Meat Science 98 (2014) 279–288

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Use of near infrared spectroscopy for estimating meat chemical composition, quality traits and fatty acid content from cattle fed sunflower or flaxseed N. Prieto a,b,⁎, Ó. López-Campos b,c, J.L. Aalhus b, M.E.R. Dugan b, M. Juárez b, B. Uttaro b a b c

Department of Agricultural, Food and Nutritional Science, University of Alberta, 4-10 Agriculture/Forestry Centre, Edmonton, Alberta T6G 2P5, Canada Lacombe Research Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, Alberta T4L 1W1, Canada Livestock Gentec, Edmonton, Alberta, T6G 2C8, Canada

a r t i c l e

i n f o

Article history: Received 5 June 2013 Received in revised form 4 April 2014 Accepted 3 June 2014 Available online 11 June 2014 Keywords: NIRS Meat quality Fatty acid Sunflower Flaxseed

a b s t r a c t This study tested the ability of near infrared reflectance spectroscopy (NIRS) to predict meat chemical composition, quality traits and fatty acid (FA) composition from 63 steers fed sunflower or flaxseed in combination with high forage diets. NIRS calibrations, tested by cross-validation, were successful for predicting crude protein, moisture and fat content with coefficients of determination (R2) (RMSECV, g · 100 g−1 wet matter) of 0.85 (0.48), 0.90 (0.60) and 0.86 (1.08), respectively, but were not reliable for meat quality attributes. This technology accurately predicted saturated, monounsaturated and branched FA and conjugated linoleic acid content (R2: 0.83–0.97; RMSECV: 0.04–1.15 mg · g−1 tissue) and might be suitable for screening purposes in meat based on the content of FAs beneficial to human health such as rumenic and vaccenic acids. Further research applying NIRS to estimate meat quality attributes will require the use on-line of a fibre-optic probe on intact samples. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction Today's health conscious consumers are willing to pay higher prices for value-added beef products with enhanced levels of fatty acids (FAs) beneficial to human health (Benjamin & Spener, 2009; Field, Blewett, Proctor, & Vine, 2009). Adding sunflower seeds or flaxseed to cattle diets presents opportunities for producing beef products with enhanced levels of FAs with potential health benefits such as conjugated linoleic acid (CLA, e.g. rumenic acid, cis(c)9,trans(t)11–18:2) and vaccenic acid (t11–18:1), as a result of bacterial biohydrogenation of polyunsaturated fatty acids (PUFAs) in the rumen (Basarab et al., 2007; Nassu et al., 2011). The amount and proportion of FAs in intramuscular fat are key factors that influence beef quality (Wood et al., 2003). On the one hand, intramuscular fat with a high proportion of saturated fatty acids (SFAs) can affect meat appearance since groups of fat cells containing solidified highly saturated fat with a high melting point appear whiter than fat with less saturation and a lower melting point, but diets high in SFAs are associated with increased levels of cardiovascular diseases (Hu & Willett, 2002). On the other hand, the susceptibility of unsaturated FAs to rapid oxidation, especially those containing more than two double bonds, increases the rate of rancidity development and colour ⁎ Corresponding author at: Lacombe Research Centre, 6000 C&E Trail, Lacombe, Alberta T4L 1W1, Canada. Tel.: +1 403 782 8176; fax: +1 403 782 6120. E-mail addresses: [email protected], [email protected] (N. Prieto).

http://dx.doi.org/10.1016/j.meatsci.2014.06.005 0309-1740/© 2014 Elsevier Ltd. All rights reserved.

deterioration of meat, as well as the flavour development during cooking and, therefore, the shelf life of the meat (Wood et al., 2003). Moreover, individual FAs have very different melting points, thus changes in proportions of FAs affect the firmness or softness of the fat, especially subcutaneous and intermuscular, but also the intramuscular fat, which, in turn may affect other characteristics of meat such as tenderness. Both FA composition and meat quality attributes are currently measured by means of relatively slow, destructive and often expensive methods. Nevertheless, characteristics such as colour and tenderness are important criteria that affect consumers' beef purchase decisions and overall satisfaction. Many countries are developing instrumental grading systems with automatic data capture (e.g. Canada, Europe). As these systems for data capture and flow mature, opportunities to measure quality characteristics at line speed will become extremely valuable. Hence, an urgent need has developed to find a fast and efficient alternative method to estimate these criteria, particularly in meat with enhanced levels of FAs with potential health effects. Near infrared reflectance spectroscopy (NIRS) is a rapid, objective and non-destructive method, neither requiring reagents nor producing waste, which provides information about the molecular bonds of organic compounds and tissue ultra-structure in a scanned sample (Downey & Hildrum, 2004), making it an ideal tool to study characteristics of meat. Indeed, NIRS has been successfully used to predict the chemical composition of meat, but the results for meat quality attributes have been inconclusive (Prieto, Roehe, Lavín, Batten, & Andrés, 2009a). The

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ability of NIRS to predict FA content in meat has also been tested; predictability being reliable for FA groups but less so for individual FAs (Prieto et al., 2011; Realini, Duckett, & Windham, 2004; Sierra et al., 2008). Therefore, the aim of this study was to test the ability of NIR spectroscopy as an early predictor of meat chemical composition, quality traits and some indicators or groups of FAs from cattle fed sunflower or flaxseed.

