Boar taint and textural attributes of meat and meat produces from EM

and IM male pig ‐ sensory data statistical analysis and modeling

IPEMA‐ COST ACTION 15215PRAGUE, 23‐24 FEBRUARY, 2017

statistical analysis and modeling

Liliana Tudoreanu ‐ University of Agricultural Sciences and Veterinary Medicine of Bucharest – Faculty of Veterinary Medicine ‐ Department of Food Control

The WG6 outcome is:

Agreed protocol for evaluation of consumers and marketattitudes towards entire males and immunocastrates in at leastattitudes towards entire males and immunocastrates in at least5 Western and 5 Eastern European countries

• ANIMAL WELFARE

Cognitive and emotional and

• BOAR TAINT

• TEXTURAL ATTRIBUTES

EATING EXPERIENCE

Consumer ATTITUDE

on meat and • ANIMAL WELFARE

• FOOD SAFETY

• MARKET CONCERNS

• INFORMATIONS

• ADVERTISMENTS ETC

emotional and volitional processes

Cognitive process: Psychological processes involved in acquisition and understanding of knowledge, formation of beliefs and attitudes, and decision making and problem solving.

Emotional and volitional processes are involved in wanting and intending.

meat and meat

produces from EM and IM male pig

ConsumersDATA

PanelsDATA

IM and EM meat and meat produces:

••BOAR TAINTBOAR TAINT

••TEXTURAL TEXTURAL

DATA from

SENSORY ANALYSIS

HARMONIZED protocols will enable the development of databases linked to robust models which can be

Models

Panel MODELS

Consumers MODELS

••TEXTURAL TEXTURAL ATTRIBUTES / ATTRIBUTES / PARAMETERS PARAMETERS

DATA from INSTRUMENTAL

MEASUREMENTS

models which can be automatically improved when new data are entered to databases. The models can be used for market predictability too.

Models

MODELS

YES!

“A meta‐analysis is a statistical analysis that combines the results of multiple scientific studies.”

Why?

Can we combine data from different studies ?

Why?• To provide a test with more power than separate studies

and more robust point estimate than is possible from the measure derived from any individual study.

• To summarize numerous and inconsistent findings and/or to resolve uncertainty when reports disagree.

• To investigate consistency of effect across different samples.

Meta‐analysis on existing data may resolve uncertainty when

reports disagree

‐ Select keywords for searching databases .

We can share the selected papers by using EndNote in WEB of Science

• Decide the keywords to use for the search

• Collect all titles – EndNote in Web of Science

Discard duplicates

Meta-analysis

• Discard duplicates

First selection – based on title and abstract

Second selection based on full text

• Collecting all PDF files from the selected papers

• Decide on the information to collect to be inserted into the data spreadsheet

Searching the databases – the choice of keywords ‐ what we can expect?

Using a federate searching engine (8 databases) for the following words in papers’ title:

Boar taint & meat & sensory resulted in 321 papers, of which 21 papers in Meat science from 2004 to 2017

Boar taint & meat & texture & consumer ‐ 101 results Boar taint & meat & texture & consumer ‐ 101 results

Boar taint & texture & consumer ‐111 results

Boar taint & consumer – 623 results

Boar taint & panel – 401 results

Pig & entire male & meat & texture – 1,113 results

What kind of data could we look for?

‐ How the selected consumers were choose from the population.‐ Cultural inheritance /traditions /gender/ time of day (etc) as factor in sensory evaluation ‐Methods for collecting the data (rating based, choice based, acceptance, Likert scales, etc) ‐Type of data reported (frequencies, inter‐quartile range, Central ‐Type of data reported (frequencies, inter‐quartile range, Central tendency summarized by median and mode , etc) ‐ Type of software used‐Models reported‐ Evaluation of textural properties? ‐Relationships between sensory and instrumental textural attributes ‐Etc

Data type

Continuous interval scale

More general methods and software, some of them well developed for other sciences

Rank data

choice /preference data

Specialized sensory data analysis methods and

software

Data from instrumental measurements –

continuous numerical

General numerical methods developed for other sciences

(medical science, psychology, etc),

SAS; SPSS; JMP; R; MINITAB; MEDCALC

software

XLSTAT Sensory; CAMO; R; SIMS; TimeSens

SAS; SPSS; JMP; R; MINITAB; MEDCALC

Trained Panel

Product description data ‐Sensory (including

textural) Profile

Consumers • Self explicated tests• Rating based studies• Ranking tests• Choice tests• Hedonic (affective tests)

Market (stakeholders, producers, retailers)

Attitude dataMarket data

Type of data reported: frequencies, inter‐quartile range, central tendency summarized by median

and mode, etc.

