Near infrared reflectance spectroscopy predicts the content of

polyunsaturated fatty acids and biohydrogenation products in

the subcutaneous fat of beef cows fed flaxseed

Running title Estimation of fatty acid composition in cow subcutaneous fat by NIR

spectroscopy

N. Prieto1, M.E.R. Dugan2, O. López-Campos2, T.A. McAllister3, J.L. Aalhus2, B.

Uttaro2

1Instituto de Ganadería de Montaña (Consejo Superior de Investigaciones Científicas –

Universidad de León). Finca Marzanas. E-24346 Grulleros, León, Spain.

2Lacombe Research Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail,

Lacombe, Alberta, T4L 1W1, Canada.

3Lethbridge Research Centre, Agriculture and Agri-Food Canada, 1st Avenue South

5403, P.O. Box 3000, Lethbridge, Alberta T1J 4B1.

*Corresponding author: Nuria Prieto. Instituto de Ganadería de Montaña (CSIC–

ULE). Finca Marzanas. E-24346 Grulleros, León (Spain). Tel +34 987 317 064, Fax

+34 987 317 161, E-mail: nuria.prieto@eae.csic.es

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Abstract

This study examined the ability of near infrared reflectance spectroscopy (NIRS) to

estimate the concentration of polyunsaturated fatty acids and their biohydrogenation

products in the subcutaneous fat of beef cows fed flaxseed. Subcutaneous fat samples at

the 12th rib of 62 cows were stored at -80 ºC, thawed, scanned over a NIR spectral range

from 400 to 2498 nm at 31 ºC and 2 ºC, and subsequently analyzed for fatty acid

composition. Best NIRS calibrations were with samples at 31 ºC, showing high

predictability for most of the n-3 (R2: 0.81-0.86; RMSECV: 0.11-1.56 mg. g-1 fat) and

linolenic acid biohydrogenation products such as conjugated linolenic acids, conjugated

linoleic acids (CLA), non-CLA dienes and trans-monounsaturated fatty acids with R2

(RMSECV, mg. g-1 fat) of 0.85-0.85 (0.16-0.37), 0.84-0.90 (0.21-2.58), 0.90 (5.49) and

0.84-0.90 (4.24-8.83), respectively. NIRS could discriminate 100 % of subcutaneous fat

samples from beef cows fed diets with and without flaxseed.

Keywords: near infrared reflectance spectroscopy, subcutaneous fat, fatty acid,

flaxseed.

1. Introduction

Today’s health conscious consumers are interested in fat composition as scientific

evidence suggests that diets high in saturated fat are associated with increased levels of

blood total and low density lipoproteins, which are associated with increased risk of

cardiovascular disease (Webb & O'Neill, 2008). Coronary heart disease is a major

public health concern, as it accounts for more deaths than any other disease or group of

diseases (British Heart Foundation, 2006). Thus, a lower saturated fatty acids (SFA) and

a higher polyunsaturated fatty acids (PUFA) intake, especially of n-3 fatty acids (FA) to

achieve an appropriate n-6/n-3 ratio (<5:1, World Health Organization, 2003), are

recommended in order to avoid cardiovascular-type disease. Due to their importance in

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human health, Canadian regulatory authorities have recently approved a food labelling

claim for foods enriched in n-3 fatty acids at ≥ 300 mg per 100 g serving (CFIA, 2003).

Hence, development of value-added beef products with enhanced levels of n-3 fatty

acids could substantially increase the n-3 FA intake of humans. The amount of

subcutaneous fat and its fatty acid composition in beef are heavily influenced by diet

(Wood et al., 2008), which also influences the quality of processed products such as

sausages that are prepared with up to 30% subcutaneous fat. Feeding flaxseed is one

approach known to increase levels of n-3 FA in pork, poultry, beef and dairy products

and consumption of these enriched products increases erythrocyte n-3 FA levels in

humans (Legrand et al., 2010). Flaxseed contains 40% oil and of this 50-60% is

linolenic acid (18:3n-3, LNA) making flaxseed one of the richest plant sources of n-3

FA. Furthermore, in ruminants, bacterial biohydrogenation in the rumen can result in

accumulation of partial hydrogenation products including vaccenic acid (trans (t)11-

18:1, VA) and rumenic acid (cis (c)9,t11-18:2, RA), both of which have purported

health benefits (Field, Blewett, Proctor, & Vine, 2009; Park, 2009). Thus, feeding

flaxseed to cattle may also present opportunities for producing beef products with

enhanced levels of partial biohydrogenation products of linolenic acid as shown by

Kronberg, Barcelo-Coblijn, Shin, Lee, & Murphy (2006), Montgomery, Drouillard,

Nagaraja, Titgemeyer, & Sindt (2008) and Nassu et al. (In Press).

