’s are the real (measured) responses and
the i
y ’s are the estimated responses found via cross-validation:
)ˆ(1
1i
p
i
n
i
yyn
BIAS −= ∑=
(3)
where n is the number of samples in the calibration set, the i
y ’s are the real (measured)
responses and the i
y ’s are the estimated responses found via cross-validation.
8
3. Results
Table 1. Mean values and variation of different carcass traits measurements on lamb carcasses. Calibration and validation (test) set. Carcass traits Mean1 Std1 Mean2 Std2
Weight (kg) 18.49 5.48 19.09 5.28 Fat (kg) 3.27 2.19 3.51 2.23 Muscle (kg) 10.35 2.77 10.95 2.59 EUROP Conformation class 6.33 (O+) 3.48 6.28 (O+) 3.13 EUROP Fat class 6.40 (2+) 3.64 6.47 (2+) 3.32 HGP fat thickness loin (GP-1) 45.63 33.51 51.11 36.37 HGP muscle thickness loin (GP-1) 287.20 100.53 302.11 90.61 HGP total thickness loin (GP-1) 419.18 95.15 439.97 81.71 HGP side thickness (GP-2) 147.45 72.63 156.22 67.71 Value (NOK3) 893.72 287.01 953.64 283.24 Value (NOK3/kg) 49.75 1.79 50.17 1.98 1 Calibration set (n=84)
2. Validation (test) set (n=36)
3 1 NOK = 0.126 EUR
In Table 1, the mean value and variation of the measurements applied on the carcasses are
shown. The carcass weight in kg is close to the national mean value (18.39 kg) of Norwegian
lamb carcasses in 2004 (Røe, 2005), both for the calibration and validation (test) set. For
conformation and fat class, the mean value was close to national mean (6.26) for
conformation class, and somewhat higher than national mean (5.68) for fat class. The larger
value for fat class is due to the set up of experimental design, spanning the variation of fatness
in carcasses.
Table 2. Prediction of fat (kg). Number of components (#) in PLS model, explained variance calibration (R2
cal), calibration error using cross-validation (RMSECV), explained variance prediction (R2
pred), prediction error using test set validation (RMSEP), bias and most important variable in projection (VIP). # R
2cal RMSECV R
2pred RMSEP Bias VIP
EUROP1 2 0.697 1.200 0.620 1.373 -0.191 Fat class
EUROP2
ADV
4 0.755 1.080 0.588 1.421 -0.109 Fat class
GP3 5 0.876 0.767 0.904 0.694 0.055 GP side
CT4 3 0.922 0.614 0.935 0.571 0.085 HU value -63
1 Basic EUROP
2 Advanced EUROP; carcass shape and length measurements
3 Visible light reflectance; Hennessy Grading Probe
4 Computer Tomography
The prediction of fat in kg is shown in Table 2. Only a small difference in prediction of fat
(kg) was found between basic and advanced EUROP classification. The calibration results
9
(RMSECV) was slightly better for EUROP advanced. EUROP advanced was also less biased
compared to basic EUROP. The most important predictor was EUROP fat class. The R2
values were 0.62 and 0.59 for basic and advanced EUROP, and the prediction error were 1.37
and 1.42, respectively. GP and computer tomography (CT) both achieved R2 values larger
than 0.9, with prediction error of 0.69 and 0.57, respectively. The most important predictor
(VIP) for fat (kg) for GP was GP side (side thickness), and the most important CT value was
HU = -63. Basic EUROP seemed to be slightly more biased (-0.19) compared to GP and CT
(0.05 and 0.08, respectively).
