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Prediction of beef meat fatty acid composition by visible-near-infrared spectroscopy was improved by preliminary freeze-drying
Meat Science 158 (2019) 107910
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Prediction of beef meat fatty acid composition by visible-near-infrared spectroscopy was improved by preliminary freeze-drying
T
D. Anduezaa, , A. Listrata, D. Duranda, J. Normandb, B.P. Mourotc, D. Gruffata ⁎
a
Université Clermont Auvergne, INRA, VetAgro Sup, UMR Herbivores, 63122 Saint-Genès-Champanelle, France Institut de l'Elevage, Service Qualité des Viandes, 23 Rue Jean Baldassini, F-69007 Lyon, France c Valorex, La Messayais, F-35210, Combourtillé, France b
ARTICLE INFO
ABSTRACT
Keywords: Visible-near-infrared spectroscopy Beef Fatty acids Freeze-dried samples Fresh samples
The aim of this study was to compare visible-near-infrared spectroscopy (VIS/NIRS) models developed from fresh or freeze-dried samples for predicting the fatty acid (FA) composition of beef samples. The hypothesis tested is that the removal of water from samples could improve the VIS/NIRS model performance. A total of 454 beef samples obtained from different bovine muscles were used. No significant differences were found in the performance of VIS/NIRS models developed from fresh or freeze-dried samples for predicting both major individual FAs and families of FAs and for some FAs (16:0, 18:0, 18:1 n-9, 18:2 n-6, 20:4 n-6, 22:5 n-3, 22:6 n-3, saturated, mono-unsaturated FA, and total n-3 long chain poly-unsaturated FAs (PUFA)). In contrast, the standard error of predictions for total PUFAs, total n-3 PUFAs, total conjugated linoleic acid, 20:5 n-3, and 18:3 n-3 were improved (by 21% on average; P < .05) in freeze-dried samples compared with fresh samples.
1. Introduction Meat is composed of water (75%), proteins, and amino acids (23%), fatty acids (1.8%), cholesterol, phospholipids, minerals, vitamins, etc. The nutritional value of meat is related to its composition in terms of amino acids, fatty acids (FAs), minerals, and vitamins. However, an excess of red meat in the diet is associated with health problems such as hypertension, heart disease, and cancer (Barnard, Nicholson, & Howard, 1995; McAfee et al., 2010; Surya et al., 2016). These diseases are mostly related to the FA composition of meats. In general, to try to decrease their impact on human health, lower saturated FA (SFA) and higher polyunsaturated FA (PUFA) intake are recommended, especially higher n-3 PUFA. Therefore, it is essential to inform consumers, whose demands regarding the nutritional quality of meat are increasing, of its FA profile. The reference method currently used for determining the FA profile of meat is gas chromatography (GC). However, this method is costly and time consuming, produces chemical wastes that are necessary to eliminate, and is difficult to use on a large scale. In contrast, near-infrared spectroscopy (NIRS) is an indirect technology that is nondestructive, multiparametric, and usable on a large scale. In the recent past, there has been a generalized study about the potential use of NIRS as an alternative technology for predicting some quality parameters or for the authentication (Andueza, Agabriel, Constant, Lucas, & Martin, 2013; Huang, Andueza, de Oliveira, Zawadzki, & Prache, 2015) ⁎
of several products. In meats, some authors have shown the ability of NIRS to predict the FA contents (Berzaghi, Dalle Zotte, Jansson, & Andrighetto, 2005; Guy, Prache, Thomas, Bauchart, & Andueza, 2011; Mourot et al., 2015; Perez-Marin, Sanz, Guerrero-Ginel, & GarridoVaro, 2009; Sierra et al., 2008). However, the prediction of minor FAs such as PUFA families and individual PUFAs in beef is difficult (Mourot et al., 2015; Sierra et al., 2008). According to these authors, the narrow variability of these FAs could contribute to explaining the difficulty of obtaining adequate models for predicting minor FAs that are important for human health. Water is a strong absorber in the infrared region (Büning-Pfaue, 2003), and the high content of water in meat (75%) (Wood, 2017) could influence the results of NIRS predictions (Thyholt & Isaksson, 1997), limiting the detection of some minor constituents. Furthermore, the absorbance of water is temperature dependent, which could increase the error of NIRS models developed from samples characterized by high moisture content if the spectra are been obtained in very standard conditions. Therefore, we hypothesized that removing water before taking the NIRS spectra could increase the precision of the models for predicting some minor important FAs and some individual PUFAs or families of PUFAs. For this purpose, the objective of the current research is to compare visible-near-infrared spectroscopy (VIS/ NIRS) models for predicting FAs in fresh and freeze-dried beef.
Corresponding author. E-mail address: [email protected] (D. Andueza).
https://doi.org/10.1016/j.meatsci.2019.107910 Received 20 May 2019; Received in revised form 24 July 2019; Accepted 7 August 2019 Available online 08 August 2019 0309-1740/ © 2019 Elsevier Ltd. All rights reserved.
Meat Science 158 (2019) 107910
D. Andueza, et al.
2. Materials and methods
2.4. Calibrations and statistics
2.1. Samples
Calibrations were developed using WinISI II version 1.60 (Infrasoft International). The samples were divided into calibration (n = 344) and validation (n = 110) sets by the application of the Kennard-Stone algorithm (Kennard & Stone, 1969) to the pool of fresh samples. The modified partial least squares regression method was used to obtain VIS/NIRS equations for all the studied parameters. To optimize the regression models, the data were subjected to standard normal variate and detrending corrections (Barnes, Dhanoa, & Lister, 1989) and firstderivative treatments (1,4,4), where the first digit is the number of the derivative, the second is the gap over which the derivative is calculated, and the third is the number of data points in the first smoothing. The best treatment was selected for each constituent based on the highest coefficient of determination in calibration (R2C) and the lowest standard error of calibration (SEC). During the calibration process, samples were considered outliers if the residual between the reference method and the predicted value was > 2.5-fold greater than the SEC (t = 2.5). Two passes of elimination were allowed. Upon completion of the calibration, the models were applied to the validation set. First, the spectral similarity between each sample in the validation test and the center of those in the calibration set was tested through the application of the standardized Mahalanobis distance (H). If the H-values of some samples were higher than 3, according to Shenk and Westerhaus (1994), these samples were considered outliers. The validation performance of each model was assessed by the coefficient of determination in external validation (R2V), the standard error of prediction (SEP), the bias, and the SEP corrected by the bias (SEP(C)). The bias and (SEP(C)) for the fresh and freeze-dried models obtained for the same determinations were compared according to the procedure proposed by Fearn (1996). In this way, biases were compared using a t confidence interval for paired samples whereas SEP(C) values were compared by calculating an interval for the ratio of standard errors. Calculations were performed using Excel 2016.