Quantitative chemical techniques for the comprehensive determination of FA

involves solvent extraction of total lipids, followed by conversion of fatty acids to their

methyl esters and then analysis by GC and Ag+-HPLC (Kramer, Hernandez, Cruz-

Hernandez, Kraft, & Dugan, 2008). This procedure is costly and time-consuming and

does not lend itself to rapid on-line analysis of fatty acid profiles in meat. On the

contrary, near infrared reflectance (NIR) spectroscopy is a rapid and non destructive

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method, neither requiring reagents nor producing waste (Osborne, Fearn, & Hindle,

1993; Prieto, Roehe, Lavín, Batten, & Andrés, 2009a). Because of these advantages,

this technology is being used for large-scale meat quality evaluation to predict chemical

composition (Alomar, Gallo, Castañeda, & Fuchslocher, 2003; Prieto, Andrés, Giráldez,

Mantecón, & Lavín, 2006) as well as physical and sensory characteristics of meat

(Shackelford, Wheeler, & Koohmaraie, 2005; Andrés et al., 2007; Prieto, Andrés,

Giráldez, Mantecón, & Lavín., 2008; Prieto et al., 2009b). Regarding FA, their structure

can produce individual spectral characteristics and therefore are very suitable for

detection and identification by NIR spectroscopy (González-Martín, González-Pérez,

Hernández-Méndez, Alvarez-García, & Merino Lázaro, 2002). Hence, NIR

spectroscopy has been applied to study the FA composition in intact pork (González-

Martín, González-Pérez, Alvarez-García, & Gónzalez-Cabrera, 2005) and beef loins

(Prieto et al., 2011), ground beef (Realini, Duckett, & Windham, 2004; Sierra, Aldai,

Castro, Osoro, Coto-Montes, & Oliván, 2008) and Iberian pig fat (González-Martín,

González-Pérez, Hernández-Méndez, & Álvarez-García, 2003). Nevertheless, to our

knowledge, there are no studies testing the ability of this technology to estimate the FA

composition in the subcutaneous fat of cows, particularly those enriched with linolenic

acid biohydrogenation products. Hence, this study was conducted to examine the

potential of NIR spectroscopy to predict the FA composition in intact subcutaneous fat

samples of beef cows following frozen storage. This work focused on those FA with

potential health effects, whose content was increased in the subcutaneous fat of beef

cows when flaxseed was included in the diet.

2. Material and methods

2.1. Animals and diets

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Sixty-four crossbred (>30 months of age) non-lactating, non-pregnant beef cows

with body weight averaging 620 ± 62 kg were used. Cows were cared for according to

Canadian Council on Animal Care guidelines (CCAC, 1993) and fed at the Lethbridge

Research Centre. Cows were randomly assigned to one of four diets, with four pens of

four cows per diet. Cows had ad libitum access to feed and water. Diets were designed

to meet or exceed nutrient requirements for mature cows (Nassu et al., In Press; NRC,

2000) and consisted of 50:50 forage to concentrate (dry matter basis) and were fed as

total mixed rations. Diets included hay control, barley silage control, hay plus flaxseed

and barley silage plus flaxseed. Flaxseed was ground together with barley in a 7:3 ratio

and flaxseed diets contained 15% flax substituted for dry rolled barley (dry matter

basis). Diets were fed for 20 weeks. Duringthe study two animals were withdrawn due

to lameness, one each from the silage and the silage plus flaxseed treatments.

2.2. Slaughter and sample collection

Animals were slaughtered at the Lacombe Research Centre. At 24 h post mortem,

approximately 200 g of subcutaneous fat was removed from the 12 th rib and stored at -

80 ºC for subsequent fatty acid determinations and NIR spectral analysis.

2.3. Fatty acid analysis

From the subcutaneous fat collected, five grams were freeze dried and subsampled

for fatty acid analysis according to Aldai, Dugan, Rolland, and Kramer (2009).

2.4. Spectra collection

Subcutaneous fat for NIRS analysis was thawed overnight at +2 ºC. Duplicate intact

circular fat cores were obtained with the help of a custom-constructed stainless steel

device (Figure 1a) to enable consolidation of fat and produce fat discs of an appropriate

diameter (38 mm) and thickness (7 mm) to fit the ring cups of the NIRS machine

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(Figure 1b). Each cold fat disc was placed in a ring cup, all visible air bubbles removed

by squeezing, and the cup backed with thin black foam (Figure 1c). NIR spectra were

collected when the subcutaneous fat samples were at 2 ºC, hereafter referred to as “cold

samples”. Subsequently, the cold samples were placed in open plastic bags and heated

in a water bath at 35º C. A DuaLogR model 600-1050 (Barnant Company Barrington,

USA) thermocouple was inserted into the center of each fat sample for temperature

monitoring during warming. As soon as the core sample reached the target endpoint

temperature (31º C), samples were immediately removed from the water bath and NIR

spectra were collected from these “warm samples”. The aim of using two temperatures

was to know at which point in the slaughter chain NIR could be used on-line. The

temperature of the warm samples approximates the temperature of subcutaneous fat

immediately after skinning, and the temperature of the cold sample mirrored that which

would be obtained after carcasses were stored in a cooler for 24 h. Subcutaneous fat

sample was scanned 32 times over the range (400-2498 nm) using a NIRSystems

Versatile Agri Analyzer (SY-3665-II Model 6500, FOSS, Sweden), and spectra

averaged by the equipment software. Two fat samples per animal were scanned using

two different cells, and each sample was scanned twice (resulting in four average

spectra per cow). This approach increased the area of the subcutaneous fat scanned and

reduced the sampling error (Downey & Hildrum, 2004). The four reflectance spectra of

each sample were visually examined for consistency and then averaged, with the mean

spectrum being used to predict the fatty acid content of each subcutaneous fat sample.