Two problems to be considered

Boar taint (odor, taste )

Textural attributes

Sensory panel studies

Samples Panel Experiment Type of data

Statistical Method * and Software

Authors ,

title, journal

Fresh loin (cooked), dry‐cured sausages (Spanishchorizo) and cooked sausages (frankfurter)(Longissimus thoracis et lumborum muscles)

10 assessors (two men and eight women, 24–55 years) with a long experience in sensory profiling in meat and meat products. Panellists ‐ all sensitive to androstenone and skatolein their pure form Panelists had participated in training (45 sessions) for the sensory evaluation of

Sensory analysis ‐ ISO 4121 (2003)Hardness, juiciness and chewiness were evaluated for loin and chorizo sausages.

Concentrations of androstenone,odor, flavor, hardness, juiciness and chewinessAll descriptors ‐ evaluated on an unstructured 10 cm line (0=“not perceivable”; 10=“extremely perceivable”).

Minitab 15.0 software

GLM and PCAusing

M Dolores Garrido, Macarena Egea, M BelénLinares, FrancescBorrisser‐Pairó, BegoñaRubio, Ceferina Viera, Beatriz Martínez , Sensory characteristicsof meat and meat products from entire male pigs.doi: http://dx.10.1016/j.meatsci.2017.02.011

DATA FROM PANEL ANALYSIS

the sensory evaluation of meat and meat products from entire male pigs

i.2017.02.011

72 neck fat samples collected at a commercialslaughterhouse approximately 12 h postmortem without priorsensory screening.

10 assessors, selectedon the basis of their individual olfactory performanceTrained for boar fat evaluation using the MW method for a period of 2 months up to 2.5 yearsPanel calibration with using smell strips

Six testing days over athree week period in the laboratory built according to the ISO 8589:2010 .‐Every assessor evaluated 216 samples in total‐Three different heating methods were used‐6‐point scale from 0 (=no deviationfromstandard fat) to 5 (=very strong deviation fromstandard fat).

Concentrations of androstenone and skatoleFor the sensory evaluation, the average score (scale from 0 to 5) was convertedinto a binary variable using a low and high threshold to decide whether a given sample is standard or deviant from standard, i.e. boar tainted.

R SOFTWARE:‐ R package lme4(Bates et al., 2003).‐ R packagepsych (Revelle, 2015)Logistic mixed modelROC (receiver operating characteristic) curves for assessors and methods

J. Trautmann et al. Boar taint detection: A comparison of three sensory protocols .MeatScience 111 (2016) 92–100http://dx.doi.org/10.1016/j.meatsci.2015.08.01

GLM= general linear modelPCA = principal component analysis

Panel sensory analysis - the need for harmonized protocol and method development

Questions arising from the study by J. Trautmann et al. [Boar taint detection: A comparison of three sensory protocols.

Meat Science 111 (2016) 92–100]http://dx.doi.org/10.1016/j.meatsci.2015.08.011

• Which is the best heating method?

• Which is the best representative sample?

Which is the minimum number of assessors needed to ensure reliable • Which is the minimum number of assessors needed to ensure reliable results for the daily routine analyses carried out in slaughter houses?

• There is a need for sensitivity and specificity analysis taking into account the heating method, no of panel assessors and the concentrations of androstenone and skatole

• The performance of a given method need to be assessed by a combination of methods [such as sensitivity, specificity, ROC‐curves and inter‐rater agreement (ICC) ]

Consumer studies

DATA FROM CONSUMER STUDIES

Samples Type of data Consumers Statistical Method and Software

Author, title , journal

Samples from 80 animals. Females , entire males , surgically castrated , immunocastrated pigs Cooked samples. Loin, (Longissimus thoracismuscle) between the 2nd and 3rd last ribs and proceeding 6 cm cranially

Odor, flavor.Frequency of categoriesof consumer scores (dislike a lot; dislike; like; and like a lot)9 point scale.The hedonic scale was considered as quantitative (from 1 to 9); number 5 was omitted.; the distance between each category of the scale was the same

No: 201 ‐ Spain , one location(51.7% men and 48.3% women) and age (from 18to 77 years) following the Spanish age distribution

SAS software The MIXED procedure Type of animal as fixed effect, tasting session as blocking effect and consumer as random effect.The MIXED procedure used to determine differences of odourand flavour acceptabilitydepending on the androstenonelevel classification. Androstenoneclassification was included as fixed

M. Font i Furnols et al. Consumers’ sensory acceptability of pork from immunocastrated

male pigs / Meat Science 80 (2008) 1013–1018doi:10.1016/j.meatsci.2008.04.018

scale was the sameAndrostenone levels : low(less than 0.5 lg/g), medium (0.5–0.99 lg/g); and high (more than 0.99 lg/g).

classification was included as fixed effect.

