Master’s thesis

Álvaro Suárez Jiménez – xwq109

High Field 1H-NMR analysis of pig subcutaneous fat layers

Supervisor: Anders H. Karlsson

Co-supervisors: Flemming H. Larsen, Jorge Ruiz

Carrascal and Mette Christensen (Carometec a/s)

Faculty of Science – Department of Food Sciences

University of Copenhagen

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TABLE OF CONTENT

1. Introduction Page 4

2. Theory background Page 5

2.1. Fatty acid profile and meat quality Page 5

2.2. Importance of rapid methods in the industry Page 8

2.3. Lipid extraction using balls mill method Page 10

2.4. HF-1H-NMR for fatty acid study Page 11

2.5. Iodine value Page 12

2.5.1. GC-FAME Page 13

2.5.2. NITFOM Page 14

3. Materials & methods Page 16

3.1. Animals and sample preparation Page 16

3.2. HF-1H-NMR Page 17

3.3. Fat extraction Page 18

3.4. Fatty acid composition (FAME) Page 18

3.5. Iodine value Page 20

3.6. Data analysis Page 20

4. Results & discussion Page 20

4.1. Gas chromatography analysis Page 21

4.2. HF-1H-NMR analysis Page 25

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5. Conclusion Page 31

6. Acknowledgements Page 31

7. Literature Page 32

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1. INTRODUCTION

The aim of the study was evaluate the importance of the different

subcutaneous fat layers for iodine value calculation on Danish pigs. A

previous study by the company CAROMETEC where the iodine value was

calculated through the subcutaneous fat without layer differentiation was used

as reference material. The analysis was performed using GC-MS and

HF-1H-NMR and different data analysis tools were used to evaluate the

calculation established. A clear differentiation between fatty acids composition

between layers was detected where bigger amount of unsaturated fatty acids

was found in the external subcutaneous fat layer and more saturated in the

internal layer. More accurate calculations for iodine value were developed

using the information from the inner fat layer.

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2. THEORY BACKGROUND

2.1. Fatty acid profile and meat quality

Meat quality is defined as a combination of properties, including

technological quality attributes, consumer acceptance, and credence

characteristics of safety and health, as well as more intangible features such

as the cleaning, green or welfare status of the production system (Lee et. Al.

2014). The consumer increasingly prefers products with a higher unsaturated

fatty acid composition because of their beneficial effects in preventing

cardiovascular diseases. Pig diets supplemented with vegetable oils such as

soybean oil, sunflower oil, and corn oil, contain a high percentage of

unsaturated fatty acids and should lead to healthy products for consumers.

(Mitchaothai et. Al. 2007).

In meat and in related products the total fatty acid content strongly

influences the physical-chemical characteristics of foods, such as elasticity,

texture, mouthfeel, juiciness and lubricity (Siliciano et. Al. 2013).

Diet influenced growth rate and fatness, the low protein diet slowing

growth and producing fatter meat, more so in the two modern breeds, and

particularly in intramuscular rather than subcutaneous fat. This diet produced

more tender and juicy meat, although pork flavour and flavour liking were

reduced (Wood et. Al. 2004). In non-ruminants, the fatty acid pattern of dietary

lipids is reflected in the fatty acid composition of tissues (Mitchaothai et. Al.

2007).

The major public health institutes and different authorities,

independently, recommend a daily balanced proportion of saturated,

monounsaturated, and polyunsaturated fatty acids in the diet for a correct

nutrition and a healthy lifestyle. In western industrialized countries the current

indications for lipid intake have raised the question of the nature of fatty acid

effects on human health (Siliciano et. Al. 2013). Although beneficial health

effects of an increased intake of polyunsaturated fatty acids was noted, it is

still not definitively assessed, and negative effects of n-6 PUFA have neither

 6  

been established (Siliciano et. Al. 2013). The diet with high polyunsaturated

fatty acids resulted in higher levels of polyunsaturated fatty acids in back fat

and more rancid loin and sausage products after one and eight months of

freezer storage compared to those from the low PUFA diet (Bryhni et. Al.

2002). This trend of increasing unsaturated fats on meat products involve

several negative effects on meat and carcass quality, such as soft adipose

tissue, difficult slicing, higher susceptibility to lipid oxidation with the

generation of toxic reactive compounds (Martin et. Al. 2007).

Differences between intramuscular and subcutaneous fat has been

studied to be able to relate fat quality of both tissues. The positive effect of

age on intra muscular fat cannot be transpose to subcutaneous where a

negative trend has been found in experiments in which pigs reached heavy

weights (Bosch et. Al. 2012). It has been also found differences in the fatty

acid composition of the outer and inner subcutaneous back fat layers from

selected pigs showing that the outer layer is more unsaturated than the inner

layer (Daza et. Al. 2007).

Total saturated fatty acids decreased from inside to outside the back-

fat, being higher in the inner and showing the lowest proportion in the outer

one. Total monounsaturated fatty acids decreased from outside to inside, the

highest proportion being that of the outer layer, and the trend for total

polyunsaturated fatty acids was similar to that of monounsaturated fatty acids,

higher levels outside and lower inside (Daza et. Al. 2007).