The spectrometer interpolated the data to produce measurements in 2 nm steps, resulting

in a diffuse reflectance spectrum of 1050 data points. Absorbance data were stored as

log (1/R), where R is the reflectance. Instrument control and initial spectral

manipulation were performed with WinISI II software (v1.04a; Infrasoft International,

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2.5. Data analysis

Calibration and validation of the NIRS data were performed using The

Unscrambler® program (version 8.5.0, Camo, Trondheim, Norway). The detection of

anomalous spectra was accomplished using the Mahalanobis distance (H-statistic) to the

centre of the population, which indicates how different a sample spectrum is from the

average spectrum of the set (Williams & Norris, 2001). A sample with an H statistic of

≥ 3.0 standardized units from the mean spectrum was defined as a global H outlier and

was eliminated from the population. In addition, some samples were removed from the

initial data set as concentration outliers (T-statistic), which measures how closely the

reference value matches the predicted value. Hence, the samples whose predicted values

exceed 2.5 times the standard error of estimation were considered as T statistic outliers

and excluded from the population. Spectral data were subjected to multiplicative scatter

correction (MSC; Dhanoa, Lister, Sanderson, & Barnes, 1994) to reduce

multicolinearity and the effects of baseline shift and curvature on spectra arising from

scattering effects due to physical effects. First or second order derivatives (Shenk,

Westerhaus, & Workman, 1992) were applied to the spectra to increase the resolution of

spectral peaks, and heighten signals related to the chemical composition of

subcutaneous fat samples (Davies & Grant, 1987). Partial least square regression type I

(PLSR1) was used for predicting FA concentration using NIR spectra as independent

variables. Internal full cross-validation was performed to avoid over-fitting the PLSR

equations. Thus, the optimal number of factors in each equation was determined as the

number of factors after which the standard error of cross-validation no longer decreased.

The predictive ability of the PLS calibration models was evaluated in terms of

coefficient of determination (R2), root mean square error of cross-validation (RMSECV)

(Westerhaus, Workman, Reeves III, & Mark, 2004) and ratio performance deviation

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(RPD) (Williams, 2001 & 2008). RMSECV and RPD are regarded as measures of

precision and accuracy of prediction and are defined by:

where n is the number of samples in the calibration set, the yi represents the real

(measured) responses, the represents the estimated responses obtained via cross-

validation and SD is the standard deviation of the reference values of the calibration set.

Williams (2001 & 2008) suggested that the RPD statistic should be equal or larger than

2, since lower RPD values could be attributed either to a narrow range of the reference

values (giving a small SD) or to a large error in the estimation (RMSECV) compared to

SD (Tøgersen, Arnesen, Nielsen, & Hildrum, 2003).

In order to discriminate among subcutaneous fat samples from beef cows fed

different diets (hay/barley silage with or without flaxseed supplementation) by NIR

spectra, discriminant analysis was performed using the dummy regression technique on

the absorbance data with The Unscrambler® software (version 8.5.0, Camo, Trondheim,

Norway) (Cozzolino, De Mattos, & Martins, 2002; Cozzolino, & Murray, 2004). The

subcutaneous fat samples were identified with dummy variables (hay/barley silage = 1,

hay/barley silage with flax = 2) and PLSR was used to generate a mathematical model

that was cross-validated (leave one-out) to select the most relevant PLS components.

According to this equation, a sample was classified as subcutaneous fat belonging to a

specific category (hay/barley silage or hay/barley silage with flax) if the predicted value

was within ±0.5 of the dummy value.

3. Results and discussion

3.1. Chemical data

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Ranges, means, standard deviations (SD) and coefficients of variation (CV) of

PUFAs and their biohydrogenation intermediates from subcutaneous fat are summarized

in Table 1. In general terms, the concentrations of FA in the subcutaneous fat were

within the normal range of variation reported by other authors in the subcutaneous

adipose tissue of beef (Noci, Monahan, French, & Moloney, 2005; Dugan, Rolland,

Aalhus, Aldai, & Kramer, 2008). The results revealed wide variability, which is

important when searching for calibration equations to be used for predictions. The

causes of such variability resulted from the different feeding regimes used in the study.

Hence, the CV were higher than 50% for most of the FA and even higher than 100% for

C20:3n-3, total conjugated linolenic acids (CLNA), c9,t11,t15-18:3 and c9,t11,c15-

18:3.

The n-6:n-3 FA ratio is often used to evaluate the nutritional quality of fat. In this

study, the n-6:n-3 ratio was 2.6 (Table 1), a value considered suitable according to the

recommendation of the World Health Organization (<5; 2003).

Additionally, FA values expressed as mg n-3 FA per 100 g subcutaneous fat were

calculated to verify if the subcutaneous fat from cows fed the four diets achieved the

regulatory label claim status for meat products in Canada (≥ 300 mg omega-3 per 100 g

serving; CFIA, 2003). The n-3 FA content of the subcutaneous fat was 2x (i.e. 600 mg

per 100 g-1 fat) that required for a label claim and thus would be suitable for producing

meat products such as sausages and ground beef that satisfy the source claim.

3.2. Spectral information

Figure 2a shows the raw spectrum [log (1/R)], averaged over warm and cold

subcutaneous fat samples. Although the overall absorbance represented by these spectra

was different for warm and cold samples as a consequence of the temperature, they

followed the same pattern. In both samples the spectral information showed a series of

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characteristic absorption bands at 1130-1250, 1350-1450, 1720-1760 and 2200-2400

nm, which are known wavelengths where the C-H bond (fundamental constituent of

fatty acid molecules) causes different forms of vibration (Murray, 1986; Murray &

Williams, 1987; Shenk et al., 1992). In addition, there was a peak at 1940 nm which

corresponds to the absorption of the O-H bond that is related to water content.