The beef samples used in this study were taken from two experiments carried out in our laboratory and conducted in a manner compatible with the national legislation on animal care (the slaughterhouse and experimental facility license numbers are #63345 01 and #63345.17, respectively). A total of 454 samples of beef, selected for wide compositional variability, were used. The beef samples used included diaphragma, infraspinatus, longissimus thoracis, and rectus abdominis muscles and chopped steaks. Samples (~ 125 g) were obtained 24 h or 48 h post mortem, cut into small cubes (approximately 1 cm3), and frozen in liquid nitrogen at −80 °C before being ground in liquid nitrogen with a mixer grinder to obtain a fine and homogenous powder (Retch MM 301, Hann Germany). Each sample was then divided into four subsamples. Three of these subsamples were subjected to lipid GC analysis, FA GC analysis and VIS/NIRS scanning, respectively, as fresh meat. The fourth sample was freeze-dried for 96 h using a freeze-dryer (Cryotec, Saint-Gély du-Fesc, France), ground again and then stored at +4 °C in plastic tubes protected from light until VIS/NIRS scanning. 2.2. Lipids and fatty acid analysis The total lipids were extracted according to the method of Folch, Lees, and Sloane Stanley (1957) by mixing the muscle powder with a 2/ 1 chloroform/methanol mixture (vol/vol) and quantified by gravimetry. Fatty acid extraction and transmethylation into fatty acid methyl esters (FAME) were subsequently performed according to the methods of Bauchart, Durand, Scislowski, Chilliard, and Gruffat (2005). Fatty acid methyl ester analysis was performed with GC using a Peri 2100 chromatography system (Perichrom Society, Saulx-les-Chartreux, France) fitted with a CP-Sil 88 glass capillary column (Varian, Palo Alto, CA; length = 100 m; diam. = 0.25 mm). The carrier gas was H2, and the oven and flame ionization detector temperatures described by Scislowski, Durand, Gruffat, and Bauchart (2004) were used. Total FAs was quantified using C19:0 as an internal standard. The identification of each individual FAME and the calculation of the response coefficients for each individual FAME were performed using the quantitative mix C4-C24 FAME (Supelco, Bellafonte, PA). Fatty acids and families of FAs determined were the following: Total FAs, 16:0, 18:0, 18:1 n-9, 18:2 n6, 18:3 n-3, 20:4 n-6, 20:5 n-3, 22:5 n-3, 22:6 n-3, total SFAs, total mono-unsaturated FAs (MUFAs), total cis-MUFAs, total trans-MUFAs, total conjugated linoleic acids (CLAs), total n-3 PUFAs, total n-3 long chain (LC) PUFAs, and total PUFAs.
3. Results 3.1. Fatty acid composition The mean, standard deviation, minimum, and maximum values for total lipids, total FAs, some individual FAs and families of FAs for the calibration and validation sets are presented in Table 1. The calibration and validation sets covered a broad and similar minimum and maximum intervals for each component. Oleic (18:1 n-9) and palmitic (16:0) acids were the most abundant FAs. Stearic acid (18:0) averaged 966 and 879 mg/100 g in the calibration and validation sets, respectively. The concentrations of SFAs and MUFAs of the pool of samples were 2550 and 2270 mg/100 g of fresh muscle, but the concentration of mean PUFAs was approximately six times lower (407 and 393 mg/100 g of fresh muscle in the calibration and validation populations, respectively). The largest differences between the two sample sets were found for C22:6 n-3 (difference between coefficients of variation associated to calibration and validation sets was of about 0.12). For the other determinations, these differences were lower than 0.10.
2.3. Visible-near-infrared spectroscopy Before scanning, fresh meat samples were thawed at laboratory temperature for 30 min. Then, approximately 20 g of ground fresh meat or 13 g of ground freeze-dried meat (Dubost et al., 2013) was placed in a 50 mm diameter ring cup (approximately 10 mm deep; part number IH - 0307, NIRSystems, Infrasoft International, South Atherton St. State College, PA 16801, USA), compressed and sealed with a disposable paper-backed wrap. They were then scanned in reflectance mode at 2 nm intervals from 400 to 2498 nm using a Foss NIRSystems model 6500 scanning visible-near-infrared spectrometer equipped with an auto cup module (Foss NIRSystems, Silver Spring, MD, USA) and controlled by ISIscan software version 2.21 (Infrasoft International, Port Matilda, PA, USA). Each spectrum was time averaged from 32 scans. The reflectance (R) values were converted into absorbance (A) values using the formula A = log (1/R).
3.2. Meat spectra The average absorbance data of the total population and average second derivative spectra for fresh and freeze-dried spectra are shown in Fig. 1. The absorbance values for fresh samples were higher than those observed for freeze-dried samples (Fig. 1a). The spectra of fresh samples were characterized by absorbance maxima at 410, 540, 950,
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and R2V equal or higher than 0.90 were found for models developed for total lipids, total FAs, 18:0, 18:1 n-9, total SFAs, total MUFAs, and total cis-MUFAs for both the fresh and freeze-dried spectra. Furthermore, when freeze-dried samples were used, R2C for total trans-MUFAs was 0.91 and R2V was 0.79 (Table 2) whereas for 16:0, R2C was higher than 0.90 when both types of samples were used but R2V were 0.86 and 0.88 when fresh and freeze-dried samples were used. For total PUFAs, the R2C values were 0.78 and 0.82 and the R2V values were 0.71 and 0.78 for fresh and freeze-dried samples, respectively. For the other FAs, the R2C and R2V values were lower than 0.8 for both fresh and freeze-dried samples. The decomposition of SEP into bias and SEP(C) for each determination when both fresh and freeze-dried samples were used is given in Table 3. For all determinations, bias was lower than SEP(C). Differences (P < .05) for biases between fresh and freeze-dried samples were found for total FAs, 18:1 n-9, 20:5 n-3, 22:6 n-3, total MUFAs, total cis MUFAs, and total n-3 PUFAs. For these determinations, the biases for fresh samples were higher than those obtained for freeze-dried samples. For SEP(C), significant differences (P < .05) between models built from fresh and freeze-dried samples were found for 18:3 n-3, 20:5 n-3, total CLAs, total n-3 PUFAs, and total PUFAs. For these FAs and sums of FAs, the SEP(C) values were lower for models developed from freezedried samples than for those developed from fresh samples. No significant differences (P > .05) were found for the other values determined.