Proximate analyses were performed on LT trimmed of all external connective tissue and ground (Robot Coupe Blixir BX3; Robot Coupe USA Inc., Ridgeland, MS, USA). The grind was analysed for protein (Moser & Herman, 2011), moisture and fat content (Leffler et al., 2008) using CEM rapid analyser systems (Sprint Protein Analyzer Model 558000, Smart Turbo Moisture Analyzer Model 907990, and Smart Trac Fat Analyzer Model 907955; CEM Corporation, Matthews, NC, USA).

2. Material and methods

2.4. Fatty acid analysis

2.1. Animals and diets

From ground meat collected, 50 g were sampled and frozen until used for FA analysis according to Mapiye et al. (2012).

Sixty-four yearling British × Continental crossbred steers with final pre-slaughter body weights averaging 533 ± 44.0 kg were cared for according to Canadian Council on Animal Care guidelines (CCAC, 1993) and fed at the Lacombe Research Centre (Alberta, Canada). Steers were randomly assigned to one of four diets with a 70:30 forage:concentrate ratio (dry matter basis). The forage was either grass hay or red clover silage, the concentrate contained either flaxseed or sunflower seed added to provide 5.4% oil to the diet, and the remainder of the concentrate included barley and a supplement providing vitamin/mineral to meet or exceed requirements (NRC, 2000). Steers had ad libitum access to feed and water with 8 animals per pen, two pens per diet, and were slaughtered at an average of 205 days on test. During the study, one animal from the flaxseed and grass hay treatment was withdrawn due to lameness. 2.2. Slaughter and sample collection Animals were slaughtered at the Lacombe Research Centre federally inspected abattoir. At 24 h postmortem, the left carcass sides were knifesplit between the 12th and 13th ribs and objective colour measurements and pH were recorded. The left M. longissimus thoracis (LT) from each animal was removed from the carcass and a 5.0 cm steak from the posterior end was ground for collection of NIR spectra and stored at −80 °C for subsequent FA analysis. The remainder of the muscle was trimmed of all extraneous fat and individual 2.5 cm steaks were cut, vacuum packaged and aged at 2 °C until 16 days after slaughter. Following the ageing period, steaks were subjected to instrumental texture and proximate analyses. 2.3. Proximate and meat quality analyses At 24 h postmortem, following 20 min of exposure to atmospheric oxygen, meat colour was measured as CIE L* (brightness), a* (red– green axis) and b* (yellow–blue axis) (CIE, 1978) with a portable Minolta colorimeter CR-300 with Spectra QC-300 Software (Minolta Canada Inc., Mississauga, ON, Canada), and pH was recorded using a Hanna HI99163 pH meter equipped with a Hanna Smart electrode FC232 for meat (Hanna Instruments, Laval QC, Canada). Prior to shear force measurement, raw aged steaks were grilled (Garland Grill ED30B; Condon Barr Food Equipment Ltd., Edmonton, AB, Canada) to an internal temperature of 35.5 °C monitored using a temperature probe inserted into the mid-point of the steak (Hewlett Packard HP34970A Data Logger; Hewlett Packard Co., Boise, ID, USA), and turned and cooked to a final temperature of 71 °C. Upon removal from the grill, each steak was placed into a polyethylene bag, sealed and immediately immersed in an ice/ water bath to prevent further cooking. Steaks were then transferred to a 2 °C cooler and allowed to stand for a 24 h period. On removal from the bag, six cores, 19 mm in diameter, were removed parallel to the fibre grain. Peak shear force was determined on each core perpendicular to the fibre grain by means of a TA-XT Plus Texture Analyzer equipped with a 30 kg load cell and a Warner–Bratzler (WB) shear head running at a crosshead speed of 200 mm · min−1 and using Texture Exponent 32 Software (Texture Technologies Corp., Hamilton, MA, USA). WB shear force was recorded as the average of all six cores.