The application of the second-order derivative to the NIR spectra resulted in a

spectral pattern display of absorption peaks both above and below the baseline (Shenk

et al., 1992), with enhanced resolution of those signals related to the fatty acid

composition of the fat (Figure 2b). The derivative decreased the spectral difference due

to temperature between warm and cold samples, showing a spectral pattern very similar

for both. Nevertheless, the peaks at 1215, 1725, 1765 and 2310 nm in the second-order

derivative spectrum, which were located in the same wavelength as in the raw spectra of

both fat samples but with better definition and inverted, were different in intensity for

both warm and cold samples. The inverted peaks can be attributed to the absorption by

the C-H bonds present in fatty acids. In this way, the absorption produced at 1215 and

2310 nm is attributed at the second overtone of the C-H bond and that at 1725 and 1765

nm corresponds to the first overtone of this bond (Murray, 1986; Murray & Williams,

1987; Shenk et al., 1992). Hence, it is possible to predict the FA profiles of

subcutaneous fat samples based on absorbance of C-H bonds and their different forms

and degrees of vibration at different wavelengths of NIRS measurements. Thus, all

information of C-H bond absorbance was combined and equations to estimate the

content of polyunsaturated fatty acid and biohydrogenation products in subcutaneous fat

were developed separately for cold and warm samples.

3.3. Prediction of the fatty acid composition

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After eliminating outliers (which were different for each estimated FA and ranged

from 0 to 2) and testing different mathematical treatments, the best calibration equations

for the FA composition of subcutaneous fat samples, using the criteria of maximising

the coefficient of determination (R2) and minimising the RMSECV, are shown in Tables

2 and 3, respectively. In relation to mathematical treatments, all the FA were more

successfully predicted when derivatives with or without previous MSC were applied to

the spectra, which reduced noise and light scattering effects. This is in agreement with

the results of others (González-Martín et al., 2002, 2003, 2005; Sierra et al., 2008;

Prieto et al., 2011) who observed that the use of the MSC or standard normal variance

and de-trend (SNVD) treatment and/or derivatives generated the NIRS calibrations that

most accurately predicted the FA content in pig subcutaneous fat, and pork and beef

meat samples.

As presented in Table 2 and 3, the prediction equations for total n-6, C18:2n-6 and

C20:4n-6 in subcutaneous fat samples showed R2 from 0.03 to 0.11 when NIR spectra

were collected on both warm and cold fat samples, indicating low NIRS predictability.

Furthermore, the RMSECV (0.09-1.88 mg. g-1 fat) were high when compared to SD,

thus the RPD were lower than 1.00, deviating substantially from that considered as

suitable for screening purposes (RPD ≥ 2; Williams, 2001 & 2008). Only for the

C20:3n-6 was the percentage of variance explained by the model over 59% on both

warm and cold fat samples (R2 = 0.62 and 0.59, respectively). Nevertheless, the

RMSECV for C20:3n-6 in warm and cold samples (RMSECV = 0.17 and 0.18 mg. g-1

fat, respectively) were still high when compared to SD (SD = 0.22 mg. g -1 fat);

generating RPD values that were not high enough (RPD = 1.29 and 1.22, respectively)

to suitably predict it. It is well known that the success of this procedure relies partially

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on the variability present in the samples analyzed, which was relatively low among

samples for these FA (Table 1); limiting prediction via NIRS.

On the other hand, when the content of total n-3, C18:3n-3 (linolenic acid, LNA)

and C20:3n-3 were estimated for warm fat samples, the predictability was higher than

found for n-6 content. In this sense, the R2 (RMSECV) ranged from 0.81 (0.11 mg. g-1

fat) to 0.86 (1.56 mg. g-1 fat) and the RPD statistics from 1.90 to 2.01, indicating that

NIRS was more suitable for predicting the presence of these FA. NIRS was less suitable

for predicting C22:5n-3 as the variance explained by the model was very low (5 %) and

the RMSECV (0.20 mg. g-1 fat) was higher than the SD (0.18 mg. g-1 fat), generating a

RPD lower than 1.0. Again, a narrower range of variability for this FA together with a

low concentration could have negatively influenced the NIRS prediction. When looking

at the equation predictions performed with the NIR spectra collected on cold samples,

the accuracy of prediction was lower for n-3, C18:3n-3 and C20:3n-3 (R2 = 0.77-0.80;

RMSECV = 0.12-1.75 mg. g-1 fat; RPD = 1.76-1.83). During the trial it was observed

that when the samples were warmed to 31 ºC, the fat which occasionally showed small

and unremovable air bubbles became free of these bubbles and also became slightly

translucent. A less homogeneous distribution of fat throughout the cells and more air

bubbles or reduced molecular vibration due to the cooler temperature could have been

the reasons for the poorer predictions when using cold samples. Thus, NIR spectroscopy

showed a higher predictability of estimation for n-3 FA content on intact warm than on

cold samples. This could be useful for early in-plant identification of beef fat that is

enriched with these FA. Regarding the n-6/n-3 ratio, the NIRS predictability was low

when both warm and cold samples were scanned (R2 = 0.71 and 0.74; RMSECV = 0.98

and 1.07 mg. g-1 fat; RPD = 1.51 and 1.44; respectively).