Table 1 Descriptive statistics for total lipid and fatty acid contents (g/100 g fresh muscle) in beef samples used in the databasea. Calibration (n = 374)
Validation (n = 80)
Constituent
Mean (SD)
Min-Max
Mean (SD)
Min-Max
Total lipids (g/100 g FM) Total FAs (g/100 g FM)
5.7 (4.11)
0.08–18.9
5.29 (3.77)
0.08–14.11
5.5 (4.00)
0.48–18.0
5.1 (3.47)
0.58–16.3
78.4–5338
1287 (897)
101–4051
57.3–4039 87.0–6361
879 (738) 1731 (1212)
89–3086 123–5339
62.1–604 6.10–147 11.9–111.1 0.00–45.9 0.00–80.2 0.00–10.5
189 (101) 37.6 (26.7) 42.6 (19.5) 8.24 (7.08) 23.6 (15.8) 1.00 (1.93)
62.0–502 6.35–128 11.9–115 0.00–39.0 0.00–86.8 0.00–11.3
159–9129
2435 (1804)
216–8116
166–8122
2181 (1524)
185–7019
133–7853
2076 (1447)
163–6526
10.0–719 0.00–140 9.78–295 0.00–147.8 149–1056
105(111) 25.4 (22.6) 74.0 (50.4) 36.4 (28.0) 393 (188)
8.4–545.6 1.9–114 10.4–264 0.00–149 148–955
Individual FAs (mg/100 g FM) 16:0 1396 (1066) 18:0 966 (806) 18:1 n-9 1853 (1405) 18:2 n-6 196 (96.1) 18:3 n-3 38.8 (25.1) 20:4 n-6 42.3 (17.6) 20:5 n-3 7.50 (7.05) 22:5 n-3 22.2 (14.3) 22:6 n-3 0.84 (1.82) Fatty acid families (mg/100 g FM) Total SFAs 2663 (2075) Total MUFAs 2352 (1800) Total cis-MUFAs 2229 (1701) Total trans-MUFAs 122 (135) Total CLAs 26.6 (25.8) Total n-3 PUFAs 72.4 (43.9) Total n-3 LC PUFAs 33.8 (25.3) Total PUFAs 407 (179)
4. Discussion The standard deviation and the minimum and maximum values of FA contents in the population samples used in the current study are representative of the beef FA content according to the literature (Berthelot & Gruffat, 2018). However, the variability in the data used in the current study was similar or higher than that reported by Mourot et al. (2015) for most FAs, with the exception of 22:6 n-3 PUFA. This high variability for lipids and most of the FAs can be explained by the use in the present study of various bovine breeds, sex, and ages varying in lipogenesis capacities and by the use of various types of muscles. Animals were also fed different diets. The lack of differences in the variability for 22:6 n-3 PUFA could be explained by the low content of this FA and because it is one of the FAs characterized by being more rapidly biohydrogenated in the rumen (Lock, Harvatine, Drackley, & Bauman, 2006). Consequently, the difficulty of finding differences in the variability between databases increases. The ability of NIRS to predict total lipid and FA contents in beef has already been reported by Mourot et al. (2015), De Marchi, Berzaghi, Boukha, Mirisola, and Galol (2007) and Tøgersen, Arnesen, Nilsen, and Hildrum (2003). Therefore, Mourot et al. (2015) reported relationships characterized by R2 > 0.90 for the SFA and MUFA families and for the most abundant individual SFAs and MUFAs (14:0, 16:0, 16:1 cis9, 18:1 cis9, 18:1 cis11). However, for the PUFA family and individual PUFAs, these authors reported NIRS models characterized by R2 < 0.62, as also reported by Cecchinato et al. (2012), Realini, Duckett, and Windham (2004) and Sierra et al. (2008). Realini et al. (2004) suggest that the failure to precisely determine these individual FAs could be related to similarities in their NIRS absorption patterns, as these FAs have the same absorbing molecular group (–CH2), and to the narrow standard deviation of the data sets. According to Andueza, Mourot, Hocquette, and Mourot (2017), this last explanation seems the most likely because other authors working with lamb meats (Guy et al., 2011), chicken (Berzaghi et al., 2005), and pork (García-Olmo et al., 2000) reported prediction models for PUFAs with R2 values higher than 0.88, which were associated with wider variability. These studies were carried out using fresh
Total SFAs: 12:0 to 24:0; Total cis-MUFAs: 14:1 Δ9cis + 15:1 Δ9cis + 16:1 Δ9cis + 17:1 Δ8 and Δ9cis + 18:1 Δ6cis to Δ15cis + 20:1 Δ9cis + 22:1 Δ9cis; Total trans-MUFAs: 16:1 Δ9trans + 18:1 Δ6 to Δ16trans; total MUFAs: cis + trans MUFAs; Total n-3 PUFAs: 18:3 n-3 + 20:3 n-3 + 20:4 n- 3 + 20:5 n3 + 22:3 n-3 + 22:4 n-3 + 22:5 n-3 + 22:6 n-3; Total n-3 LC PUFAs: 20:5 n3 + 22:5 n-3 + 22:6 n-3;Total CLAs: 9cis,11trans-CLA + 11cis,13transCLA + total cis,cis CLAs + total trans,trans CLAs; Total PUFAs: n-6 PUFAs + n3 PUFAs + CLAs. a Abbreviations: FAs, fatty acids; FM, fresh muscle; Min, minimum; Max, maximum; SD, standard deviation; SFAs, saturated FAs; MUFAs, mono-unsaturated FAs; n, number of samples; PUFAs, poly-unsaturated FAs; CLA, conjugated linoleic acid; LC, long chain.
1180, 1450, and 1940 nm, whereas a higher number of absorption bands characterized the average freeze-dried spectra (410, 540, 950, 1180, 1450, 1740, 1940, 2070, 2180, 2320, 2360, and 2470 nm). After normalization and scatter correction (Fig. 1b), most of the absorption bands were similar for both processing types (maxima at 1650, 1740, 1770, 2010, 2080, 2230, 2250, 2320, and 2360 nm). However, the spectrum of average freeze-dried samples showed higher maxima and lower minima (particularly in the area between 1450 and 2440 nm) than those observed in the spectrum of average fresh samples. The maxima of the second derivative fresh sample spectrum at 500, 600, 940, 1128, 1400, and 1850 nm were higher than those of the freezedried sample spectrum. 3.3. Near-infrared models for the prediction of fatty acid concentrations in fresh and freeze-dried meat samples The statistics related to the calibration models and the statistics obtained when the calibration models were applied to the validation sets are given in Table 2. Coefficients of determination in calibration
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2.5
log (1/R)
2 1.5 1 0.5 0 400
900
1400
1900
2400
1900
2400
Wavelengths Freeze-dried
Fresh
0.6
2nd derivative
0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 400
900
1400
Wavelengths Freeze-dried
Fresh
Fig. 1. Average log (1/R) (a) and second derivative (b) of visible and near-infrared spectrum for fresh and freeze-dried beef muscle samples.
and homogenized meat samples. In general, fresh conditions were chosen to minimize the manipulations of samples, and homogenization was chosen because it is an important condition for obtaining good relationships between NIRS spectra and laboratory data (Prieto, Andres, Giraldez, Mantecon, & Lavin, 2006; Tøgersen et al., 2003). Fresh meat spectra were characterized by the presence of absorbance bands relative to water and by the influence of water on the absorbance of other compounds. Indeed, according to Büning-Pfaue (2003) and (Osborne & Fearn, 1988), maxima at 940, 1128, 1400, and 1850 nm were observed with fresh meat, whereas the spectra obtained using freeze-dried meat samples were characterized only by a maximum at 1860 nm. The band shift in this interval (1850–1860 nm) between the two types of sample presentation can be attributed to the effect of the interaction between water and solutes (Büning-Pfaue, 2003; Giangiacomo, 2006). A shift band caused by the interaction of water and solutes could also explain the observed band at 1128 nm in the fresh spectrum, whereas Osborne and Fearn (1988) reported an absorption band for water at 1190 nm. In the current study, the spectra of fresh samples were similar to those reported by Cecchinato et al. (2012) and Sierra et al. (2008). However, when most water was removed throughout the freeze-drying process, the SEP values for PUFA family, total n-3 PUFAs, total CLAs, and some individual PUFAs (18:3 n-3, and 20:5 n-3) were improved by 21% on average. These results showed that freeze-drying significantly improved the precision of NIRS prediction models for FAs that were present at concentrations between 20 and 400 mg/100 g of muscle
although for some FAs or families of FAs such as 20:4 n-6, total transMUFA, ant total n-3 LC PUFAs no improvement was observed. In contrast, when FA concentrations were higher than 400 mg/100 g or lower than 20 mg/100 g, no difference was observed between sample presentation types. These results could be explained by the higher intensity of absorbance values in freeze-dried samples than in fresh samples. In this way, the intensity of the signal obtained with fresh samples was not limiting for predicting FAs present in quantities higher than 400 mg/ 100 g of muscle. However, for predicting most FAs present in muscles in smaller quantities, between 20 and 400 mg/100 g of muscle, the higherintensity signal obtained with freeze-dried samples, particularly between 1500 and 2500 nm, could be a major asset for improving the precision of NIRS prediction models. For FAs present at concentrations lower than 20 mg/100 g of muscle, the significant improvement obtained when freeze-dried samples were used was not systematic. Consequently, these results suggest that NIRS seems unable to detect concentrations lower than 20 mg/100 g of muscle even when most of the water was eliminated in the freeze-drying process. Sierra et al. (2008) and Mourot et al. (2015) suggest that the NIRS prediction results for PUFAs would be improved by using population samples characterized by wide variability. However, this variability is difficult to obtain when working on beef meats because a great part of PUFAs ingested by animals is saturated by rumen biohydrogenation, limiting PUFA variability in muscles and meats. In these conditions, removing water from samples, even though this process has a cost and is laborious, could be an interesting alternative for predicting PUFAs in beef by VIS/NIRS.