Overall, significant differences in the fatty acid profile of the three studied

subcutaneous fat layers were reported, with a general trend to a higher

unsaturation when the layer is closer to the animal outer surface. Such

variations could be partially due to a different metabolism in each layer, aimed

to keep the fat fluid at ambient temperature (unsaturated fats have lower

melting temperature than saturated fats) (Daza et. Al. 2007). Fat sources with

high proportions of polyunsaturated fatty acids or monounsaturated fatty acids

are commonly used or at least have been tested for swine feeding. These

feeding strategies allow a substantial modification of the fatty acid profile of

pork meat and meat products, leading to more unsaturated fats, which follows

 7  

health advice about the decrease in the consumption of saturated fats due to

their implication in cardiovascular diseases (Martin et. Al. 2007).

Nevertheless, it seems that the outer layer is the first one deposited, followed

by the inner, and thus, variations in the fat content and the fatty acid

composition of the feeding sources during the different phases of the rearing

system could be also implied in the overall fatty acid profile of each layer

(Daza et. Al. 2007).

The increase in saturated fatty acids and the decrease in

monounsaturated fatty acids and polyunsaturated fatty acids contents of

subcutaneous adipose tissue of conjugated linoleic acid fed pigs have been

also found. The inhibition of the ∆9 desaturase by conjugated linoleic acid has

been suggested as the main reason explaining the modifications in total

saturated fatty acids and monounsaturated fatty acids contents obtained in

most of the studies. On the other hand, the inhibitory effect of dietary

conjugated linoleic acids on other desaturase activities could be also the

reason explaining the observed decrease in some polyunsaturated fatty acids

contents (Martin et. Al. 2007).

Monounsaturated fatty acids supplementation significantly affected the

contents of total saturated fatty acids and monounsaturated fatty acids of

subcutaneous adipose tissue. Thus, the back fat from pigs fed the high-

monounsaturated fatty acids experimental diets reached the highest

proportions of monounsaturated fatty acids and the lowest of saturated fatty

acids (40.35% of monounsaturated fatty acids and 39.04% of saturated fatty

acids, average value regardless of conjugated linoleic acids level), whereas

those animals fed the low monounsaturated fatty acids treatments showed the

lowest monounsaturated fatty acids and the highest saturated fatty acids

contents (37.74% of monounsaturated fatty acids and 41.37% of saturated

fatty acids, average value regardless of conjugated linoleic acids level) (Martin

et. Al. 2007).

The inclusion of oils with a high proportion of C18:2 in the feeding

increases the concentration of C18:2 in the tissues and reduces the

concentration of endogenous synthesized fatty acids (monounsaturated fatty

 8  

acids and saturated fatty acids), whereas the use of a high C18:1 content

feeding enhances the proportion of C18:1 in pig tissues, decreasing the

amount of saturated fatty acids, although polyunsaturated fatty acids content

is not significantly modified (Ruiz and Lopez-Bote, improvement dry cured

ham). According to Bryhni et. Al. (2002) it is emphasize the importance of

controlling the PUFA content in feed to avoid problems during storage and

processing.

As shown for the number of studies focus on this topic, the relation

between feeding and the fatty acid profile presented on the meat product is

increasing its importance. The rising significance of healthy products for

consumers is motivating bigger efforts to offer more beneficial meat for health

on the market.

2.2. Importance of new rapid methods in the meat

industry

One of the important quality parameters in porcine carcass grading for

determining farmer payment and carcass sorting before splitting and cutting is

the quality of the fat in the porcine carcass, as it is important for many

parameters of meat quality (Viereck, Sørensen and Engelsen, 2012). Classic

analysis methods for fat analysis have big reactive expenses and they are

time-consuming.

Another important fact when speaking about analysis of raw materials in

the meat sector is the big challenge due to huge range of variability of the

incoming meat. This high variability turns into high variability in quality of the

products and smaller control of the processes on the production lines and final

products. Thus, analysis methods for using on the meat sector needs to be

able to cover this huge variability.

The classic method for analysis of fatty acid profile of food is gas

chromatography. This method shows good accuracy but it requires extensive

sampling and is time-consuming (Dalitz et. Al. 2012). Hence, new methods

 9  

have been investigated to find a proper method which could be used at on-

line process. The high speed of processes on the meat industries (up to 1200

carcasses per hour) makes spectroscopy methods the most factible technique

for this aim.

According to Prieto et. Al. (2009) visible and near infrared reflectance

spectroscopy (Vis-NIR) has the potential of predicting quickly and accurately

different attributes of meat quality and it is suitable for on-line use and for

simultaneous determination of different traits. Because of these advantages,

the technology is being broadly used by the industry research-base for large-

scale meat quality evaluation to predict the chemical composition of meat

(Prieto et. Al. 2009). The main inconvenience for spectroscopy methods is the

need of very robust models capable of covering the big range of variability of

raw materials analyzed. Some characteristics, which could be studied with

rapid methods, are important criteria that affect consumers’ evaluation of meat

quality. Hence, there is an urgent need to find a fast and efficient alternative

method to estimate these criteria (Prieto et. Al. 2009).

Focusing on NMR methods, low field NMR has been displayed as a

appropriate method for water/moisture analysis on fat (Todt et. Al. 2006).

Thus, fraud by addition of water could be found by rapid NMR analysis. 1H

high resolution NMR has also given some interesting results in meat

authentication and determination of geographical origin. Common adulteration

practices consist both in undeclared mixing of meat from different species and

in mixing of expensive with cheaper meat (Mannina et. Al. 2012).