2.5. Spectra collection An aliquot of ground meat was placed in the ring cups of the NIRS machine with the help of a modified syringe in order to avoid air bubbles, and the cup was backed with thin black foam (Fig. 1). Subsequently, each meat sample was scanned 32 times over the range (400–2498 nm) using a NIRSystems Versatile Agri Analyzer (SY-3665-II Model 6500, FOSS, Sweden) benchtop equipment, and spectra averaged by the equipment software. Two meat samples per animal were scanned using two different cells, increasing the area of muscle scanned and reducing the sampling error (Downey & Hildrum, 2004). The two reflectance spectra were visually examined for consistency and then averaged, with the mean spectrum being used to predict meat chemical composition, quality traits and FA composition. 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, Port Matilda, MD). 2.6. Data analysis Calibration and validation of the NIRS data were performed using The Unscrambler® program (version 10.2, Camo, Trondheim, Norway). Two passes of elimination of outliers (H and T) were allowed, the number of outliers removed from the population being 2. Spectral data (n = 61) were subjected to multiplicative scatter correction (MSC; Dhanoa, Lister, Sanderson, & Barnes, 1994) and Standard Normal Variate and Detrend (SNV-D; Dhanoa, Lister, & Barnes, 1995) to reduce multicolinearity and the confounding effects of baseline shift and curvature on spectra arising from scattering effects due to physical effects. First or second-order derivatives, based on the Savitzky–Golay procedure (Naes, Isaksson, Fearn, & Davies, 2002), were applied to the spectra to heighten the signals related to the organic compounds of the meat samples (Davies & Grant, 1987). Partial least square regression type I (PLSR1) was used to predict chemical composition, quality attributes and FA content using NIR spectra as independent variables. Internal full cross-validation (leave one-out) was performed to avoid overfitting 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 accuracy of prediction was evaluated in terms of the coefficient of determination (R2) and root mean square error of cross-validation (RMSECV). 3. Results and discussion 3.1. Prediction of chemical composition and meat quality attributes Table 1 summarises the ranges, means, standard deviations (SD) and coefficients of variation (CV) of meat chemical composition and quality characteristics. The values found were similar to those indicated by Juárez et al. (2011) and Mapiye et al. (2013) in omega-3 enhanced beef. The CV was lowest for water (1.7%) and highest for intramuscular

N. Prieto et al. / Meat Science 98 (2014) 279–288

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Fig. 1. Meat sample processing before NIR spectra collection: aliquot of ground meat placed in the ring cup of the NIRS machine with the help of a modified syringe in order to avoid air bubbles, and the cup backed with thin black foam.

fat content (49.9%), whereas all meat quality traits except pH (1.7%) showed intermediate variability (6.5–23.1%). The statistical summary of calibration and prediction by crossvalidation is presented in Table 2. Accurate NIRS predictions were found for crude protein (CP, R2 = 0.85; RMSECV = 0.48% on a wet matter basis, RPD = 2.10), moisture (R2 = 0.90; RMSECV = 0.60%, RPD = 2.13) and intramuscular fat content (IMF, R2 = 0.86; RMSECV = 1.08% on a wet matter basis, RPD = 2.01). Many studies have shown the ability of NIR spectroscopy to predict the content of main chemical components in meat of different species, as reviewed in Prieto, Roehe, Lavín, Batten, and Andrés (2009a). In the present study, when mathematical treatments were applied to the spectra, NIRS predictability was at a higher level. Namely, the best mathematical treatment was based on the scatter correction standard normal variate and detrend combined with the first or second derivative (SNV-D + 1D/2D) (Fig. 2). It is known that the SNV-D treatment improves NIRS predictions by reducing multicolinearity and the confounding effects of baseline shift and curvature (Dhanoa et al., 1995). Furthermore, the use of derivatives not only reduces scattering effects but also highlights the signals related to organic compounds (Davies & Grant, 1987). In relation to objective colour measurements, although the variance explained by the model was higher than 80% for L* value, the root mean square error of cross-validation was still high (RMSECV = 1.27) as compared to SD, resulting in a ratio performance deviation (RPD = 1.69) not high enough to meet the requirements for analytical purposes (Williams, 2001, 2008). NIRS predictability was lower for a* and b* Table 1 Descriptive statistics for chemical composition and quality attributes of beef longissimus thoracis samples (n = 63).

Chemical components Crude protein (g · 100 g−1 wet matter) Moisture (g · 100 g−1) Intramuscular fat (g · 100 g−1 wet matter) Quality attributes Colour L* Colour a* Colour b* pH 24 h Shear force 16 d (kg) a b c

se: standard error. SD: standard deviation. CV: coefficient of variation.