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Accurate NIRS predictions were found for the total conjugated linolenic acids

(CLNA) and its two isomers c9,t11,t15-18:3 and c9,t11,c15-18:3, when the NIR spectra

were collected on both warm and cold fat samples. The coefficients of determination

were over 0.83 (reaching up to 0.87) and the standard errors of cross-validation were

low (RMSECV = 0.16-0.37 mg. g-1 fat) compared to SD for these FA. Consequently,

RPD statistics ranged from 1.90 to 2.05, making them suitable for screening purposes

(Williams 2001 & 2008). In the same way, total conjugated linoleic acids (CLA) and

total t,t-CLA and total c,t-CLA could be accurately predicted by NIR spectroscopy

when spectra from warm fat samples were collected (R2 = 0.87, 0.90 and 0.86;

RMSECV = 2.58, 0.21 and 2.39 mg. g-1 fat; RPD = 2.12, 2.71 and 2.02; respectively).

When the NIR spectra were collected on cold samples, the predictability was slightly

lower (R2 = 0.82, 0.83 and 0.84; RMSECV = 2.79, 0.27 and 2.58 mg. g -1 fat; RPD =

1.96, 2.11 and 1.90; respectively) although the prediction equations were accurate

enough to be used for screening purposes. According to De la Torre et al. (2006) and

Nassu et al. (In Press), these products coming from the LNA biohydrogenation

preferentially accumulate in intramuscular and back fat when flaxseed combined with

hay has been fed. In this sense, in the current study NIR spectroscopy was demonstrated

to be a rapid and accurate approach to estimate their content. Within c,t-CLA isomers,

c9,t11-CLA (rumenic acid, RA) is typically the most concentrated isomer and widely

studied. Considered to have beneficial effects on human health (Field et al., 2009), the

levels of RA were increased in back fat and Longissimus thoracis muscle when feeding

flaxseed together with hay, in comparison with feeding flaxseed plus silage in those

tissues (Nassu et al., In Press). The NIRS predictability for the RA content was slightly

lower than that for total CLA, total t,t- and total c,t-CLA, but the corresponding

calibration equations still showed high R2 and low RMSECV (R2 = 0.84 and 0.82;

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RMSECV = 2.24 and 2.26 mg. g-1 fat; RPD = 1.90 and 1.89; warm and cold fat spectra,

respectively) and were deemed appropriate for prediction.

Regarding the non-CLA dienes, successful prediction byNIR spectroscopy was

observed when spectra were collected on both warm and cold fat samples (R2 = 0.90

and 0.90, RMSECV = 5.49 and 5.46 mg. g-1 fat, RPD = 2.39 and 2.40, respectively).

Nassu et al. (In Press) observed a forage type by flaxseed level interaction indicating a

preferential accumulation of LNA biohydrogenation products such as the non-CLA

dienes in backfat when feeding flaxseed combined with hay. The potential health effects

of many non-CLA dienes are not known, but if flaxseed is to be fed to ruminants at

elevated levels, it will be important to ascertain if non-CLA dienes have any positive or

negative effects on human or animal health (Chilliard et al., 2007). NIR spectroscopy

could provide a rapid estimate of the dienes content of fat.

In the case of monounsaturated FA (MUFA), content of total trans-MUFA was

predicted with accuracy when NIR spectra of both warm and cold fat samples were

collected (R2 = 0.90 and 0.90, RMSECV = 8.83 and 9.13 mg. g-1 fat, RPD = 2.52 and

2.43; respectively). In contrast, the NIRS predictability for total cis-MUFA content was

less reliable (R2 = 0.71 and 0.76, RMSECV = 29.84 and 27.15 mg. g-1 fat, RPD = 1.51

and 1.66; respectively), probably due to lower variability in the sample population (CV

= 8.6 % vs. 64.4 %, Table 1). Furthermore, NIR spectroscopy was shown to be an

accurate method to predict the content of (t)11-18:1 (vaccenic acid, VA) (R2 = 0.84 and

0.84; RMSECV = 4.24 and 4.42 mg. g-1 fat, RPD = 2.02 and 1.95; warm and cold fat

spectra, respectively). As with RA, bacterial biohydrogenation of PUFAs in the rumen

can result in accumulation of partial biohydrogenation products among which VA has

purported health benefits (Field et al., 2009). Feeding flaxseed may present

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opportunities for producing beef products with enhanced levels of VA and NIR

spectroscopy shows good potential to accurately predict VA content.