Meat and fat samples, loin between the last dorsal and last lumbar vertebrae

Androstenone and skatole levelshedonic (questions Q1–Q4) and intensity evaluations (questions Q5–Q11),, using a 1–9 scoring scale, without 5Scores were merged in 4 groupsFactors: Sensitive and insensitive consumersLow and High levels of skatole and androstenone

No: 140‐ France; two locationsEqually distributedbetween males and females and 27, 17, 17, 24 and 15% of them were≤30, 31–40, 41–50, 51–60 and N60 years of age, respectively)

R SOFTWARE‐differences between meatsample groups ‐non parametric tests. ‐ Differences in mean scores ‐Friedman rank sum test (overall effect withinexperiments) or the Wilcoxon rank sum test (paired comparisons).Differences in distributions of scores ‐Fisher's exact test for count data

M. Bonneau, P. Chevillon . Acceptability of entire male pork with various levels of androstenone and skatole by consumers according to their sensitivity to androstenone.Meat Science 90 (2012) 330–337doi:10.1016/j.meatsci.2011.07.019

The data we have and how to use it

Type of data

Possible results:‐ Identify knowledge gaps…and find new

information ‐ Develop meta‐analysis model(s) for predicting consumer preference/

attitude

Identify the adequate

method and software to work with attitude

‐ Identify region / cultural differences and similarities

etc

work with

http://www.biostathandbook.com/metaanalysis.html

Meta‐analysis software : REVMAN; CMA; STATA, SPSS, METAWIN, EXCEL MIX 2.0

‐ MIX2.0 ‐ https://www.meta‐analysis‐made‐easy.com/about/

QUARTILESQuantileregression (QR) in XLSTAT

• Provide an estimate of conditional quantiles of the dependent variable instead of conditional mean.

• The parameter estimates in QR linear models have the same interpretation as those of any other linear model, as rates of change.

• the coefficients of the QR model can be interpreted as the rate of change of some quantile of the dependent variable distribution per unit change in the value of some regressor.

Quantile regression enables to work with a wide range of distributions, without being subject to any range of distributions, without being subject to any restrictions such as normality assumption, thus contrasting with usual regression.It is possible to mix qualitative and quantitative explanatory variables. It looks ideal for meta‐analysis and for harmonized protocols data reporting.

https://www.xlstat.com/en/solutions/features#collapseOne‐6

PROPORTIONS

The Cochran Armitage trend

Allows to test if a series of proportions can be considered as

Type of data

Method and

Software

Information resulting from the data analysis

PROPORTIONS

XLSTAT software

proportions can be considered as varying linearly with an ordinal or

continuous score variable

https://www.xlstat.com/en/solutions/features#collapseOne‐6

ORDINAL Ordinal logit

Allows to link the cumulative

Type of data

Information resulting from the data analysis

Method and

Software

ORDINAL VARIABLES (SURVAYS

DATA)

Ordinal logitmodel

(XLSTAT)

Allows to link the cumulative probability of a level to explanatory variables.

https://www.xlstat.com/en/solutions/features#collapseOne‐6

In order to usefully combine data from different published papers (meta‐analysis) and understand the influence of the parameters we choose, we need to clearly define dependent variable and independent variables ‐ more information are needed!variables ‐ more information are needed!

A spreadsheet containing all information need to be discussed.

Testing a protocol using TimeSenssoftware

(developed by INRA‐ France)

Working across

http://www.timesens.com/about.aspx?lang=fr

across EUROPE using the

same set of samples –testing an

agreed protocol

Some questions to address

Should we consider just tenderness and/or juiciness or should we include other textural attributes ? Is there a consensus on the definition/method for tenderness and juiciness? Which preparation methods should we consider for the agreed protocol ?the agreed protocol ? Which kind of sample type (minced meat / slice; which cut) should it be used? Other issues?

Some questions to address for the instrumental measurements that can be linked to sensory data

• What instrumental data should we use to link to sensory data?

• Tenderness:

How should we consider it to be measured by: share force; fracture force; hardness during compression; puncture force; etc? What about compression speed and length?fracture force; hardness during compression; puncture force; etc? What about compression speed and length?

• Juiciness?

• Which should be the recommendable (agreed?) methods for instrumental tenderness and juiciness measurements (compression/Vis‐NIR spectroscopy air flow method)?

(Long, Y., Tang, X., Wang, W., Peng, Y., Dong, X., Kang, X. and Li, Y. (2017), A unique method for detecting beef tenderness based on viscoelasticity principle. J Texture Stud. Accepted Author Manuscript. doi:10.1111/jtxs.12258 – we do not know how it works on pork meat)