Table 3. Prediction of muscle (kg). Number of components (#) in PLS model, explained variance calibration (R2
cal), calibration error using cross-validation (RMSECV), explained variance prediction (R2
pred), prediction error using test set validation (RMSEP), bias and most important variable in projection (VIP). # R
2cal RMSECV R
2pred RMSEP Bias VIP
EUROP1 1 0.634 1.669 0.699 1.499 -0.516 Carcass weight
EUROP2
ADV
2 0.754 1.618 0.712 1.384 -0.151 L1
GP3 1 0.733 1.425 0.690 1.432 -0.164 Carcass weight
CT4 5 0.910 0.833 0.917 0.744 0.007 HU value 63
1 Basic EUROP
2 Advanced EUROP; carcass shape and length measurements
3 Visible light reflectance; Hennessy Grading Probe
4 Computer Tomography
The prediction of muscle (kg) is shown in Table 3. EUROP advanced seemed to perform
slightly better compared to basic EUROP, with R2 values of 0.70 and 0.71, and RMSEP
values of 1.50 and 1.38 for basic and advanced EUROP, respectively. Carcass weight and
linear measure L1 (circumference of m. semimembranosus) (Fig. 1), seemed to be the best
predictors for basic and advanced EUROP, respectively. GP did not improve the predictions
of muscle compared to EUROP, while CT showed major improvements, yielding a R2 value
of 0.92 and prediction error of 0.74. The most important predictors for GP and CT was
carcass weight and CT value HU = 63. Basic EUROP (-0.516) seem to be more biased than
the other methods, and seem to underestimate the muscle content in kg, especially for
carcasses with high content of muscle in kg. CT gave the smallest bias, and seems to be
virtually unbiased with respect to muscle in kg (0.007). The comparison of different methods
for prediction of muscle (kg) is shown in Figure 2.
10
4 6 8 10 12 14 16 184
6
8
10
12
14
16
18
Predicted muscle (kg)
Measure
d m
uscle
(kg)
dis
section
EUROP
EUROPADV
GP
CT
Figure 2. Measured vs. predicted; muscle (kg). Measurements: EUROP, EUROP advanced, Hennessy Grading Probe (GP) and computer tomography (CT). Table 4. Prediction of value (NOK). Number of components (#) in PLS model, explained variance calibration (R2
cal), calibration error using cross-validation (RMSECV), explained variance prediction (R2
pred), prediction error using test set validation (RMSEP), bias and most important variable in projection (VIP). # R
2cal RMSECV R
2pred RMSEP Bias VIP
EUROP1 1 0.728 148.850 0.727 155.250 -49.976 Carcass weight
EUROP2
ADV
3 0.831 117.390 0.736 144.770 -18.846 L1
GP3 2 0.833 116.720 0.830 116.460 -14.943 GP side
thickness CT
4 5 0.940 70.130 0.945 65.420 -0.660 HU value 63 Table 5. Prediction of value (NOK/kg). # R
2cal RMSECV R
2pred RMSEP Bias VIP
EUROP1 1 0.001 1.797 0.039 1.961 -0.402 Carcass weight
EUROP2
ADV
1 0.004 1.794 0.048 1.949 -0.379 Width of leg
GP3 1 0.027 1.766 0.103 1.892 -0.319 GP total loin
thickness CT
4 3 0.196 1.607 0.396 1.530 -0.173 HU value 70 1 Basic EUROP
2 Advanced EUROP; carcass shape and length measurements
3 Visible light reflectance; Hennessy Grading Probe
4 Computer Tomography
The predictions of value in NOK and NOK/kg are shown in Table 4 and 5. Basic EUROP
seemed to yield somewhat poorer predictions compared to advanced EUROP. Basic EUROP
11
seemed to be more biased than the other methods. The most important predictor for value in
NOK was carcass weight and linear measure L1 (Fig. 1), for basic and advanced EUROP,
respectively. For value in NOK/kg, the most important predictors were carcass weight and
width of leg (Fig. 1), for basic and advanced EUROP, respectively. GP and CT seemed to
improve the predictions, with CT yielding the highest R2 values and lowest RMSEP. The
most important predictors for GP and CT for value in NOK were GP side and CT value
HU=63. For value in NOK / kg, HGP total and CT value HU 70 were the most important
predictors. The variance explained for value in NOK / kg was low for all methods, not
achieving R2 values above 0.4 for prediction. CT gave the lowest bias of all the methods, -
0.66 and -0.17 for value in NOK and NOK / kg, respectively.
4. Discussion
All data in this study have been sampled in controlled environments. In an industry
environment, it is highly probable that the accuracy of measurements will be poorer due to
faster operating speed and other influential factors during production. These factors may be
handled by automating measurements, making them less vulnerable to human assessment or
operator error. EUROP and carcass shape and length measurements can be automated by
image analysis such as video image analysis or VIA. GP can be automated by using robotics
for probing to ensure high repeatability. CT is already an equipment-driven application, and
may be automated by computer programming and feeding carcasses into the CT during
slaughter. The speed and cost of CT is, however, still a major concern for industry
applications.