Regarding iodine value, an early prediction enables the slaughter

industry to quantify fat quality and thereby use the information for product

sorting resulting in increased production efficiency and economic gain.

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2.3. Lipid extraction using balls milI method

One of the most time consuming steps on the fat analysis methods is the

lipid extraction. Efforts to achieve a method is present in some studies (Perez-

Palacios et. Al.2008).

According to Perez-Palacios et. Al. (2008), chemical and physical

treatments used for lipid extraction must remove them from their binding sites

with cell membranes, lipoproteins and glycolipids. It has been demonstrated

that the use of different methods results in different lipid recoveries in

biological samples. Indeed, results varied widely due to differences in

extraction methodology.

The standard methodology has been for decades solid–liquid extraction

procedures. These are based on a solvent (hydrophilic or hydrophobic, acidic,

neutral or basic) added to a solid. Insoluble material can be separated by

gravity or vacuum filtration, and soluble material is ‘extracted’ into the solvent

(Segura & Lopez-Bote 2014). The search for new and accurate lipid

extractions methods in meat and meat products is a very demanded topic. In

fact, depending on the tissue source, multiple groups adapted the

conventional protocols or developed new ones (Segura & Lopez-Bote 2014).

Although pure lipids are soluble in a wide range of organic solvents, the

model solvent or solvent mixture for extracting lipids should be polar enough

to remove such lipids from their association with cell membranes and tissue

constituents, but also not so polar that the solvent does not readily dissolve all

triacylglycerol molecules and other nonpolar lipids and, of course, should not

react chemically with the extract (Segura & Lopez-Bote 2014).

It has been shown that the balls mill protocol (which it was the extraction

method on this study) allows the analyst to treat a large number of samples in

a shorter time than the classic FOL method. Attending to fat content, the balls

mill mehotd offers analogous results to FOL method in quantity, but it shows

lower variability. In case that the fatty acid profile was the pursued goal, OS

extraction (where extract and methylate fatty acids is done in a Toluene-

Methanol/HCl solution in a rapid one-step procedure) showed a similar speed

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than balls mill method but results obtained with balls mill are closest to those

obtained with the classic FOL procedure. (Segura & Lopez-Bote 2014)

2.4. HF-1H-NMR for fatty acid study

The perfect process analytical method would be based on a robust, non-

invasive and easy to handle customized technique operating in real-time. The

ideal instrumentation comes without any need for calibration (absolute

method), it has a professional support, and it is compliant to increasing

regulatory requirements. At least for NMR a trend can be observed towards

such an all-in-one device suitable for every purpose (Dalitz et. Al. 2012).

High-resolution 1H-NMR has been proposed as a fast and accurate

technique suitable for the analysis of edible oils and fats. This spectroscopic

method, often accompanied by the application of very simple mathematical

systems of calculation, has successfully been employed for the analysis of

acyl chain composition in olive oil and other edible vegetable oils, providing

huge information about their use in human nutrition. High-resolution NMR

spectroscopy, complementary to chemometric analysis, has been used as a

rapid quality control and authentication tool (Siliciano et. Al. 2013).

Quantitative high-resolution on-line NMR spectroscopy can be applied to

the investigation of complex fluid mixtures containing analytically similar

compounds. The development of on-line (flow) techniques has tremendously

increased the value of NMR spectroscopy as a non-invasive method for

process development applications (Dalitz et. Al. 2012). The application of this

kind of spectroscopy to the analysis of animal fats is restricted. At this time,

and at the best of our knowledge, meat and meat products apparently seem

to be seldom explored (Siliciano et. Al. 2013). However, the usefulness of 1H

NMR spectroscopy has been increasingly recognized for its noninvasiveness,

rapidity and sensitivity to a wide range of compounds in one single

measurement (Christophoridou and Dais 2009).

It is possible to settle that the routine application of high-resolution 1H

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NMR techniques in the screening of the quantitative composition of fatty acid

chains is a useful addition to the methods for the analysis of the lipid fractions

of meat products. The short time required for sample preparation and spectra

recording can allow the analysis of a larger number of matrices per day. High-

resolution 1H-NMR spectroscopy could be used as a powerful tool in

alternative, to the classical chromatographic methods for the determination of

fatty acid chain compositions in meat products (Siliciano et. Al. 2013). In those

cases where liquid state is necessary, deuterated chloroform is an appropriate

chemical compound. On the deuterated chloroform the H from the chloroform

molecules is replaced by a deuterium isotope. This avoid the signal from the

H on the chloroform improving the received signal.

As described by Viereck, Sørensen and Engelsen (2012), high

resolution NMR has been demonstrated as suitable for subcutaneous fat layer

differentiation directly on fat tissue. The PLS model developed on Viereck,

Sørensen and Engelsen (2012) study could to some extend predict iodine

value with a R2 of 0.71 and a prediction error RMSECV of 2.11 using 5PCs.

The possibility of improving this prediction using liquid state samples was

developed in the present research.

2.5. Iodine value

Iodine value is a measure of the average amount of unsaturation of fats

and oils and is expressed in terms of the number of centigrams of iodine

absorbed per gram of sample (% iodine absorbed). The iodine value can be

determined using a traditional titration, or by gas chromatographic

quantification, however, both methods are quite time consuming. Hence,

spectroscopic techniques have been shown to be useful as faster methods

(Viereck, Sørensen and Engelsen, 2012).