Comparisons among the current study and those in the literature for the prediction of

FA in subcutaneous fat by NIR spectroscopy are complicated because of the use of

different NIRS equipment, measurement modes, wavelength ranges, sample preparation

and FA chemical analysis. Furthermore, it must be emphasised this work was focused

only on those FA with potential health effects whose content was increased in the

subcutaneous fat of beef cows when flaxseed was included in the diet. Additionally,

most researchers test the ability of NIR spectroscopy to predict the FA composition in

intramuscular fat, not in subcutaneous fat. A few researchers have used NIR

spectroscopy to predict the FA composition in the subcutaneous fat in pigs (González-

Martín et al., 2002 & 2003; Pérez-Marín, De Pedro Sanz, Guerrero-Ginel, & Garrido-

Varo, 2009; Pérez-Juan et al., 2010), but to our knowledge there are no studies that have

evaluated the ability of NIRS to estimate the FA composition of subcutaneous fat in

beef. In comparison with pork, the current study shows stronger predictions than those

obtained by González-Martín et al. (2002) for C18:1, C18:2 and C18:3 content in the

subcutaneous fat of swine when NIR spectra were collected on fat extracted with

solvents (R2 = 0.83, 0.77 and 0.59; respectively) or when melted using microwaves (R2

= 0.81, 0.69 and 0.40; respectively). In the present study the spectra were collected on

intact frozen-thawed subcutaneous fat whereas in the study by González-Martín et al.

(2002) the fat underwent significant treatment before spectral collection, which could

have negatively influenced the strength of the predictions. Indeed, González-Martín et

al. (2003) showed better results when scanning intact the subcutaneous fat of swine for

C18:2 (R2 = 0.91), which was similar to the accuracy of the predictions in the current

study. Pérez-Juan et al. (2010) found similar results for c9,t11-CLA in subcutaneous fat

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from pigs (R2 = 0.92, RMSECV = 2 mg. g-1 fat) compared to beef subcutaneous fat in

the present study. In contrast, in two separate studies Pérez-Juan et al. (2010; R2 = 0.68,

RMSECV = 11 mg. g-1 fat, RPD = 1.67) and Pérez-Marín et al. (2009; R2 = 0.39,

RMSECV = 4.70 mg. g-1 fat, RPD = 1.3) reported that NIRS more reliably predicted the

C18:2n-6 content of subcutaneous fat from pigs than found in the present study for beef.

However, these were still not accurate enough to be used for screening purposes. This

lack of agreement between studies could be due to differences in the variability of the

samples. Indeed, the FA studied in the present work showed a wider range of variation

than that found in the previous studies (Pérez-Marín et al., 2009; Pérez-Juan et al.,

2010) with subcutaneous fat from swine which likely arose from either the different

feeding regimes used in this study or different levels between species (pig vs. cattle, that

is monogastric vs. ruminant due to complexity of the rumen environment).

In general, the prediction equations for FA composition were more accurate when

NIR spectra were collected on intact warm than cold subcutaneous fat samples. This

approach would potentially allow NIR spectra to be collected immediately after

slaughter when fat is still warm, a very important aspect when considering on-line use

of this technology in the abattoir. The NIRS equipment used in this study was a

benchtop unit not configured for on-line testing; hence, further studies with equipment

provided with a fibre-optic probe are required to assess the on-line implementation of

NIR spectroscopy in the abattoir. Under practical conditions where fat samples are

scanned fresh the predictability of NIRS predictions are expected to be higher than

those using fat whose structure and cell walls may have been affected by the formation

of ice crystals of varying sizes during freezing and thawing, since the possible effects

arising from the frozen storage would be eliminated.

3.4. Discrimination of subcutaneous fat samples from beef cows fed different diets by

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NIR spectroscopy.

In order to ascertain whether the NIR spectra collected on warm fat samples could

provide useful information to discriminate subcutaneous fat samples from beef cows fed

diets with or without flaxseed, the absorbance data matrix (MSC+2D, mathematical

treatment that provided better predictions for most FA) was reduced to a coordinate axis

system, so each sample was defined by the corresponding scores for each PLS

component. When the whole sample set was represented on a XY plane according to the

scores for PLS component 1 and PLS component 2, two different clusters were

observed (Figure 3) with one cluster on the left representing subcutaneous fat samples

derived from beef cows fed hay or silage (hay / barley silage) and the other on the right

from cows that were fed these forages along with flaxseed (hay / barley silage flax).

Thus, most of the samples belonging to the hay / barley silage group showed negative

scores in relation to PLS component 1 whereas those for the samples included in the

hay/ barley silage flax group were positive, with sample groupings being related to the

degree of similarity in their spectra.

With regard to the dummy regression, 5 PLS components were retained in the model

since after that the standard error of cross validation no longer meaningfully decreased.

The scores corresponding to 5 PLS components could successfully discriminate 100 %

of the subcutaneous fat samples according to the diet that the beef cows were fed (hay

or barley silage alone or combined with flaxseed) (Figure 4). Statistically significant

differences (p < 0.001) in some of the studied FA between the subcutaneous fat samples

from beef cows fed diets with and without flaxseed (Nassu et al., In Press) could have

provided the basis for successfully classifying the whole sample set according to the

spectral data.

4. Conclusion

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This study shows that the content of n-3 FA and linolenic biohydrogenation

products such as CLNA, CLA, non-CLA dienes and trans-MUFA were predicted with

accuracy by means of NIR spectroscopy in the subcutaneous fat of beef cows fed

flaxseed. These predictions were better from warm than from cold subcutaneous fat

samples what would potentially allow NIR spectra to be collected immediately after

slaughter. Additionally, accurate NIRS predictions were found for individual

biohydrogenation intermediates including rumenic and vaccenic acids, which have

purported health benefits. Furthermore, NIR spectroscopy could discriminate 100 % of

subcutaneous fat samples from beef cows fed different diets (hay/ barley silage with or

without flaxseed supplementation). Hence, this technology has the potential to quickly

and accurately estimate the content of FA of subcutaneous fat from beef cows,

particularly when feeding diets with large differences in polyunsaturated fatty acids.