For prediction of fat tissue, the results showed that GP and CT predicted fat tissue well
achieving R2 higher than 0.9. EUROP predicted fat somewhat satisfactory, and the most
important predictor was EUROP fat class. The most important GP predictor was side
thickness (GP side), which indicates that side thickness of lamb carcasses is a very good
predictor of carcass fat. Previous studies of CT value frequency distribution (histogram)
showed that the CT histograms of lamb carcass soft tissues had two peaks, separated by a
valley; the left peak represented the amount of fat, and the right peak amount of muscle
(Johansen et al. 2007). The CT value HU=-63, which was the most important predictor for fat,
represented the local maxima of the left peak in CT histogram. In relation to cost and speed,
the results for GP showed that this method is very promising for prediction of carcass fat,
which was in accordance with the previous studies by Johansen et al. (2007) and Kongsro et
12
al. (2008). Side thickness measured by GP seemed to be a very good predictor of carcass
fatness.
For prediction of muscle, CT predicted muscle in kg well, achieving R2 higher than 0.9.
The other methods predicted muscle satisfactorily, whereas advanced EUROP using linear
measurements achieved the highest explained variance. Conformation does not seem to
contribute more to explaining variation in muscle (kg) compared to carcass weight using basic
EUROP, which indicates the importance of carcass weight as a single predictor for muscle.
Carcass weight is also objective and cheap for on-line use. If repeatability, reproducibility and
reliability of EUROP classification are poor, one can consider the use of carcass weight as a
single predictor of carcass muscle. The L1 measure (circumference of m. semimembranosus)
was the most important predictor of muscle using advanced EUROP, but carcass weight was
almost similar with respect to VIP measures. The circumference of m. semimembranosus
seemed to be a good indicator of muscle content in carcasses. Carcass weight was also the
most important predictor for GP measures, but the probe measurements (GP-1 and GP-2)
seemed to add some explained variation to the models compared to basic EUROP
classification. During this trial, there were some challenges using GP measurements in the
loin; site GP-1 (Fig. 1). For very small carcasses, it was especially difficult to probe the
muscle perpendicularly, and the accuracy and repeatability may be affected. By using frames
to align the probes, the accuracy and repeatability for measuring muscle thickness may be
improved. Repeated measurements showed that alignment was not critical for the accuracy
and repeatability of fat thickness. In a previous study (Johansen et al. 2006), underestimation
of muscle using basic EUROP classification was observed. This can also be observed in
Figure 2 and in the bias in Table 3. Basic EUROP seemed to underestimate large carcasses; a
bias introduced during training of assessors or operators and sampling of carcasses during
inspections and exams (too little variation in carcass size). The CT value 63, representing the
muscle peak in CT histograms, was the most important CT value for prediction of muscle.
For prediction of value in NOK, CT achieved the best prediction results. GP predicted
value rather well, and EUROP predicted value fair. Carcass shape and length measurements
using advanced EUROP did not improve prediction as for muscle. Carcass weight and L1
length of leg were the most important predictors for EUROP basic and advanced,
respectively, as observed for muscle. This indicates the high correlation between muscle in kg
and value in NOK. GP side thickness was the most important predictor for value in NOK
using GP measures, which seemed to reflect the value of the lamb cut side, which is
13
considered valuable in the Norwegian market, despite its high fat content (raw material for
dry cured lamb side; traditional Christmas meal in Norway).
The value in NOK / kg is not well predicted by either of the methods, not achieving R2
above 0.4. Table 1 shows that the standard deviation of value per kg was low, indicating that
there was not much variation to model. The value in NOK / kg seemed to be somewhat
constant, varying only +/- 2 NOK (~2 std.). Previous unpublished trials at Animalia pilot plant
has shown that the variation in NOK / kg of dissection was explained mainly by butchers. A
combination of limited variation in data for value per kg, and variance caused by butcher
dissection error can be the reason for the poor predictions made by the different methods. CT
gave the best predictions of the different methods, where the CT value HU=70 was the most
important predictor. This CT value represents a value close to the peak for muscle. The small
shift towards higher CT values may be caused by leaner muscle (higher CT value) yielding a
higher value per kg. However, the difference is only 7 HU units, and may not be a valid
difference or may be caused by repeatability (machine) error in CT (Allen and Leymaster,
1985).