Iodine value is used as a quality indicator for several parameters on the

meat industry. The range of information related to the iodine value is width,

from Information about rearing system of the animals to information to choose

the best technological use of the raw material. In the other hand, it is

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important to consider that iodine value is only an indicator of number of

unsaturations, but it does not give any information about the kind mollecules

where the double bonds are presented.

The amount of unsaturation of the constituent fatty acids has been

measured by the iodine value, which is currently determined by the Wijs

method. Major drawbacks of that method include the use of dangerous iodine

dichloride (Wijs reagent) and the time-consuming procedures for reagent

preparation and chemical analysis. A procedure to determine the iodine value

from the fatty acid composition has been proposed by an American Oil

Chemists’ Society (AOCS) method (Kyriakidis and Katsiloulis 2000).

Accordingly, the proposed procedure for the calculation of the iodine value

from the percentages of fatty acid methyl esters (FAME) by and coefficients

specific for every vegetable oil can be used successfully for the determination

of iodine value determination (Kyriakidis and Katsiloulis 2000).

2.5.1. GC-FAME for iodine value calculation:

Iodine values were calculated through the method use for the American

Oil Chemists’ Society. Gas chromatography analysis of the fatty acid methyl

esters is performed on the samples and once the concentration of individual

fatty acids is known, through calculation of the following formula, iodine value

is expressed:

Iodine value = (% 16:1 × 0.950) + (% 18:1 × 0.860) + (% 18:2 × 1.732) +

(% 18:3 × 2.616) + (% 20:1 × 0.785) + (% 22:1 × 0.723)

This method is a good improvement regarding the Wijs method, but it is

still quite time-consuming and it is not possible to use it in a normal meat

industry line speed. Anyhow, it is the most appropriate method for calibration

of spectroscopy techniques.

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2.5.2. NITFOM:

NitFom is an invasive handheld measuring device, which predicts the

iodine value and fatty acid profile of pork fat with a measurement cycle of 3

seconds.

This technique has been developed by the company CAROMETEC in

co-operation with the Danish National Advance Technology Foundation and

the University of Copenhagen (Faculty of Science, Department of Food

Science). This system uses Near-Infrared-Transmission spectroscopy in

combination with chemometric modeling for predicting several fat

characteristics as iodine value, or melting point.

 

Figure  1.  NITFOM  equipment  (From  CAROMETEC)

With a short measurement cycle (3 seconds), this equipment fits perfect

on the high speed production lines common on the meat industry. The

NITFOM can be used to get information pre-measurement and post-

measurement. Thereby, feed regimen of the animal can be known, as product

quality or slicing yield optimization. The iodine value is calculated with a

precision of ± 2.0 on cold carcass.

The use companies are giving to the equipment is wide depending on

the country. While in USA the companies are more interesting on get

information pre-measuremente about the feed regimen of the animal

slaughtered, on Germany the method is being used to get more complete

information about the requirements for the subsequently cooking hams.

The measurement (1100-2200 nm) is performed on the fat tissue in the

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neck region where the probe penetrates 4 cm into tissue. The speed with

which the NitFom probe is retracted from the carcass determines how many

spectra are obtained (8 measurements on 3 seconds).

 

Figure  2.  Applied  spectroscopy  of  the  NITFOM  equipment  (From  CAROMETEC)

Model is built using PLS and using as reference values a GC analysis

performed by the Danish Meat Research Institute. On hot carcass

classification R2cv was 0.94 with a RMSECV = 1.8 IV, while on cold carcass

R2cv was 0.93 and RMSECV = 1.8 IV.

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3. MATERIALS & METHODS

3.1. Animals and sample preparation

A total of 90 samples of subcutaneous fat from pig, from skin to meat

presented were obtained from a local slaughterhouse by the company

CAROMETEC. The animals were chosen among normal Danish slaughter

pigs breed including extreme samples regarding their iodine values (from

approx. 60 to 80). Pigs were slaughtered on March 26th 2014. From the

company CAROMETEC we got estimated iodine values measured on 49 of

these samples through two different methods described previously (GC-FAME

and NITFOM). These iodine values were calculated based on the whole fat

piece on both methods without layer differentiation (both fat layers from the

samples were melted together before analysis). Measurements with the

NITFOM-instrument were performed on cold carcasses (after one night

storage at a temperature around 6 ºC). Sample size was approx. 5x2 cm

times 6 cm deep and were numbered from 10 to 99.

The samples were kept on -18 ºC until the analysis was performed.

Samples were divided manually in frozen state with a knife into 5 different

layers. These layers are from outside to inside the animal:

-­‐ Skin

-­‐ Upper or external subcutaneous fat layer

-­‐ Link between both subcutaneous fat layers

-­‐ Lower or internal subcutaneous fat layer

-­‐ Muscle layer

The differentiation of the 4 upper layers were not completely clear in

some of the samples due to the irregular shapes of the samples or the non

parallel line of the link between fat layers close to the skin. Besides, several

samples show brown dots on its external fat layer (Figure 3), probably due to

some kind of skin penetrations on the external fat layer, but as it will be shown

later, it did not affects the results on the fatty acid analysis. Skin, connective

tissue between both layers and muscle were discarded, while the two different

fat layers were selected for the upcoming experiment. They were named U for

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the external layer, and D for the internal layer, plus the previous mentioned

numbers of the sample. Thereby, in total, 98 samples were obtained.

 Figure  3.  Sample  U26  before  homogenization.