Further research will now be required to further validate NIR spectroscopy for fatty acid

analyses on-line in the abattoir.

5. Acknowledgements

The authors wish to thank Lacombe Research Centre operational, processing and

technical staff for their dedication and expert assistance. Nuria Prieto has a JAE-Doc

contract from the Spanish National Research Council (CSIC) under the programme

“Junta para la Ampliación de Estudios”.

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Table 1. Descriptive statistics for fatty acids (mg. g-1 fat tissue) in subcutaneous fat of

beef cows (n = 62).

Fatty acid Range Mean SD10 CV11 (%)

PUFA1

n-6 7.2-15.4 11.5 1.68 14.6

C18:2n-6 6.4-14.4 10.8 1.61 14.9

C20:3n-6 0.1-1.1 0.5 0.22 45.5

C20:4n-6 0.1-0.6 0.2 0.09 39.8

n-3 1.3-14.5 6.0 3.09 51.6

C18:3n-3 0.8-13.2 5.4 2.86 53.4

C20:3n-3 0.0-0.7 0.2 0.21 110.0

C22:5n-3 0.1-1.3 0.4 0.18 40.2

CLNA2 0.0-2.4 0.6 0.75 128.0

c9,t11,t15-18:3 0.0-1.5 0.3 0.45 137.6

c9,t11,c15-18:3 0.0-1.1 0.3 0.32 122.8

CLA3 1.8-24.3 9.6 5.46 56.6

t,t-CLA4 0.2-2.7 0.8 0.57 70.08

c,t-CLA5 1.7-21.3 8.7 4.83 55.3

c9,t11-CLA 1.3-18.5 7.0 4.17 59.9

Non-CLA dienes6 3.7-55.6 17.1 13.10 76.5

MUFA7

cis-MUFA8 411.6-616.4 521.6 44.99 8.6

trans-MUFA9 10.2-103.1 34.5 22.22 64.4

t11-18:1 2.8-35.7 12.7 8.54 67.1Ratios

n-6/n-3 1.0-6.8 2.6 1.57 60.51PUFA: polyunsaturated fatty acids; 2CLNA: conjugated linolenic acids; 3CLA: conjugated linoleic acids; 4t,t-CLA: t12,t14 + t11,t13 + t10,t12 + t9,t11+ t8,t10 + t7,t9 + t6,t8-CLA; 5c,t-CLA: t12,c14 + c12,t14+ t11,c13 + c11,t13 + t10,c12 + t8,c10 + t7,c9 + c9,t11 + t9,c11-CLA; 6Non-CLA dienes: t11,t15-18:2 + c9,t13-/t8,c12-18:2 + t8,c13-18:2 + c9t12-18:2/c16-18:1 + t9c12-18:2 + t11c15-18:2 + c9c15-18:2 + c12c15-18:2; 7MUFA: monounsaturated fatty acids; 8cis-MUFA: c9-14:1 + c9-15:1 + c7-16:1 + c9-16:1 + c10-16:1 + c11-16:1 + c13-16:1 + c9-17:1 + c9-c10-18:1 + c11-18:1 + c12-18:1 + c13-18:1 + c14-18:1 + c15-18:1 + c9-20:1 + c11-20:1; 9trans-MUFA: t9-16:1 + t11/t12-16:1 + t6-t8-18:1 + t9-18:1 + t10-18:1 + t11-18:1 + t12-18:1 + t13-t14-18:1 + t15-18:1 + t16-18:1; 10SD: standard deviation; 11CV: coefficient of variation.

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26

Table 2. Prediction of fatty acid profile in subcutaneous fat of beef cows from NIR

spectra collected on warm fat samples (31 ºC).

Mathematical treatment T1 R2 2 RMSEC3 RMSECV4 RPD5

PUFA

n-6 MSC6+1D 1 0.07 1.61 1.88 0.89

C18:2n-6 1D7 1 0.06 1.54 1.78 0.91

C20:3n-6 MSC+2D8 6 0.62 0.14 0.17 1.29

C20:4n-6 1D 1 0.11 0.09 0.09 1.00

n-3 MSC+2D 5 0.86 1.36 1.56 2.01

C18:3n-3 MSC+2D 6 0.83 1.24 1.50 1.92

C20:3n-3 MSC+2D 4 0.81 0.10 0.11 1.90

C22:5n-3 1D 1 0.05 0.17 0.20 0.90

CLNA MSC+2D 6 0.85 0.29 0.37 2.03

c9,t11,t15-18:3 MSC+2D 5 0.85 0.17 0.23 1.96

c9,t11,c15-18:3 MSC+2D 6 0.85 0.12 0.16 2.00

CLA MSC+2D 5 0.87 1.85 2.58 2.12

t,t-CLA MSC+2D 6 0.90 0.16 0.21 2.71

c,t-CLA MSC+2D 6 0.86 1.73 2.39 2.02

c9,t11-CLA MSC+2D 6 0.84 1.67 2.24 1.90

Non-CLA dienes MSC+2D 5 0.90 4.10 5.49 2.39

MUFA

cis-MUFA MSC+2D 6 0.71 23.64 29.84 1.51

trans-MUFA MSC+2D 5 0.90 6.81 8.83 2.52

t11-18:1 MSC+2D 6 0.84 3.35 4.24 2.02

Ratios

n-6/n-3 1D 6 0.71 0.79 0.98 1.511T: number of PLS terms utilized in the calibration equation, 2R2: coefficient of determination of

calibration, 3RMSEC: root mean square error of calibration,4RMSECV: root mean square error

of cross-validation, 5RPD: ratio performance deviation calculated as SD/RMSECV, 6MSC:

multiplicative scatter correction, 71D: first-order derivative, 82D: second-order derivative.