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Table 2 Statistical parameters of visible-near-infrared spectroscopy models for fatty acids using ground fresh and freeze-dried beef samples from the calibration and validation databasesa. Fresh
Table 3 Bias and standard error of prediction corrected for bias (SEP(C)) for fatty acids (FAs) obtained when each sample of the validation set was predicted using the visible-near-infrared spectroscopy calibration model obtained for each determinationc.
Freeze-dried 2
2
Constituent
T
SEC
R C
SEP
R V
T
SEC
R2C
SEP
R2V
Total lipids Total FAs
5 8
0.81 0.85
0.96 0.95
1.00 1.16
0.93 0.90
9 8
0.80 0.90
0.96 0.94
1.01 1.09
0.93 0.90
Individual FAs 16:0 18:0 18:1 n-9 18:2 n-6 18:3 n-3 20:4 n-6 20:5 n-3 22:5 n-3 22:6 n-3
9 8 5 8 3 7 1 3 2
228 164 308 45.1 14.6 10.8 4.55 11.2 0.61
0.95 0.95 0.94 0.77 0.59 0.51 0.08 0.13 0.11
355 202 402 57.0 19.8 14.6 7.61 15.1 2.03
0.86 0.93 0.90 0.70 0.51 0.49 0.10 0.11 0.16
8 8 9 8 13 6 7 8 9
248 169 311 42.8 10.8 9.91 4.38 8.74 0.70
0.94 0.95 0.95 0.79 0.78 0.56 0.31 0.34 0.49
319 212 370 58.6 16.4 14.0 6.55 13.1 1.74
Fatty acid families Total SFAs Total MUFAs Total cis MUFAs Total trans MUFAs Total CLAs Total n-3 PUFAs Total n-3 LC PUFAs Total PUFAs
6 7 7 8 3 3 6 8
442 390 362 40.9 9.68 27.4 15.2 82.3
0.95 0.95 0.95 0.88 0.78 0.30 0.28 0.78
560 514 490 52.8 14.2 45.6 24.9 105
0.90 0.90 0.90 0.78 0.67 0.28 0.06 0.71
9 9 8 14 13 13 9 10
431 367 368 35.6 8.09 23.8 14.6 74.2
0.95 0.96 0.95 0.91 0.86 0.59 0.42 0.82
570 473 457 50.4 11.3 37.9 24.8 84.9
Components
Bias
SEP(C)
Fresh
Freeze-dried
Fresh
Freeze-dried
Total lipids Total FAs
0.0 0.3a
0.0 0.1b
1.0 1.1
1.0 1.1
0.88 0.92 0.91 0.67 0.67 0.55 0.17 0.32 0.22
Individual FAs 16:0 18:0 18:1 n-9 18:2 n-6 18:3 n-3 20:4 n-6 20:5 n-3 22:5 n-3 22:6 n-3
95.6 41.9 121a 10.9 5.7 3.2 2.2a 2.6 0.7a
49.1 41.4 21.4b 6.64 3.4 3.7 1.3b 1.8 0.4b
344 198 386 56.3 19.1a 14.4 7.3a 15.0 1.9
317 208 371 58.2 16.1b 13.6 6.5b 13.0 1.7
0.90 0.90 0.90 0.79 0.76 0.47 0.25 0.78
Fatty acid families Total SFAs Total MUFAs Total cis MUFAs Total trans MUFAs Total CLAs Total n-3 PUFAs Total n-3 LC PUFAs Total PUFAs
161 150a 153a 9.2 5.2 13.2a 5.2 24.9
74.0 54.1b 53.1b 4.0 2.6 7.4b 5.7 16.0
581 495 468 52.4 13.3a 43.9a 24.5 102a
569 473 457 50.6 11.8b 37.5b 24.3 85.0b
Total SFAs: 12:0 to 24:0; Total cis-MUFAs: 14:1 Δ9cis + 15:1 Δ9cis + 16:1 Δ9cis + 17:1 Δ8 and Δ9cis + 18:1 Δ6cis to Δ15cis + 20:1 Δ9cis + 22:1 Δ9cis; Total trans-MUFAs: 16:1 Δ9trans + 18:1 Δ6 to Δ16trans; total MUFAs: cis + trans MUFAs; Total n-3 PUFAs: 18:3 n-3 + 20:3 n-3 + 20:4 n-3 + 20:5 n3 + 22:3 n-3 + 22:4 n-3 + 22:5 n-3 + 22:6 n-3; Total n-3 LC PUFAs: 20:5 n3 + 22:5 n-3 + 22:6 n-3; Total CLAs: 9cis,11trans-CLA + 11cis,13transCLA + total cis,cis CLAs + total trans,trans CLAs; Total PUFAs: n-6 PUFAs + n3 PUFAs + CLAs. a Abbreviations: FA, fatty acids; SFAs, saturated fatty acids; MUFAs, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; CLAs, conjugated linolenic acids; LC long chain, 20:5 n-3 + 22:5 n- + 22:6 n-3; T, number of terms used to perform the calibration model; SEC, standard error of calibration; R2C, coefficient of determination of calibration. SEP, standard error of prediction; R2V, coefficient of determination of validation.
Total SFAs: 12:0 to 24:0; Total cis-MUFAs: 14:1 Δ9cis + 15:1 Δ9cis + 16:1 Δ9cis + 17:1 Δ8 and Δ9cis + 18:1 Δ6cis to Δ15cis + 20:1 Δ9cis + 22:1 Δ9cis; Total trans-MUFAs: 16:1 Δ9trans + 18:1 Δ6 to Δ16trans; total MUFAs: cis + trans MUFAs; Total n-3 PUFAs: 18:3 n-3 + 20:3 n-3 + 20:4 n-3 + 20:5 n3 + 22:3 n-3 + 22:4 n-3 + 22:5 n-3 + 22:6 n-3; Total n-3 LC PUFAs: 20:5 n3 + 22:5 n-3 + 22:6 n-3; Total CLAs: 9cis,11trans-CLA + 11cis,13transCLA + total cis,cis CLAs + total trans,trans CLAs; Total PUFAs: n-6 PUFAs + n3 PUFAs + CLAs. a, b Values on the same line that had different letters within statistics were significantly different (P < .05). c Abbreviations: FA, fatty acids; SFAs, saturated fatty acids; MUFAs, mono-unsaturated fatty acids; PUFA, polyunsaturated fatty acids; CLAs, conjugated linoleic acids; LC long chain, 20:5 n-3 + 22:5 n- + 22:6 n-3.