Subsequently, after separating the two layers, each layer was

homogenized. For this homogenization, samples were frozen in liquid nitrogen

and minced using a Moulinex . After mincing, the samples were frozen and

stored for further analysis at -18 ºC. Thereby, 98 samples where 49 belong to

the external fat layer and 49 to the internal fat layer. These samples were later

analyzed by GC-MS and HF-NMR methods.

3.2. HF-1H-NMR analysis

To ensure an appropriate signal on the future NMR analysis, the biggest

amount of fat dissolved without presenting precipitation was chosen. A test to

calibrate this optimal dissolution of fat in CHCl3 was carried out using 600 µL

of chloroform as solvent and different amounts of fat for testing. Optimal

amount was selected as 50 mg. The samples were Vortex mixed (30

seconds) and centrifuged afterwards (10 minutes at 5000 rpm). For the HF-1H-NMR experiment 250 µL of the dissolution previously created was mixed

with 250 µL of deuterated chloroform.

Spectra of the 98 samples were collected using a Bruker Avance DRX-

500 (Bruker BioSpin, Theinstetten, Germany) spectrometer, operating at a

frequency of 500.13 MHz for protons equipped with a double-tuned BBI

probe. As described before, 50% of CDCl3 was added as lock solvent.

Diameter of sample tube was 5 mm. Data were recorded at 298 K. Other

recording parameters include 32k data points, 64 scans, recycle delay of 5 s,

a spectral width of 10000 Hz and an acquisition time of 1.639 s. Spectra was

aligned according to the (CH2)n resonance (approximately 1.25 ppm) of the

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fatty acids. After alignment only the spectral region 0.5-6.0 ppm was used for

further analysis.

3.3. Fat extraction

The fat extraction for the GC-MS analysis was performed using the

method described by Segura and Lopez-Bote (2014). In a 2 mL safe-lock

micro test tube, 200 mg of the homogenized fat sample was accurately

weighed. Four steel balls (2 mm Ø) and 1.5 mL of CHCl3:MeOH 8:2 mixture

was added. After being tightly capped, the tubes were placed in an adapters

and was homogenized for 2 min at 20 Hz in a Mill MM400 mixer (Retsch

technology, Haan, Germany). The resulted biphasic system was allowed to

separate by centrifugation (8 min, 10,000 rpm, 25ºC). The solvent was

decanted into a previously weighted 4 mL vial and thereafter stored in an

freezer at -4 ºC.

3.4. Fatty acid composition (FAME)

The fatty acid composition was determined according to Jart (1997) with

some modifications: 100 µL of extraction solvent were transferred to a 10 mL

test tube. Solvent was evaporated under nitrogen stream at 25 ºC. 1.00 mL

sodium-methylate solution was added, followed by a Vortex mix for 30

seconds. The test tubes were placed in a 60 ºC water bath until the samples

were clear (after this the lipid phase was not visible anymore). The samples

were kept in the water bath for an additional 30 minutes. When samples were

removed from the bath 4.00 mL of saturated sodium chloride was added and

1.00 mL of hexane, and thereafter it was Vortex mix for 30 seconds. The

samples were left until the phases have separated. The top phase is the

hexane and contains the methylated fatty acids, the samples were evaporated

and dissolved in 1.0 mL of hexane prior to analysis on the gas chromatograph

(HP 6890 series, Hewlett-Packard, Palo Alto, CA, USA) with a 30 m x 0.32

mm x 0.25 µm Omegawax column (Supelco, Bellefonte, USA) and with FID

detection. The oven temperature programmed and conditions were: 50 ºC for

1 minute; from 50 to 180 at 15 ºC/minute; from 180 to 240 at 3 ºC/minute; at

240 ºC, held for 10 minutes. Injected volume was 1 µL and the split ratio was

1:25. Hydrogen was used as carrier gas and the flow was 1 mL/minute. The

 19  

results were analyzed with Chemstation software (Agilent Technologies) and

the fatty acid methyl esters were identified by comparing retentions times with

known standards. Fatty acid presented in most of the samples were:

· Myristic acid à CH3(CH2)12COOH à C14:0

· Palmitic acid à CH3(CH2)14COOH à C16:0

· Palmitoleic acid à CH3(CH2)5CH=CH(CH2)7COOH à C16:1cis-Δ9

· Stearic acid à CH3(CH2)16COOH à C18:0

· Oleic acid à CH3(CH2)7CH=CH(CH2)7COOH à C18:1cis-Δ9

· Linoleic acid à CH3(CH2)4CH=CHCH2CH=CH(CH2)7COOH à à C18:2cis-Δ9, Δ12

· α-Linolenic acid à CH3CH2CH=CHCH2CH=CHCH2CH=CH(CH2)7COOH à

à C18:3cis-Δ9, Δ12, Δ15

The results were reported as % fatty acid of the total content of detected

fatty acids. Duplicates for the fatty acid analysis through GC were carried out

performing the methylation and analysis on the gas chromatographer two

times.

Results from the GC-MS on paper transferred manually to a computer.

As it is shown on Figure 4, the results from the GC-MS shows some ambiguity

close to the retention time belonging to C16:0. Due to similar reasons several

samples were not completely clear on the transcription. However, the most

consistent possibility was chose supporting the decisions with the literature.