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Table 3. Prediction of fatty acid profile in subcutaneous fat of beef cows from NIR

spectra collected on cold fat samples (2 ºC).

Mathematical treatment T1 R2 2 RMSEC3 RMSECV4 RPD5

PUFA

n-6 MSC6+2D 1 0.04 1.63 1.71 0.98

C18:2n-6 MSC+2D 1 0.03 1.57 1.64 0.98

C20:3n-6 MSC+2D 4 0.59 0.16 0.18 1.22

C20:4n-6 MSC+2D 1 0.10 0.09 0.09 1.00

n-3 MSC+2D 6 0.77 1.54 1.75 1.76

C18:3n-3 1D7 6 0.80 1.29 1.56 1.83

C20:3n-3 1D 6 0.79 0.11 0.12 1.79

C22:5n-3 MSC+2D8 1 0.08 0.17 0.20 0.90

CLNA 2D 6 0.87 0.27 0.37 2.05

c9,t11,t15-18:3 2D 5 0.83 0.18 0.24 1.90

c9,t11,c15-18:3 2D 6 0.87 0.12 0.16 2.00

CLA MSC+2D 5 0.82 2.26 2.79 1.96

t,t-CLA MSC+2D 6 0.83 0.21 0.27 2.11

c,t-CLA MSC+2D 6 0.84 1.92 2.58 1.90

c9,t11-CLA MSC+2D 5 0.82 1.79 2.26 1.89

Non-CLA dienes MSC+2D 6 0.90 4.20 5.46 2.40

MUFA

cis-MUFA 2D 6 0.76 21.66 27.15 1.66

trans-MUFA MSC+2D 5 0.90 7.06 9.13 2.43

t11-18:1 2D 6 0.84 3.42 4.42 1.95

Ratios

n-6/n-3 1D 6 0.74 0.78 1.07 1.441T: number of PLS terms utilized in the calibration equation, 2R2: coefficient of determination of

calibration, 3RMSEC: root mean square error of calibration, 4RMSECV: root mean square error

of cross-validation, 5RPD: ratio performance deviation calculated as SD/RMSECV, 6MSC:

multiplicative scatter correction, 71D: first-order derivative, 82D: second-order derivative.

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Figure 1.

a) Custom-built device to obtain uniform circular cores of intact subcutaneous fat.

b) i: Backfat is cored, and corer is fitted with a fat-advancement device. ii: The corer is

clamped into the sampling device. Fat is advanced slightly into the sizing chamber to

trim the end of the sample flat. Trimmed material is removed, and fat is fully advanced

into the sizing chamber before sample is cut to the correct thickness (7 mm). iii: The

end of the sizing chamber is opened and fat is further advanced to load it directly into a

ring cup.

i ii iii

c) Filled ring cup used for measurement with the NIR apparatus.

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Figure 2. Average NIR spectra of warm (31 ºC) and cold (2 ºC) subcutaneous fat

samples collected from cows (a) prior to mathematical treatment [Log (1/R)] and (b)

second-order derivative.

a)

b)

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

400

506

612

718

824

930

1036

1142

1248

1354

1460

1566

1672

1778

1884

1990

2096

2202

2308

2414

Wavelength (nm)

Seco

nd-o

rder

der

ivat

ive

Warm subcutaneous fat Cold subcutaneous fat

0

0.5

1

1.5

2

2.5

400

498

596

694

792

890

988

1086

1184

1282

1380

1478

1576

1674

1772

1870

1968

2066

2164

2262

2360

2458

Wavelength (nm)

Log

(1/R

)

Warm subcutaneous fat Cold subcutaneous fat

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Figure 3. Scores corresponding to PLS component 1 and PLS component 2 calculated

using the MSC+2D spectra of warm subcutaneous fat samples from beef cows fed

different diets (hay/silage with or without flaxseed supplementation).

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Figure 4. PLS discriminant analysis using the 5 PLS components of the MSC+2D

spectra of warm subcutaneous fat samples from beef cows fed different diets (hay / barley

silage with or without flaxseed supplementation).

Discriminant analysis

0

0.5

1

1.5

2

2.5

3

Pred

icte

ddu

mm

yva

lue

Hay/Silage (dummy value = 1) Hay/SilageFlax (dummy value = 2)

Discriminant analysis

0

0.5

1

1.5

2

2.5

3

Pred

icte

ddu

mm

yva

lue

Hay/Silage (dummy value = 1) Hay/SilageFlax (dummy value = 2)

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Highlights

> NIR spectra were collected on subcutaneous fat samples at the 12 th rib of 62 cows. >

Then, polyunsaturated fatty acids and biohydrogenation products were analysed. > We

found high predictability for most of the n-3, conjugated linolenic acids and CLA. >

Non-CLA dienes and trans-monounsaturated fatty acids were successfully predicted. >

NIR discriminated 100% of subcutaneous fats from cows fed with and without flaxseed.

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643644

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