5. Conclusions
Bardou-Valette, V. Largeau, and A. Thomas for the GC analysis of fatty acids and F. Picard for the NIRS analysis. We also thank Terrena, Elivia, and Guillaume Mairese, who contributed to obtaining a significant portion of the samples used in this study and Berengere Hoez (Tellus), and Emmanuel Bedier (IDENA) to their contribution to production of some animals of this study and to obtention of samples. The beef samples used in this paper were taken from three experiments carried out in our laboratory. We thank ProSafeBeef European program (FoodCT-2006-36241), the 14th FUI (contract number F1211006E), Bretagne and Pays de la Loire Regions, the General Council of the Côtes d'Armor, Lannion-Trégor-Community, FEDER (SpecMeat Project (Number 36710), and ADEME (Number 497/CGI/ LS/IF) for funding this study.
The results of this study suggest that the major individual FAs or FA families in beef can be predicted using fresh samples. However, techniques applied to samples for removing their water content, such as freeze-drying, even though they require some additional manipulations, could be interesting for predicting individual PUFAs or PUFA families present in small quantities in beef and not well predicted in fresh samples. Otherwise, the VIS/NIRS prediction of FAs present in very small quantities is still not possible using fresh or freeze-dried samples. Therefore, further investigations are necessary to improve the performance of VIS/NIRS models to predict these beef FA concentrations. Declarations of Competing Interest
References
None.
Andueza, D., Agabriel, C., Constant, I., Lucas, A., & Martin, B. (2013). Using visible or near infrared spectroscopy (NIRS) on cheese to authenticate cow feeding regimes. Food Chemistry, 141(1), 209–214. Andueza, D., Mourot, B.-P., Hocquette, J.-F., & Mourot, J. (2017). Chapter 16 - Phenotyping of animals and their meat: Applications of low-power ultrasounds, near-infrared spectroscopy, Raman spectroscopy, and Hyperspectral imaging. Woodhead Publishing. Barnard, N. D., Nicholson, A., & Howard, J. L. (1995). The medical costs attributable to meat consumption. Preventive Medicine, 24(6), 646–655. Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43(5), 772–777. https://doi.org/10.1366/0003702894202201.
Acknowledgments The authors express their thanks to the staff of the INRA Experimental Unit Herbipôle (UE1414) for animal management and slaughtering. They also thank the staff of the INRA UMR Herbivores and French Livestock Institute Meat Quality Team for technical assistance, namely, L. Allais, A. Cartes, and C. Falguère for muscle sampling, D. Chadeyron, B. Fréchet, and A. Delavaud for sample preparation, S. 5
Meat Science 158 (2019) 107910
D. Andueza, et al. Bauchart, D., Durand, D., Scislowski, V., Chilliard, Y., & Gruffat, D. (2005). Effects of diets supplemented with oils on specific fatty acids of rectus abdominis muscle in Charolais fattening bulls. In J. F. Hocquette, & S. Gigli (Vol. Eds.), Indicators of milk and beef quality. Vol. 112. Indicators of milk and beef quality (pp. 431–436). EAAP. Wageningen Acad. Publishers. Berthelot, V., & Gruffat, D. (2018). Fatty acid composition of muscles INRA feeding system for ruminants. In INRA (Ed.). INRA feeding system for ruminants (pp. 193–202). Wageningen Academic Publishers. Berzaghi, P., Dalle Zotte, A., Jansson, L. M., & Andrighetto, I. (2005). Near-infrared reflectance spectroscopy as a method to predict chemical composition of breast meat and discriminate between different n-3 feeding sources. Poultry Science, 84(1), 128–136. Büning-Pfaue, H. (2003). Analysis of water in food by near infrared spectroscopy. Food Chemistry, 82(1), 107–115. Cecchinato, A., De Marchi, M., Penasa, M., Casellas, J., Schiavon, S., & Bittante, G. (2012). Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy. Journal of Animal Science, 90(2), 429–438. De Marchi, M., Berzaghi, P., Boukha, A., Mirisola, M., & Galol, L. (2007). Use of near infrared spectroscopy for assessment of beef quality traits. Italian Journal of Animal Science, 6(sup1), 421–423. Dubost, A., Micol, D., Picard, B., Lethias, C., Andueza, D., Bauchart, D., & Listrat, A. (2013). Structural and biochemical characteristics of bovine intramuscular connective tissue and beef quality. Meat Science, 95(3), 555–561. Fearn, T. (1996). Comparing standard deviations. NIR News, 7(5), 5–6. Folch, J., Lees, M., & Sloane Stanley, G. H. (1957). A simple method for the isolation and purification of total lipides from animal tissues. The Journal of Biological Chemistry, 226(1), 497–509. García-Olmo, J., De Pedro, E., Garrido-Varo, A., Jimenez, A., Salas, J., & Santolalla, M. (2000). Fatty acids analysis of Iberian pig fat by near infrared spectroscopy (NIRS). Options Méditerranéennes. Série A Séminaires Méditerranéennes, (41), 191–195. Giangiacomo, R. (2006). Study of water–sugar interactions at increasing sugar concentration by NIR spectroscopy. Food Chemistry, 96(3), 371–379. Guy, F., Prache, S., Thomas, A., Bauchart, D., & Andueza, D. (2011). Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS). Food Chemistry, 127(3), 1280–1286. Huang, Y., Andueza, D., de Oliveira, L., Zawadzki, F., & Prache, S. (2015). Comparison of visible and near infrared reflectance spectroscopy on fat to authenticate dietary history of lambs. Animal, 9(11), 1912–1920. Kennard, R. W., & Stone, L. A. (1969). Computer aided Design of Experiments. Technometrics, 11(1), 137. https://doi.org/10.2307/1266770. Lock, A. L., Harvatine, K. J., Drackley, J. K., & Bauman, D. E. (2006). Concepts in fat and fatty acid digestion in ruminants. Paper presented at the proceedings of the intermountain nutrition conference. McAfee, A. J., McSorley, E. M., Cuskelly, G. J., Moss, B. W., Wallace, J. M. W., Bonham,
M. P., & Fearon, A. M. (2010). Red meat consumption: An overview of the risks and benefits. Meat Science, 84(1), 1–13. Mourot, B. P., Gruffat, D., Durand, D., Chesneau, G., Mairesse, G., & Andueza, D. (2015). Breeds and muscle types modulate performance of near-infrared reflectance spectroscopy to predict the fatty acid composition of bovine meat. Meat Science, 99, 104–112. Osborne, B. G., & Fearn, T. (1988). Near infrared spectroscopy in food analysis. New York: Longman Scientific & Technical / John Wiley & Sons, Inc. Perez-Marin, D., Sanz, E. D., Guerrero-Ginel, J. E., & Garrido-Varo, A. (2009). A feasibility study on the use of near-infrared spectroscopy for prediction of the fatty acid profile in live Iberian pigs and carcasses. Meat Science, 83(4), 627–633. https://doi.org/10. 1016/j.meatsci.2009.07.012. Prieto, N., Andres, S., Giraldez, F. J., Mantecon, A. R., & Lavin, P. (2006). Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples. Meat Science, 74(3), 487–496. https://doi.org/10. 1016/j.meatsci.2006.04.030. Realini, C. E., Duckett, S. K., & Windham, W. R. (2004). Effect of vitamin C addition to ground beef from grass-fed or grain-fed sources on color and lipid stability, and prediction of fatty acid composition by near-infrared reflectance analysis. Meat Science, 68(1), 35–43. Scislowski, V., Durand, D., Gruffat, D., & Bauchart, D. (2004). Dietary linoleic acid-induced hypercholesterolemia and accumulation of very light HDL in steers. Lipids, 39(2), 125–133. Shenk, J. S., & Westerhaus, M. O. (1994). The application of near infrared reflectance spectroscopy (NIRS) to forage analysis. In G. C. Fahey, M. Collins, D. R. Mertens, & L. E. Moser (Eds.). Forage quality, evaluation, and utilization (pp. 406–449). Soil Science Society of America: Madison, WI American Society of Agronomy, Crop Science Society of America. Sierra, V., Aldai, N., Castro, P., Osoro, K., Coto-Montes, A., & Oliván, M. (2008). Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy. Meat Science, 78(3), 248–255. Surya, R., Helies-Toussaint, C., Martin, O. C., Gauthier, T., Gueraud, F., Tache, S., & Huc, L. (2016). Red meat and colorectal cancer: Nrf2-dependent antioxidant response contributes to the resistance of preneoplastic colon cells to fecal water of hemoglobinand beef-fed rats. Carcinogenesis, 37(6), 635–645. https://doi.org/10.1093/carcin/ bgw035. Thyholt, K., & Isaksson, T. (1997). Differentiation of frozen and unfrozen beef using nearinfrared spectroscopy. Journal of the Science of Food and Agriculture, 73(4), 525–532. Tøgersen, G., Arnesen, J. F., Nilsen, B. N., & Hildrum, K. I. (2003). On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy. Meat Science, 63(4), 515–523. Wood, J. D. (2017). Chapter 20 - meat composition and nutritional value. Woodhead Publishing.