Range

Mean ± sea

SDb

CVc(%)

18.2–22.9

20.3 ± 0.12

0.97

4.8

71.0–75.6 1.7–11.2

73.2 ± 0.15 4.3 ± 0.27

1.23 2.14

1.7 49.9

29.7–39.9 15.0–23.9 5.4–10.9 5.4–5.9 3.4–9.1

34.2 19.9 8.1 5.6 5.2

2.23 1.83 1.22 0.10 1.19

6.5 9.2 15.1 1.7 23.1

± ± ± ± ±

0.28 0.23 0.15 0.01 0.15

values (R2 = 0.71 and 0.77, RMSECV = 1.45 and 0.77, respectively), showing ratio performance deviations (RPD = 1.25 and 1.56, respectively) substantially lower than that considered in the literature as suitable for screening purposes (Williams, 2001, 2008). This agrees with Hoving-Bolink et al. (2005), Prieto, Andrés, Giráldez, Mantecón, and Lavín (2008) and De Marchi, Penasa, Cecchinato, and Bittante (2013), who found no reliable NIRS prediction equations for L*, a* and b* colour values in beef. In this study, since the spectral range included the visible region, more accurate predictions could have been expected. Nevertheless, the time elapsed between the objective colour measurements and NIRS analysis (approx. 5 h) could have reduced the reliability of these NIRS predictions. Indeed, during the time elapsed, the oxidation states of myoglobin pigments in meat would have been modified, thus giving rise to changes in colour. In addition to that, the grinding of meat samples for NIR spectra collection could have enhanced the rate of meat discoloration; hence, reflectance differences in the visible region between the spectra and the objective colour measurements could have resulted. This agrees with the results found by De Marchi et al. (2013), who found that meat quality traits were better predicted on intact than on ground samples. Indeed, successful predictions for L* colour value (R2 N 0.76, RPD N 2.0) were reported by Leroy et al. (2003) and Andrés et al. (2008), in beef, and Kapper, Klont, Verdonk, and Urlings (2012a), in pork, when NIR spectra were collected on intact samples. Likewise, Prieto, Ross, Navajas, Nute, Richardson, Hyslop, Simm and Roehe (2009b) found reliable NIRS predictions for L*, a* and b* values (R2 N 0.86, RPD N 2.02) when intact beef samples were scanned. The ability to estimate 24 h pH values on intact meat from NIR spectra on homogenized meat was poor (R2 = 0.73; RMSECV = 0.09; RPD = 1.14), which is in accordance with that found by Prieto et al. (2008) and De Marchi et al. (2013) in beef samples. The failure of NIRS to estimate pH value in this study could be due to the sample preparation (intact for reference method vs. ground for NIR spectra), since scanning the samples after grinding could reduce the precision of pH estimations due to a lack of information about the muscle structure i.e. light scattering properties in intact muscle tissue (Prieto et al., 2008). Additionally, when measuring pH on intact meat the pH values are mainly from lean, since pH measurements are taken in those homogenous parts of the muscle without apparent marbling. However, in the ground samples, pH values are from both lean and fat, so pH values from the same muscle could be different depending on the sample preparation. In this sense, ElMasry, Sun, and Allen (2012) showed how the pH values vary from sample to sample and from location to location in the same muscle depending on the marbling. Conversely, Cozzolino and Murray (2002) and Andrés et al. (2008) accurately predicted pH values in

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Table 2 Prediction of chemical composition and quality attributes of beef longissimus thoracis using visible and near infrared spectra collected on homogenized samples (n = 61).

Chemical components Crude protein (g · 100 g−1 wet matter) Moisture (g · 100 g−1) Intramuscular fat (g · 100 g−1 wet matter) Quality attributes Colour L* Colour a* Colour b* pH 24 h Shear force 16 d (kg) a b c d e f g h i

Mathematical treatment

Ta

R2b

RMSECc

RMSECVd

RPDe

SNV-Df + 2Dg

6

0.85

0.34

0.48

2.10

SNV-D + 2D SNV-D + 1Dh

6 5

0.90 0.86

0.39 0.83

0.60 1.08

2.13 2.01

None (Log 1/R) MSC + 1D MSCi 2D SNV-D

5 7 6 5 7

0.80 0.71 0.77 0.73 0.81

1.02 1.11 0.62 0.05 0.45

1.27 1.45 0.77 0.09 0.62

1.69 1.25 1.56 1.14 1.70

T: number of PLS terms utilized in the calibration equation. R2: coefficient of determination of calibration. RMSEC: root mean square error of calibration. RMSECV: root mean square error of cross-validation. RPD: ratio performance deviation calculated as SD/RMSECV. SNV-D: standard normal variate and detrend. 2D: second-order derivative. 1D: first-order derivative. MSC: multiplicative scatter correction.