In terms of accuracy, the different methods varied; basic EUROP seemed to yield the
lowest overall accuracy, while computer tomography (CT) seemed to yield the highest. For
muscle tissue, EUROP yielded the same accuracy as GP, but did yield a higher bias. In terms
of speed, the chain speed at Norwegian abattoirs (300-400 animals per hour during lamb
slaughter season) does not favour CT. In average, the CT speed was 6 minutes per carcass
using anatomical scanning. Using other scanning methods, such as spiral scanning, the speed
can be improved, but will probably not reach 6 seconds or less at chain speed. The high
accuracy of CT can be applied for dissection purposes (replacing dissection) or to obtain and
assess breeding traits. For industrial use, faster CT scanning methods and computer interfaces
must be developed for on-line use. The second best accuracy was achieved by the optical
probe (Hennessy grading probe; HGP). The HGP can be operated at chain speed of 6 seconds
or less by a trained operator or robot. This study shows that visible light reflectance probing is
an accurate method, achieving higher accuracy and smaller biases compared to the current
EUROP system used in European abattoirs. Of all the methods tested in this paper; using CT
as dissection reference (Kongsro et al. 2008), and optical probing as on-line tool calibrated
against CT, may be the best current application for assessing carcass composition and value.
Other methods not tested in this paper may provide a higher accuracy than GP. However, the
results in this trial proved that GP was an accurate tool, especially for fat prediction. For
14
muscle, a solid frame can help the probe to achieve higher accuracy and repeatability,
especially for small carcasses.
Breed and sex was not included in the data providing the calibration models. Introducing
breed and sex may improve predictions, however, since none of the methods included breed
and sex information; the comparisons of methods are still valid. The bias present in some of
the methods are well known in prediction of carcass composition (Hambrock, 2005). The
most common problems are overestimation of muscle in fat carcasses, and underestimation of
muscle in lean carcasses. The main source of error may be sex and breed error and changes in
animal material over time; however, this can be handled by proper sampling and experimental
design when performing calibrations. Dissection error may also influence the results, where
commercial dissection tend to lead to over- and underestimation of fat and muscle tissue
depending on carcass muscle and fat content. The butchers tend to cut or dissect
“economically” rather than “scientifically” (Johansen et al., 2007)
Recent advances of CT technology using multi-slice spiral scanning have improved the
speed of CT scanners. The scanning method used in this paper was sequential scanning with
fixed anatomical points. This is a time-consuming task, and was done to study the variation
and effect of different anatomical sites in a lamb carcass. By scanning the whole body using
multi-slice CT scanning, both speed and accuracy will be improved, due to continuous
scanning and coverage of the whole body and carcass tissues, respectively. This may lead the
way for CT scanning on-line in an industry environment or pilot plants. The hardware cost of
a basic CT scanner is in the range from 300.000 to 600.000 EUR, depending on the supplier,
technology and toolboxes. Maintenance may also be expensive compared to other methods,
but in the long run, the high accuracy and repeatability of measuring lamb carcass
composition using CT will pay off, both for farmers, butchers and suppliers of lamb carcasses
and meat.
15
5. Conclusion
Four different technologies for assessing lamb carcass composition and value were tested
in this study. The accuracy of carcass tissue prediction varied between the different
technologies, where computer tomography yielded the highest overall accuracy, and EUROP
classification yielded the lowest. Computer tomography gave the most unbiased predictions,
while EUROP classification did show some bias between predicted and measured carcass
tissue in kg, and carcass value. Due to high cost and low operating speeds of CT, optical
probing (GP) may be the second best solution of the technologies used in this study,
combined with a CT dissection reference as an alternative to manual dissection.
Acknowledgements
This study was sponsored by grant 162188 of the Research Council of Norway, as a part of
a Ph. D. study program. The butchers at the pilot plant at Animalia are acknowledged for their
dissection skills and valuable discussion concerning the value of meat. Engineer Knut Dalen
at the Norwegian University of Life Sciences is acknowledged for technical contributions
concerning computer tomography. Hennessy Grading Systems Ltd. is acknowledged for
support and information concerning their grading probe. Prof. Gunnar Malmfors is
acknowledged for fruitful discussions and knowledge concerning lamb grading and anatomy.
16
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