 Figure  4.  GC  results  2nd  replicate  of  sample  U58

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3.5. Iodine value

Two calculated iodine values given by CAROMETEC were used on the

data analysis (one calculated through GC-FAME analysis and the other value

calculated through the NITFOM), and a third iodine value was calculated

based on the formula from the AOCS method for iodine value calculation

using GC-FAME.

3.6. Data analysis

Multivariate data analysis for GC-MS and HF-1H-NMR was performed in

the form of principal component analysis (PCA) and partial least squares

regression (PLS) to obtain optimal quantitative and qualitative information

from the measured experimental spectra. Different pre-processing techniques

were necessary for both methods. 18028 data points were resulted for

modeling of the HF-1H-NMR analysis. All models were validated using Full

cross-validation, from which the root mean square error of cross-validation

(RMSECV) was calculated to measure prediction error. Data was analyzed

using Microsoft Excel and the chemometric software LatentiX 2.12

(www.latentix.com, Latent5, Copenhagen, Denmark).

4. RESULTS & DISCUSSION

As first step, the average value of the fatty acid composition based on

the two replicates was calculated for each of the 98 samples. From here on,

the rest of the analyses were based on this average.

It is remarkable the non detection of some FA in several samples:

Oleic acid (16:1): Not detected in samples U14 and U99.

Linoleic acid (18:2): Not detected in sample U51.

Alfa-linoleic acid (18:3): Presented in 77.5% of the samples.

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4.1. Gas Chromatography analysis

Table 1 shows simple statistical analyses of the results obtain from the

GC-MS based on the average of both replicates. Coefficient of variation (CV)

shown for both layers, could have as reason the differences on fatty acid

composition between layers, nonetheless the CV in each layer is a good

explanation of the precision achieve on the study. As it is presented the

internal fat layer achieve better results than the external and the reason

explaining this fact is not only regarding the probability of small errors on the

analysis. Regarding Daza et. Al. (2007) the external layer is the first one were

the new fat is deposited, followed by the internal one. The older presence of

fatty acid in the body could explain the bigger stability of the compounds

presented, thus increasing the variability of the composition in the external

layer. Anyway, excessive coefficients of variation are shown on fatty acids

16:1 and 18:0 of the external layer as in 18:3 of both layers. The high CV

value on 18:3 could be explain as well due to the small amount of this fatty

acid on the samples where is on average 0.93%.

Table  1.  Content  of  fatty  acid  on  the  study  where  upper  is  external  and  lower  internal  fat  layer  

The iodine values provided by CAROMETEC were calculated through

two different methods: GC-FAME and NITFOM. As it is shown on Figure 5a

the correlation between both methods was really good (R2 = 0,95355). The R2

between the iodine value calculated and the iodine value receive from

CAROMETEC was 0.84 (Figure 5b).

 22  

 

Figure  5a.  Correlation  between  iodine  value  calculated  with  GC-­‐FAME  and  iodine  value  calculated  using  NITFOM

Figure  5b.  Correlation  between  iodine  value  calculated  and  Iodine  value  GC-­‐FAME  from  CAROMETEC

The first step on the data analysis was choosing the best pre-processing

technique for GC data. As the GC peaks were discrete variables with large

variation between them regarding fatty acid levels autoscaling was warranted

as the best option. Afterwards, a preliminary data analysis using PCA with the

98 samples and the 7 fatty acid concentration was performed. Samples U14

and U51 were detected as outliers through the residual vs. T2 plot. A deeper

observation of these two samples on the original data reveal a clear reason

for this fact. Both layer have an empty value on their fatty acids (16:1 for U14

and 18:2 for U51), thus, and keeping this fact in mind samples were not

removed.

 23  

 Figure   6.   Scores   and   loading   from   PCA   with   98   samples   (Yellow   samples   belongs   to   internal  subcutaneous  fat  layer)  and  7  fatty  acid  concentration  as  variables.  

Figure 6 shows clear differentiation between external and internal layer.

As it is known from the literature, the external layer on the subcutaneous fat

has a bigger amount of unsaturated fatty acids. The closer distance with the

environment (colder than the inside of the body) does mandatory the bigger

amount of unsaturated carbons presented on the fat to ensure its fluidity. In

the other hand the fat on the internal layer is always on a higher temperature

(closer to normal body temperature) so the presence of unsaturated fatty

acids is not necessary. As it is clear on Figure 6 PC#2 explain almost the

variability between layers. Its negative values are related to the internal layer

and saturated fatty acids as 18:0 and 16:0 whenever its positive values are

related to the external layer and unsaturated fatty acids. The internal layer is

correlated mainly to 18:0 and the external layer correlated to 16:1.

After this PCA analysis, a PLS was calculated using the iodine values

from the NITFOM as Y on the model. From the three different iodine values,

NITFOM was used for calculation of the PLS model, as NITFOM is less

correlated to fatty acids concentration, than the other two iodine values, which

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

-1

-0.5

0

0.5

1

C18:1

C16:0

C14:0

C16:1

Loadings PC#1 (48.473%)

C18:0

C18:2

C18:3

Load

ings

PC#

2 (2

2.60

6%)

-4 -3 -2 -1 0 1 2 3 4 5

-3

-2

-1

0

1

2

3

4

U51

U61 U87

U75

U99

U55

U66

U81

U85

U83

D83 U91

U74 U86

U95

U68

D61 U92 U73 U93

D81 D86 D91 D75

D51

U90

U59

D87

U89

D59

U42

U38

U58

D85

D99

D55

U43

D73

U36 U31

PCA Scores and Loadings [Model 8]

Scores PC#1 (48.473%)

D74

U49 U80 U47

U35

D68

U33

D93 D58

D66

D89

U40 U29 D90 D92

D95

U17

U12

U22

U19

D80 D43

U16

U39

U26 U32

U44

D49 D47 D40

U25

D42 U14

U28 U34

D38 D25

D26 U10 D31

D44

D28 D14

U37

D29 D19 D33

D12

D36

D10

D35 D17 D16 D39

D22 D34

D32

D37

Scor

es P

C#2

(22.