beef samples (R2 = 0.81, 0.97; RPD = 2.11, 3.17; respectively), probably because of the use of an intact meat sample when scanned using NIRS. Nevertheless, Kapper et al. (2012a) were not successful in estimating pH in intact pork samples when NIRS measurements were taken in a laboratory setting (R2 = 0.39; RPD = 1.3), although Kapper, Klont, Verdonk, Williams, and Urlings (2012b) improved the NIRS predictability of pH in intact pork samples when NIRS measurements were collected under production plant conditions (R2 = 0.65; RPD = 1.7). This disagreement between results could be due to different ranges of pH reference data and repeatability of the reference method as well as differences between species. Regarding the prediction of shear force at 16 days ageing, R2 and RMSECV of 0.81 and 0.62 kg, respectively, were reported in this study after applying SNV-D to the raw spectra. Nevertheless, when comparing the RMSECV to SD of the reference samples, the RPD (1.70) was not high enough to meet the requirements for analytical purposes (Williams, 2001, 2008). This could, again, be attributed to the sample treatment prior to spectra collection, since the muscle structure and fibre arrangements are severely altered during grinding. Ripoll, Albertí, Panea, Olleta, and Sañudo (2008) and Prieto et al. (2008) also failed to predict WB shear force in beef (R2 = 0.74 and 0.45; RPD = 1.44 and 1.18; respectively). Conversely, Meullenet, Jonville, Grezes, and Owens (2004) found high

NIRS predictability for WB shear force in poultry meat samples (R2 = 0.92, RPD = 2.16), probably because in that study meat samples were scanned in the intact form instead of being homogenized. Nevertheless, Yancey, Apple, Meullenet, and Sawyer (2010) and De Marchi et al. (2013) did not observe accurate predictions in beef, even when collecting NIR spectra on intact samples. This suggests that more factors have influence on shear force prediction such as differences between species, different muscles, and heterogeneity of muscle tissue in beef, which can cause a high variability of WB shear force reference values within the same muscle, hence increasing the difficulty of NIRS prediction of this parameter. Nevertheless, the potential of NIR spectroscopy for online classification of beef carcasses for longissimus tenderness has been shown by Shackelford, Wheeler, King, and Koohmaraie (2012), who pointed out that this technology could facilitate tenderness-based beef merchandising systems.

3.2. Prediction of fatty acid composition Descriptive statistics for selected groups of FAs in homogenized beef LT samples are summarised in Table 3. The coefficients of variation ranged from 30 to 53%, except for PUFA content (CV = 14%).

Fig. 2. Visible and near infrared reflectance spectra from the ground lean samples from steers (n = 61) after the application of the standard normal variate and detrend combined with the second order derivative mathematical treatments.

N. Prieto et al. / Meat Science 98 (2014) 279–288 Table 3 Descriptive statistics for fatty acids (mg · g−1 muscle) in beef longissimus thoracis samples (n = 63).

SFAsd MUFAse PUFAsf BCFAsg Omega-3h CLAsi t,t-CLAj c,t-CLAk c9,t11-CLA cis-MUFAl trans-MUFAm t11-18:1

Range

Mean ± sea

SDb

CVc(%)

5.67–26.37 5.46–28.78 1.36–2.60 0.26–0.90 0.14–1.00 0.09–0.53 0.01–0.08 0.07–0.50 0.05–0.45 4.61–24.17 0.73–4.61 0.31–2.71

14.03 13.60 1.97 0.49 0.37 0.25 0.03 0.22 0.17 11.38 2.21 1.02

4.766 4.729 0.271 0.148 0.163 0.089 0.015 0.080 0.072 4.026 0.867 0.451

33.98 34.78 13.78 30.33 44.47 36.03 52.77 36.85 41.42 35.37 39.17 44.41

± ± ± ± ± ± ± ± ± ± ± ±

0.600 0.596 0.034 0.019 0.020 0.011 0.002 0.010 0.009 0.507 0.109 0.057

a

se: standard error. SD: standard deviation. c CV: coefficient of variation. d SFAs: saturated fatty acids. e MUFAs: monounsaturated fatty acids. f PUFAs: polyunsaturated fatty acids. g BCFAs: branched fatty acids: C15:0iso + C15:0ai + C16:0iso + 17:0iso + C17:0ai +18:0iso. h Omega-3: C18:3n − 3 + C20:5n − 3 + C22:5n − 3 + C22:6n − 3. i CLAs: conjugated linoleic acids. j t,t-CLA: t12,t14 + t11,t13 + t10,t12 + t9,t11 + t8,t10 + t7,t9 + t6,t8-CLA. k c,t-CLA: t12,c14 + c12,t14 + t11,c13 + c11,t13 + t10,c12 + t8,c10 + t7,c9 + c9, t11 + t9,c11-CLA. l cis-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. m trans-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. b