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Prediction of beef meat fatty acid composition by visible-near-infrared spectroscopy was improved by preliminary freeze-drying
T
D. Anduezaa, , A. Listrata, D. Duranda, J. Normandb, B.P. Mourotc, D. Gruffata ⁎
a
Université Clermont Auvergne, INRA, VetAgro Sup, UMR Herbivores, 63122 Saint-Genès-Champanelle, France Institut de l'Elevage, Service Qualité des Viandes, 23 Rue Jean Baldassini, F-69007 Lyon, France c Valorex, La Messayais, F-35210, Combourtillé, France b
ARTICLE INFO
ABSTRACT
Keywords: Visible-near-infrared spectroscopy Beef Fatty acids Freeze-dried samples Fresh samples
The aim of this study was to compare visible-near-infrared spectroscopy (VIS/NIRS) models developed from fresh or freeze-dried samples for predicting the fatty acid (FA) composition of beef samples. The hypothesis tested is that the removal of water from samples could improve the VIS/NIRS model performance. A total of 454 beef samples obtained from different bovine muscles were used. No significant differences were found in the performance of VIS/NIRS models developed from fresh or freeze-dried samples for predicting both major individual FAs and families of FAs and for some FAs (16:0, 18:0, 18:1 n-9, 18:2 n-6, 20:4 n-6, 22:5 n-3, 22:6 n-3, saturated, mono-unsaturated FA, and total n-3 long chain poly-unsaturated FAs (PUFA)). In contrast, the standard error of predictions for total PUFAs, total n-3 PUFAs, total conjugated linoleic acid, 20:5 n-3, and 18:3 n-3 were improved (by 21% on average; P < .05) in freeze-dried samples compared with fresh samples.
1. Introduction Meat is composed of water (75%), proteins, and amino acids (23%), fatty acids (1.8%), cholesterol, phospholipids, minerals, vitamins, etc. The nutritional value of meat is related to its composition in terms of amino acids, fatty acids (FAs), minerals, and vitamins. However, an excess of red meat in the diet is associated with health problems such as hypertension, heart disease, and cancer (Barnard, Nicholson, & Howard, 1995; McAfee et al., 2010; Surya et al., 2016). These diseases are mostly related to the FA composition of meats. In general, to try to decrease their impact on human health, lower saturated FA (SFA) and higher polyunsaturated FA (PUFA) intake are recommended, especially higher n-3 PUFA. Therefore, it is essential to inform consumers, whose demands regarding the nutritional quality of meat are increasing, of its FA profile. The reference method currently used for determining the FA profile of meat is gas chromatography (GC). However, this method is costly and time consuming, produces chemical wastes that are necessary to eliminate, and is difficult to use on a large scale. In contrast, near-infrared spectroscopy (NIRS) is an indirect technology that is nondestructive, multiparametric, and usable on a large scale. In the recent past, there has been a generalized study about the potential use of NIRS as an alternative technology for predicting some quality parameters or for the authentication (Andueza, Agabriel, Constant, Lucas, & Martin, 2013; Huang, Andueza, de Oliveira, Zawadzki, & Prache, 2015) ⁎
of several products. In meats, some authors have shown the ability of NIRS to predict the FA contents (Berzaghi, Dalle Zotte, Jansson, & Andrighetto, 2005; Guy, Prache, Thomas, Bauchart, & Andueza, 2011; Mourot et al., 2015; Perez-Marin, Sanz, Guerrero-Ginel, & GarridoVaro, 2009; Sierra et al., 2008). However, the prediction of minor FAs such as PUFA families and individual PUFAs in beef is difficult (Mourot et al., 2015; Sierra et al., 2008). According to these authors, the narrow variability of these FAs could contribute to explaining the difficulty of obtaining adequate models for predicting minor FAs that are important for human health. Water is a strong absorber in the infrared region (Büning-Pfaue, 2003), and the high content of water in meat (75%) (Wood, 2017) could influence the results of NIRS predictions (Thyholt & Isaksson, 1997), limiting the detection of some minor constituents. Furthermore, the absorbance of water is temperature dependent, which could increase the error of NIRS models developed from samples characterized by high moisture content if the spectra are been obtained in very standard conditions. Therefore, we hypothesized that removing water before taking the NIRS spectra could increase the precision of the models for predicting some minor important FAs and some individual PUFAs or families of PUFAs. For this purpose, the objective of the current research is to compare visible-near-infrared spectroscopy (VIS/ NIRS) models for predicting FAs in fresh and freeze-dried beef.
Corresponding author. E-mail address: [email protected] (D. Andueza).
https://doi.org/10.1016/j.meatsci.2019.107910 Received 20 May 2019; Received in revised form 24 July 2019; Accepted 7 August 2019 Available online 08 August 2019 0309-1740/ © 2019 Elsevier Ltd. All rights reserved.
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2. Materials and methods
2.4. Calibrations and statistics
2.1. Samples
Calibrations were developed using WinISI II version 1.60 (Infrasoft International). The samples were divided into calibration (n = 344) and validation (n = 110) sets by the application of the Kennard-Stone algorithm (Kennard & Stone, 1969) to the pool of fresh samples. The modified partial least squares regression method was used to obtain VIS/NIRS equations for all the studied parameters. To optimize the regression models, the data were subjected to standard normal variate and detrending corrections (Barnes, Dhanoa, & Lister, 1989) and firstderivative treatments (1,4,4), where the first digit is the number of the derivative, the second is the gap over which the derivative is calculated, and the third is the number of data points in the first smoothing. The best treatment was selected for each constituent based on the highest coefficient of determination in calibration (R2C) and the lowest standard error of calibration (SEC). During the calibration process, samples were considered outliers if the residual between the reference method and the predicted value was > 2.5-fold greater than the SEC (t = 2.5). Two passes of elimination were allowed. Upon completion of the calibration, the models were applied to the validation set. First, the spectral similarity between each sample in the validation test and the center of those in the calibration set was tested through the application of the standardized Mahalanobis distance (H). If the H-values of some samples were higher than 3, according to Shenk and Westerhaus (1994), these samples were considered outliers. The validation performance of each model was assessed by the coefficient of determination in external validation (R2V), the standard error of prediction (SEP), the bias, and the SEP corrected by the bias (SEP(C)). The bias and (SEP(C)) for the fresh and freeze-dried models obtained for the same determinations were compared according to the procedure proposed by Fearn (1996). In this way, biases were compared using a t confidence interval for paired samples whereas SEP(C) values were compared by calculating an interval for the ratio of standard errors. Calculations were performed using Excel 2016.