606%

)

 24  

are calculated from the fatty acids concentration. Scores plot shows similar

results as the PCA calculated before, with clear differentiation between layers

(Not shown).

Figure 7 shows the NITFOM placed on the loadings close to C18:2 and

C18:3 dots as it is expected from the theory. However, it is important to

observe position of oleic acid 18:1 placed away from the NITFOM dot, which

means that the values are not positively correlated. As it was shown in the

theory, C18:1 and NITFOM should be positive correlated due to iodine value

is a calculation depending on the number of double bonds in fatty acids.

However, the position of C18:1 on the loadings plot could be explained by the

fact that all the fatty acids concentration are strongly correlated between

themselves, as they are explained on percentage. Therefore, what is

explained on the loadings plot is the fact which for the same iodine value the

increase on C18:1 concentration will decrease the concentration of C18:2.

Thus, due to the positive correlation between iodine value with both fatty acids

is much stronger on C18:2 than on C18:1, this situation induces the negative

correlation between C18:1 and iodine value, and it locates oleic acid far from

both, linoleic acid and iodine value. This positioning of C18:1 regarding C18:2

and iodine value is always repeated for the different models calculated and,

for the different iodine values used on the calculation.

 Figure  7.  PLS  loadings  using  both  layers  for  calculation.  

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

C18:2

Iodtal (NitFom)

C18:3

C18:0

C16:1

Loadings PC#1 (46.776%)

PLS Loadings [Model 9]

C14:0

C18:1

C16:0 Load

ings

PC#

2 (2

2.70

8%)

 25  

Afterwards, different models were calculated in order to find the best

model for iodine value calculation. Thus, 6 models were performed as shown

as follow:

Table  2.  Comparative  between  PLS  models  calculated  using  GC-­‐MS  data.  

 

Table 2 shows the 6 PLS model calculated and their root mean square

error cross-validated and coefficient of determination respectively. As we can

see the model calculated using the internal layer is for both iodine values

better than the model using both layers and only external. Thus, the internal

layer seems to be better on the calculation of iodine value through the FA

composition. The variation between the models using the iodine value GC-

FAME calculated and the models using the iodine value calculated through

the NITFOM is explained by the correlation between both methods of

calculation (R2 = 0,9535). As the iodine value calculated with the NITFOM is a

prediction itself, models using the iodine value calculated with the GC-FAME

are more robust from a theoretical point of view. The fact that better models

are calculated when using the NITFOM could be explained accepting that the

NITFOM prediction removes some variables on its calculation.

4.2. HF-1H-NMR analysis

On a first view of the spectra resulted of the HF-NMR analysis it is

possible to easily identify different functional groups from the fat samples. The

relation between Table 3 and Figure 8 is clearly observable.

 26  

Table  3.  Situation  of  the  peak  and  functional  group  related  on  HF-­‐NMR  spectroscopy  

 

Figure  8.  Raw  data  obtain  from  all  the  HF-­‐NMR  samples  analysis.

The peaks on 4,10; 4,31 and 5,24 ppm are related to the glycerol

molecule from the fat.

Mean centering was the preprocessing chosen for NMR data. A PCA

was carried out. On Figure 9 is observed a quiet clear differentiation between

layers along PC#2 even when some samples are not following this rule.

These samples will be carefully observed on the incoming PLS models. PC#1

and PC#2 explain 88% of the total variance.

0123456-1

0

1

2

3

4

5

6

7

8

9x 106

ppm

 27  

 

Figure  9.  Score  plot  for  PC1  and  PC2  from  the  NMR  results

As explained before, the external layer has a bigger content on

unsaturated fatty acids regarding the internal. Differences between samples

from external layer within samples from the internal layer are visible on the

raw spectra as well.

On Figure 10 it is noticeable the intensity differences between both

spectra. The peak representative for the functional group (CH2)n (around 1.30

ppm) is stronger on the sample from the internal layer sample (D34 green

color), whereas on the peak for CH=CH-CH2 (around 2.04 ppm), the stronger

signal comes from the external layer sample (U34 blue color).

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2

x 107

-1

-0.5

0

0.5

1

1.5

x 107

D42

U87

D40

U12

U74

U43

D59

U39

U51

D86

U29

D87

D35

U38

D10 D49

U93

U25

D36

U90

U92

U22 D26 D39

U91

U33

U28 D44 U83

D34 D81

U40 D29

U95

U35

U86

D47

D89

U37 U36

D51

U16

D38

U44 U26 U34

U89

U66

U31

D55 D25 U47

U73

D85

D33

U14

U42

D80

D68

D75 D22 D92

U55

D17

D83

D93

U85

U81

Scores PC#1 (58.241%)

PCA Scores [Model 2: Mean centered/all variables/all sam ...]