The best calibration equations for the FA composition in beef muscle samples, using the criteria of maximising the R2 and minimising the RMSECV, are shown in Table 4. As previously shown for chemical

283

composition parameters, the best results were found after applying the SNV-D together with the second order derivative for the majority of the FAs, agreeing with Sierra et al. (2008) that this most accurately predicted the FA content in beef. When FAs were grouped (Table 4), the calibration equation for SFAs and MUFAs showed accurate predictions (R2 = 0.97 and 0.75, RMSECV = 1.05 and 1.15 mg · g−1 muscle, RPD = 4.54 and 4.11, respectively), whereas the NIRS predictability for PUFA content was not reliable (R2 = 0.44, RMSECV = 0.21 mg · g−1 muscle, RPD = 1.18). This is probably due to the small range of PUFA content in our sample set (1.36– 2.60 mg · g− 1 muscle) as well as its low variability (CV = 13.78%, Table 3) and concentration (about 7% of total FAs). Nevertheless, since the NIRS predictability was high for SFA and MUFA contents, the concentration of PUFAs could be calculated as the difference between the total FAs predicted by NIRS (R2 = 0.97, RMSEC = 1.80 mg · g− 1 muscle, RMSECV = 2.09 mg · g−1 muscle, RPD = 4.70) and the sum of both SFA and MUFA contents. In general, most researchers have described accurate NIRS calibrations to estimate SFA and MUFA contents in meat (González-Martín, González-Pérez, Hernández-Méndez, & Álvarez-García, 2003; Pla, Hernández, Ariño, Ramírez, & Díaz, 2007; Realini et al., 2004; Sierra et al., 2008). For PUFA content, our results are in accordance with those reported by González-Martín, GonzálezPérez, Alvarez-García, and Gonzalez-Cabrera (2005) in pork, and Sierra et al. (2008) and Prieto et al. (2011) in beef. However, GonzálezMartín et al. (2003) and Pla et al. (2007) reported better predictions in pork and rabbit meat, respectively, than those obtained in this study, possibly because of the greater range in PUFA content in these species. Within the minor groups, branched fatty acids (BCFAs) were accurately predicted (R2 = 0.92, RMSECV = 0.05 mg · g−1 muscle, RPD = 3.02). In spite of their low presence in meat, the CV was high (30%). Sierra et al. (2008) also predicted BCFAs in beef, although their NIRS predictability was at a lower level than that found in the current study. On the contrary, total omega-3 FAs (n − 3 FAs) were not reliably predicted as indicated by the low R2 and RPD and high RMSECV obtained

Table 4 Prediction of fatty acid content of beef longissimus thoracis using visible and near infrared spectra collected on homogenized samples (n = 61).

SFAf MUFAg PUFAh BCFAi Omega-3j CLAk t,t-CLAl c,t-CLAm c9,t11-CLA cis-MUFAn trans-MUFAo t11–18:1 a

Mathematical treatment

Ta

R2b

RMSECc (mg · g−1 muscle)

RMSECVd (mg · g−1 muscle)

RPDe

1Dp SNV-Dq + 2Dr SNV-D SNV-D + 2D SNV-D + 2D SNV-D + 2D SNV-D + 2D 2D MSC + 2D SNV-D + 1D SNV-D + 2D SNV-D + 2D

4 4 4 4 5 3 5 3 6 4 3 4

0.97 0.96 0.44 0.92 0.61 0.83 0.77 0.82 0.84 0.95 0.82 0.79

0.88 0.98 0.18 0.04 0.08 0.04 0.006 0.03 0.02 0.88 0.37 0.18

1.05 1.15 0.21 0.05 0.12 0.04 0.009 0.039 0.038 0.99 0.42 0.25

4.54 4.11 1.18 3.02 1.36 2.28 1.56 2.08 1.95 4.07 2.10 1.81

T: number of PLS terms utilized in the calibration equation. R2: coefficient of determination of calibration. c RMSEC: root mean square error of calibration. d RMSECV: root mean square error of cross-validation. e RPD: ratio performance deviation calculated as SD/RMSECV. f SFAs: saturated fatty acids. g MUFAs: monounsaturated fatty acids. h PUFAs: polyunsaturated fatty acids. i BCFAs: branched fatty acids: C15:0iso + C15:0ai + C16:0iso + 17:0iso + C17:0ai + 18:0iso. j Omega-3: C18:3n − 3 + C20:5n − 3 + C22:5n − 3 + C22:6n − 3. k CLA: conjugated linoleic acids. l t,t-CLA: t12,t14 + t11,t13 + t10,t12 + t9,t11 + t8,t10 + t7,t9 + t6,t8-CLA. m c,t-CLA: t12,c14 + c12,t14 + t11,c13 + c11,t13 + t10,c12 + t8,c10 + t7,c9 + c9,t11 + t9,c11-CLA. n cis-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. o trans-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. p 1D: first-order derivative. q SNV-D: standard normal variate and detrend. r 2D: second-order derivative. b