The beef samples used in this study were taken from two experiments carried out in our laboratory and conducted in a manner compatible with the national legislation on animal care (the slaughterhouse and experimental facility license numbers are #63345 01 and #63345.17, respectively). A total of 454 samples of beef, selected for wide compositional variability, were used. The beef samples used included diaphragma, infraspinatus, longissimus thoracis, and rectus abdominis muscles and chopped steaks. Samples (~ 125 g) were obtained 24 h or 48 h post mortem, cut into small cubes (approximately 1 cm3), and frozen in liquid nitrogen at −80 °C before being ground in liquid nitrogen with a mixer grinder to obtain a fine and homogenous powder (Retch MM 301, Hann Germany). Each sample was then divided into four subsamples. Three of these subsamples were subjected to lipid GC analysis, FA GC analysis and VIS/NIRS scanning, respectively, as fresh meat. The fourth sample was freeze-dried for 96 h using a freeze-dryer (Cryotec, Saint-Gély du-Fesc, France), ground again and then stored at +4 °C in plastic tubes protected from light until VIS/NIRS scanning. 2.2. Lipids and fatty acid analysis The total lipids were extracted according to the method of Folch, Lees, and Sloane Stanley (1957) by mixing the muscle powder with a 2/ 1 chloroform/methanol mixture (vol/vol) and quantified by gravimetry. Fatty acid extraction and transmethylation into fatty acid methyl esters (FAME) were subsequently performed according to the methods of Bauchart, Durand, Scislowski, Chilliard, and Gruffat (2005). Fatty acid methyl ester analysis was performed with GC using a Peri 2100 chromatography system (Perichrom Society, Saulx-les-Chartreux, France) fitted with a CP-Sil 88 glass capillary column (Varian, Palo Alto, CA; length = 100 m; diam. = 0.25 mm). The carrier gas was H2, and the oven and flame ionization detector temperatures described by Scislowski, Durand, Gruffat, and Bauchart (2004) were used. Total FAs was quantified using C19:0 as an internal standard. The identification of each individual FAME and the calculation of the response coefficients for each individual FAME were performed using the quantitative mix C4-C24 FAME (Supelco, Bellafonte, PA). Fatty acids and families of FAs determined were the following: Total FAs, 16:0, 18:0, 18:1 n-9, 18:2 n6, 18:3 n-3, 20:4 n-6, 20:5 n-3, 22:5 n-3, 22:6 n-3, total SFAs, total mono-unsaturated FAs (MUFAs), total cis-MUFAs, total trans-MUFAs, total conjugated linoleic acids (CLAs), total n-3 PUFAs, total n-3 long chain (LC) PUFAs, and total PUFAs.
3. Results 3.1. Fatty acid composition The mean, standard deviation, minimum, and maximum values for total lipids, total FAs, some individual FAs and families of FAs for the calibration and validation sets are presented in Table 1. The calibration and validation sets covered a broad and similar minimum and maximum intervals for each component. Oleic (18:1 n-9) and palmitic (16:0) acids were the most abundant FAs. Stearic acid (18:0) averaged 966 and 879 mg/100 g in the calibration and validation sets, respectively. The concentrations of SFAs and MUFAs of the pool of samples were 2550 and 2270 mg/100 g of fresh muscle, but the concentration of mean PUFAs was approximately six times lower (407 and 393 mg/100 g of fresh muscle in the calibration and validation populations, respectively). The largest differences between the two sample sets were found for C22:6 n-3 (difference between coefficients of variation associated to calibration and validation sets was of about 0.12). For the other determinations, these differences were lower than 0.10.
2.3. Visible-near-infrared spectroscopy Before scanning, fresh meat samples were thawed at laboratory temperature for 30 min. Then, approximately 20 g of ground fresh meat or 13 g of ground freeze-dried meat (Dubost et al., 2013) was placed in a 50 mm diameter ring cup (approximately 10 mm deep; part number IH - 0307, NIRSystems, Infrasoft International, South Atherton St. State College, PA 16801, USA), compressed and sealed with a disposable paper-backed wrap. They were then scanned in reflectance mode at 2 nm intervals from 400 to 2498 nm using a Foss NIRSystems model 6500 scanning visible-near-infrared spectrometer equipped with an auto cup module (Foss NIRSystems, Silver Spring, MD, USA) and controlled by ISIscan software version 2.21 (Infrasoft International, Port Matilda, PA, USA). Each spectrum was time averaged from 32 scans. The reflectance (R) values were converted into absorbance (A) values using the formula A = log (1/R).
3.2. Meat spectra The average absorbance data of the total population and average second derivative spectra for fresh and freeze-dried spectra are shown in Fig. 1. The absorbance values for fresh samples were higher than those observed for freeze-dried samples (Fig. 1a). The spectra of fresh samples were characterized by absorbance maxima at 410, 540, 950,
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and R2V equal or higher than 0.90 were found for models developed for total lipids, total FAs, 18:0, 18:1 n-9, total SFAs, total MUFAs, and total cis-MUFAs for both the fresh and freeze-dried spectra. Furthermore, when freeze-dried samples were used, R2C for total trans-MUFAs was 0.91 and R2V was 0.79 (Table 2) whereas for 16:0, R2C was higher than 0.90 when both types of samples were used but R2V were 0.86 and 0.88 when fresh and freeze-dried samples were used. For total PUFAs, the R2C values were 0.78 and 0.82 and the R2V values were 0.71 and 0.78 for fresh and freeze-dried samples, respectively. For the other FAs, the R2C and R2V values were lower than 0.8 for both fresh and freeze-dried samples. The decomposition of SEP into bias and SEP(C) for each determination when both fresh and freeze-dried samples were used is given in Table 3. For all determinations, bias was lower than SEP(C). Differences (P < .05) for biases between fresh and freeze-dried samples were found for total FAs, 18:1 n-9, 20:5 n-3, 22:6 n-3, total MUFAs, total cis MUFAs, and total n-3 PUFAs. For these determinations, the biases for fresh samples were higher than those obtained for freeze-dried samples. For SEP(C), significant differences (P < .05) between models built from fresh and freeze-dried samples were found for 18:3 n-3, 20:5 n-3, total CLAs, total n-3 PUFAs, and total PUFAs. For these FAs and sums of FAs, the SEP(C) values were lower for models developed from freezedried samples than for those developed from fresh samples. No significant differences (P > .05) were found for the other values determined.