U10

U19

D14

D66 U32

U99

D12 D58

D99

D28 D61 D73

D16 D43 D91

D74

U80

D95 D37

D19

U61

U58

D90 D32

U49

U75

D31

U17

U59 U68

Scor

es P

C#2

(30.

081%

)

 28  

 

Figure   10.   Detail   from   raw   spectra   showing   differentiation   between   subcutaneous   fat   layers.  Internal  layer  sample  in  green  color  and  external  layer  sample  in  blue  color.

In order to evaluate the potential to predict the iodine value, several PLS

models were calculated with both fat layer together and separately. Variable

selection was performed in the different cases using regression coefficient

values where variables with value close to zero were removed (Variable

Importance in Projection method was also used with worse results). Several

tests eliminating variables ranges related to different peaks from the NMR raw

data were performed and no improvements on the prediction were observed.

Both iodine value calculated with the GC-FAME (Calculated from

CAROMETEC fatty acid analysis and calculated with the analysis previously

performed) were selected in different models. The iodine values from

CAROMETEC did not make layer differentiation; hence, more accurate

models were calculated using iodine values calculated from the GC-FAME

previously performed. In the models where the % of fatty acid was included as

variable, iodine values obtained from CAROMETEC were used due to the

direct correlation between the fatty acid % and the iodine value calculated

from the previous GC-FAME. This direct correlation improve falsely the model

being important to avoid that situation.

1.21.31.41.51.61.71.81.922.10

1

2

3

4

5

6

x 106

ppm

 29  

Outlier detection was performed using the high leverage (T2) values but

no samples were deleted. Results from the different model calculations are

shown on the Table 4 and explained below.

Table  4.  Main  characteristics  of  the  PLS  models  calculated  from  the  NMR  data.  

Model 1 - A first model with all the variables and the 98 samples was

calculated.

Model 2 – Variable selection using Regression Coefficient values was

performed. Variables with value close to zero were deleted from the model

progressively until model started to decrease quality of predictions.

Model 3 & 4 – Calculation with the different layers separately. The

prediction using the internal fat layer seems to be much more accurate than

with the external layer or both layers, as it was supported for the GC data

analysis performed before.

Model 5 – Again variable selection using Regression Coefficient values

was performed. The prediction calculated is quite good, but the high number

of PCs necessary for prediction was encouraging for continuing a better

prediction model search.

Model 6 – The % of fatty acid obtain from the GC-FAME experiment was

added. As explained before, due to the direct correlation between these

values and the iodine value calculated, it was mandatory the use of the iodine

value obtained from CAROMETEC.

The number of PCs used for prediction decrease significantly so new

model using the internal layer data is following. was attained through variable

selection (using coefficient regression again) from Model 5. On each variable

 30  

selection performed the variables were removed little by little until the model

started to decrease quality by increasing the RMSECV and decreasing R2.

Models 7 – The model was attained through variable selection (using

coefficient regression again). The number of PCs used for prediction

decrease significantly so new model using the internal layer data is following.

Model 8 – Same procedure than in Model 6 was done but using only the

internal layer. The model improves significantly its quality as number of PCs

used decreases, prediction error decreases and coefficient of determination

increases.

Model 9 - It was reached by variable selection using coefficient

regression from the Model 8.

Model 10 – As the iodine value from CAROMETEC was calculated from

the mixing of both layers, a sum of spectra from each layer was performed on

each sample and new model was calculated. This technique was neither a big

difference on the model quality.

Model 11 – Variable selection from Model 10 was performed with slightly

model improving.

Based on these models calculated using the NMR data and the previous

models calculated using the GC-MS, it could be stated that best predictions

models are the ones using only data from the internal subcutaneous fat layer.

The best model using GC-MS has a RMSECV = 2.488 and R2 = 0.8696

(Model 3 in Table 2), the best model gotten from the HF-1H-NMR has a

RMSECV = 1.768 and R2 = 0.9003 Model 5 in Table 4), and the best model

developed using data from both methods has a RMSECV = 2.491 and R2 =

0.8704 (Model 9 in Table 4). These results combined with the fact that NMR

measurements are less time consuming than GC-MS analysis shows HF-

NMR as the best analytical method for iodine value prediction.

 31  

5. CONCLUSION

The present study shows a clear improvement on iodine value

predictions when using analysis information from the inner subcutaneous fat

layer. This statement is supported by both methods used in the study: Gas

chromatography and HF-1H-NMR.

The different models developed for iodine value prediction, using both

methods separately and together, conclude that the best model is based on

HF-1H-NMR analysis, resulting in RMSECV = 1.768 and R2 = 0.9003 model

parameters.

6. ACKNOWLEDGEMENTS

I would like to thank Bente Pia Danielsen for her incalculable help on the

laboratory work, with really kind and warm assistance in every moment. Also,

I want to thank Josefina Gonzalez-Solveyra for her enormously support on the

most stressful moments, knowing how to touch the piano keys to create the

necessary melodies for appeasing my being on each moment of need. I would

like to thank my family as well, because without their unbroken support I

would not be finishing this step, living as I live, and writing this with the

feelings I am doing it. To all my friends who have been upholder so many

times to my struggle and worries, particularly to Manuel Tobajas and Clara De

Juanas for taking care of my mind even when they have to be focus on the

great experience they are living. I would like to finally thank to the beautiful

nights on this city, for being always the good shelter I need. Gracias.

The hallway ends and all the doors have been left open.    

 32  

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