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for the prediction equation (R2 = 0.61, RMSECV = 0.12 mg · g−1 muscle, RPD = 1.36). When plotting the n − 3 FA concentration predicted by NIR spectroscopy against that obtained by chemical analysis (Fig. 3), a

lack of homogeneous distribution is evident along the regression. As a predictive procedure, NIR spectroscopy is not independent of the disadvantages arising from the samples used for calibration, therefore,

Fig. 3. Relationship between reference values of some fatty acids and values predicted by NIR spectroscopy on ground beef samples.

a)

N. Prieto et al. / Meat Science 98 (2014) 279–288 Fig. 4. Regression coefficient plots resulting from a partial least squares regression analysis of visible and near infrared spectra when predicting a) chemical composition and quality attributes and b) fatty acid content in ground lean samples from steers. The absolute value of the regression coefficient at each wavelength shows the influence of each wavelength on the prediction equation.

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b)

Fig. 4. (continued).

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a wide range together with a homogeneous spread of reference values is required to maximise the predictability of NIR spectroscopy. The steers used in the present study were part of an ongoing study evaluating the effects of oilseed (sunflower seed or flaxseed) and forage type (hay or red clover silage) on the enrichment of healthy FAs in beef; therefore, the lack of a true control group (no oilseed) might have prevented a wider range of n − 3 FAs. The low predictability found in our study for n − 3 FAs is in agreement with that reported by previous authors for either total or individual n − 3 FAs in beef (Prieto et al., 2011; Sierra et al., 2008) and in rabbit meat (Pla et al., 2007). Conversely, Realini et al. (2004) observed high NIRS predictability for the content of C18:3 n − 3 in ground beef (R2 = 0.95, RMSECV = 0.05 mg · g−1 muscle, RPD = 4.06). It has to be noted that the range of n − 3 FA content in their study was wider than that observed in the other studies and the samples were probably homogeneously distributed along the n − 3 FA range. The total CLA content was accurately predicted by NIRS, with the prediction equation showing a coefficient of determination of 0.83 and a low standard error of cross-validation (RMSECV = 0.04 mg · g− 1 muscle) compared to SD for this FA. Consequently, the RPD statistic (2.28) met the requirements for analytical purposes. This finding is of great interest due to the potential health benefits of CLA and, accordingly, their increasing importance from the consumer point of view accordingly (Belury, 2002; Ip, Masso-Welch, & Ip, 2003). For specific CLAs, NIRS predictability was acceptable for predicting c,t-CLA (R2 = 0.82, RMSECV = 0.039 mg · g−1 muscle, RPD = 2.08) content but not for t,t-CLA (R2 = 0.77, RMSECV = 0.009 mg · g−1 muscle, RPD = 1.56) content in spite of its high CV (52.77%, Table 3). A narrow range of variability for t,t-CLA (0.01–0.08 mg · g−1 muscle, Table 3) together with a low presence in meat (0.03 mg · g−1 muscle on average, Table 3) could have made their prediction by NIRS difficult. Evaluation of the PUFA c9,t11-CLA content (rumenic acid) indicated that NIRS could be used as an approximate predictor (R2 = 0.84, RMSECV = 0.038 mg · g−1 muscle, RPD = 1.95). The rapid and approximate estimation of rumenic acid content in lean beef by NIRS would be very valuable due to its purported roles in the prevention and possible treatment of several diseases including diabetes, obesity and some types of cancer (Belury, 2002; Ip et al., 2003). The results of the current study were slightly better than those previously reported by Prieto et al. (2011) for rumenic acid content in beef (R2 = 0.71, RPD = 1.8) and much better than those found by Sierra et al. (2008), who did not find accurate NIRS predictions for either total CLA (R2 = 0.587, RPD = 1.55) or rumenic acid content (R2 = 0.613, RPD = 1.52) in beef. Regarding MUFAs, cis-MUFA content was successfully predicted by NIRS (R2 = 0.95, RMSECV = 0.99 mg · g− 1 muscle, RPD = 4.07). Although the NIRS predictability for trans-MUFA content was not as high, it was acceptable (R2 = 0.82, RMSECV = 0.42 mg · g−1 muscle, RPD = 2.10). When using NIR spectra to predict individual MUFAs such as t11–18:1 (vaccenic acid), the variance explained by the model was about 80%, although the standard error of cross-validation (RMSECV = 0.25 mg · g−1 muscle) was still relatively high when compared to the standard deviation after eliminating two outliers (SD = 0.443). Hence, the ratio performance deviation (RPD = 1.81) did not meet the requirements for analytical purposes, albeit the prediction equation could be suitable for screening purposes. The ability of NIRS to predict the vaccenic acid content reported in this study was similar to that shown previously by Prieto et al. (2011) (R2 = 0.70, RPD = 1.80) in beef, and more accurate than that reported by Pla et al. (2007) (R2 = 0.33, RPD = 1.21) in rabbit meat, which was not reliable. Taking into consideration the potential human health-related benefits of vaccenic acid as the precursor for rumenic acid, discriminating meat samples by NIRS according to their vaccenic acid proportion could be of great interest. NIRS technology has recently been shown to be successful for the discrimination of steer fat samples based on vaccenic acid content (Prieto, Dugan, López-Campos, Aalhus, & Uttaro, 2013).

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3.3. Regression coefficients of the prediction equations As shown in Fig. 4, the wavelengths showing the highest regression coefficients for the prediction of protein and moisture content were related to the absorption of N\H (2055 and 2570 nm) and O\H bonds (1450 and 1940 nm) (Murray & Williams, 1987; Shenk, Westerhaus, & Workman, 1992), respectively. Additionally, some regression coefficients at wavelengths where C\H bonds absorb energy (1215, 1395, 1670 and 1765 nm) were observed. This is in agreement with that reported by Prieto, Andrés, Giráldez, Mantecón, and Lavín (2006), who indicated analytically useful regions of the spectra related to the C\H bonds for the protein and moisture content prediction by NIRS. As expected for the prediction of the fat content, the wavelengths showing the highest regression coefficients were related to the absorption of C\H bonds (1215, 1725 and 2310 nm). Conversely, the regression coefficients observed for the prediction of quality attributes (colour L*, a* and b* values and pH) were not well defined or seemed to be noisy, hence there were no reliable predictions of these traits by NIRS. For the estimation of the shear force value, it has to be noted that the highest regression coefficients were related to the absorption of C\H bonds (1124 and 1360 nm). Regarding the prediction models for the FA content, the wavelengths showing the highest regression coefficients were related to the absorption of C\H bonds (1215, 1725 and 2310 nm) for all FAs, except for PUFAs and n − 3. For the latter, the regression coefficients were not well defined and noisy, hence the unsuccessful prediction of PUFAs and n − 3 content by NIRS. 4. Conclusion NIR spectroscopy was successfully employed to predict the chemical composition of meat from steers fed sunflower or flaxseed, although the prediction ability for meat quality attributes was limited, probably because of collecting NIR spectra on ground samples and the time elapsed between the reference methods and NIRS analyses. NIRS prediction was accurate for SFA, MUFA (total, cis- and trans-MUFA), BCFA and CLA (total and cis,trans-CLA) contents, but not reliable for PUFAs and omega-3 although better predictions might be found in other species with a greater range of these FAs. This technology might be suitable for discrimination purposes in beef based on the content of fatty acids beneficial to human health such as rumenic and vaccenic acids. Further research applying NIRS to estimate meat chemical composition, quality traits and fatty acid composition in beef would logically be with intact meat, either using benchtop equipment on excised samples or, ideally, with portable equipment directly on the carcass. The latter, if successful, would also have evident advantages regarding the possible speed of the analyses. Acknowledgements Financial support received from the AAFC Peer Review Program (1821) and the Alberta Meat and Livestock Agency (2012R20R) is gratefully acknowledged. Drs. N. Prieto and Ó. López-Campos thank the Alberta Crop Industry Development Fund (2011C313F) and Livestock Gentec, the Canadian Beef Grading Agency and the Alberta Livestock and Meat Agency (2012S008S) for their funding support, respectively. The authors also thank the Lacombe Research Centre operational, processing and technical staff for their dedication and expert assistance. References Andrés, S., Silva, A., Soares-Pereira, A. L., Martins, C., Bruno-Soares, A.M., & Murray, I. (2008). The use of visible and near infrared reflectance spectroscopy to predict beef M. Longissimus thoracis et lumborum quality attributes. Meat Science, 78, 217–224. Basarab, J. A., Mir, P.S., Aalhus, J. L., Shah, M.A., Baron, V. S., Okine, E. K., & Robertson, W. M. (2007). Effect of sunflower seed supplementation on the fatty acid composition of muscle and adipose tissue of pasture-fed and feedlot finished beef. Canadian Journal of Animal Science, 87, 71–86.

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