Table 1 Descriptive statistics for total lipid and fatty acid contents (g/100 g fresh muscle) in beef samples used in the databasea. Calibration (n = 374)
Validation (n = 80)
Constituent
Mean (SD)
Min-Max
Mean (SD)
Min-Max
Total lipids (g/100 g FM) Total FAs (g/100 g FM)
5.7 (4.11)
0.08–18.9
5.29 (3.77)
0.08–14.11
5.5 (4.00)
0.48–18.0
5.1 (3.47)
0.58–16.3
78.4–5338
1287 (897)
101–4051
57.3–4039 87.0–6361
879 (738) 1731 (1212)
89–3086 123–5339
62.1–604 6.10–147 11.9–111.1 0.00–45.9 0.00–80.2 0.00–10.5
189 (101) 37.6 (26.7) 42.6 (19.5) 8.24 (7.08) 23.6 (15.8) 1.00 (1.93)
62.0–502 6.35–128 11.9–115 0.00–39.0 0.00–86.8 0.00–11.3
159–9129
2435 (1804)
216–8116
166–8122
2181 (1524)
185–7019
133–7853
2076 (1447)
163–6526
10.0–719 0.00–140 9.78–295 0.00–147.8 149–1056
105(111) 25.4 (22.6) 74.0 (50.4) 36.4 (28.0) 393 (188)
8.4–545.6 1.9–114 10.4–264 0.00–149 148–955
Individual FAs (mg/100 g FM) 16:0 1396 (1066) 18:0 966 (806) 18:1 n-9 1853 (1405) 18:2 n-6 196 (96.1) 18:3 n-3 38.8 (25.1) 20:4 n-6 42.3 (17.6) 20:5 n-3 7.50 (7.05) 22:5 n-3 22.2 (14.3) 22:6 n-3 0.84 (1.82) Fatty acid families (mg/100 g FM) Total SFAs 2663 (2075) Total MUFAs 2352 (1800) Total cis-MUFAs 2229 (1701) Total trans-MUFAs 122 (135) Total CLAs 26.6 (25.8) Total n-3 PUFAs 72.4 (43.9) Total n-3 LC PUFAs 33.8 (25.3) Total PUFAs 407 (179)
4. Discussion The standard deviation and the minimum and maximum values of FA contents in the population samples used in the current study are representative of the beef FA content according to the literature (Berthelot & Gruffat, 2018). However, the variability in the data used in the current study was similar or higher than that reported by Mourot et al. (2015) for most FAs, with the exception of 22:6 n-3 PUFA. This high variability for lipids and most of the FAs can be explained by the use in the present study of various bovine breeds, sex, and ages varying in lipogenesis capacities and by the use of various types of muscles. Animals were also fed different diets. The lack of differences in the variability for 22:6 n-3 PUFA could be explained by the low content of this FA and because it is one of the FAs characterized by being more rapidly biohydrogenated in the rumen (Lock, Harvatine, Drackley, & Bauman, 2006). Consequently, the difficulty of finding differences in the variability between databases increases. The ability of NIRS to predict total lipid and FA contents in beef has already been reported by Mourot et al. (2015), De Marchi, Berzaghi, Boukha, Mirisola, and Galol (2007) and Tøgersen, Arnesen, Nilsen, and Hildrum (2003). Therefore, Mourot et al. (2015) reported relationships characterized by R2 > 0.90 for the SFA and MUFA families and for the most abundant individual SFAs and MUFAs (14:0, 16:0, 16:1 cis9, 18:1 cis9, 18:1 cis11). However, for the PUFA family and individual PUFAs, these authors reported NIRS models characterized by R2 < 0.62, as also reported by Cecchinato et al. (2012), Realini, Duckett, and Windham (2004) and Sierra et al. (2008). Realini et al. (2004) suggest that the failure to precisely determine these individual FAs could be related to similarities in their NIRS absorption patterns, as these FAs have the same absorbing molecular group (–CH2), and to the narrow standard deviation of the data sets. According to Andueza, Mourot, Hocquette, and Mourot (2017), this last explanation seems the most likely because other authors working with lamb meats (Guy et al., 2011), chicken (Berzaghi et al., 2005), and pork (García-Olmo et al., 2000) reported prediction models for PUFAs with R2 values higher than 0.88, which were associated with wider variability. These studies were carried out using fresh
Total SFAs: 12:0 to 24:0; Total cis-MUFAs: 14:1 Δ9cis + 15:1 Δ9cis + 16:1 Δ9cis + 17:1 Δ8 and Δ9cis + 18:1 Δ6cis to Δ15cis + 20:1 Δ9cis + 22:1 Δ9cis; Total trans-MUFAs: 16:1 Δ9trans + 18:1 Δ6 to Δ16trans; total MUFAs: cis + trans MUFAs; Total n-3 PUFAs: 18:3 n-3 + 20:3 n-3 + 20:4 n- 3 + 20:5 n3 + 22:3 n-3 + 22:4 n-3 + 22:5 n-3 + 22:6 n-3; Total n-3 LC PUFAs: 20:5 n3 + 22:5 n-3 + 22:6 n-3;Total CLAs: 9cis,11trans-CLA + 11cis,13transCLA + total cis,cis CLAs + total trans,trans CLAs; Total PUFAs: n-6 PUFAs + n3 PUFAs + CLAs. a Abbreviations: FAs, fatty acids; FM, fresh muscle; Min, minimum; Max, maximum; SD, standard deviation; SFAs, saturated FAs; MUFAs, mono-unsaturated FAs; n, number of samples; PUFAs, poly-unsaturated FAs; CLA, conjugated linoleic acid; LC, long chain.
1180, 1450, and 1940 nm, whereas a higher number of absorption bands characterized the average freeze-dried spectra (410, 540, 950, 1180, 1450, 1740, 1940, 2070, 2180, 2320, 2360, and 2470 nm). After normalization and scatter correction (Fig. 1b), most of the absorption bands were similar for both processing types (maxima at 1650, 1740, 1770, 2010, 2080, 2230, 2250, 2320, and 2360 nm). However, the spectrum of average freeze-dried samples showed higher maxima and lower minima (particularly in the area between 1450 and 2440 nm) than those observed in the spectrum of average fresh samples. The maxima of the second derivative fresh sample spectrum at 500, 600, 940, 1128, 1400, and 1850 nm were higher than those of the freezedried sample spectrum. 3.3. Near-infrared models for the prediction of fatty acid concentrations in fresh and freeze-dried meat samples The statistics related to the calibration models and the statistics obtained when the calibration models were applied to the validation sets are given in Table 2. Coefficients of determination in calibration
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2.5
log (1/R)
2 1.5 1 0.5 0 400
900
1400
1900
2400
1900
2400
Wavelengths Freeze-dried
Fresh
0.6
2nd derivative
0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 400
900
1400
Wavelengths Freeze-dried
Fresh
Fig. 1. Average log (1/R) (a) and second derivative (b) of visible and near-infrared spectrum for fresh and freeze-dried beef muscle samples.
and homogenized meat samples. In general, fresh conditions were chosen to minimize the manipulations of samples, and homogenization was chosen because it is an important condition for obtaining good relationships between NIRS spectra and laboratory data (Prieto, Andres, Giraldez, Mantecon, & Lavin, 2006; Tøgersen et al., 2003). Fresh meat spectra were characterized by the presence of absorbance bands relative to water and by the influence of water on the absorbance of other compounds. Indeed, according to Büning-Pfaue (2003) and (Osborne & Fearn, 1988), maxima at 940, 1128, 1400, and 1850 nm were observed with fresh meat, whereas the spectra obtained using freeze-dried meat samples were characterized only by a maximum at 1860 nm. The band shift in this interval (1850–1860 nm) between the two types of sample presentation can be attributed to the effect of the interaction between water and solutes (Büning-Pfaue, 2003; Giangiacomo, 2006). A shift band caused by the interaction of water and solutes could also explain the observed band at 1128 nm in the fresh spectrum, whereas Osborne and Fearn (1988) reported an absorption band for water at 1190 nm. In the current study, the spectra of fresh samples were similar to those reported by Cecchinato et al. (2012) and Sierra et al. (2008). However, when most water was removed throughout the freeze-drying process, the SEP values for PUFA family, total n-3 PUFAs, total CLAs, and some individual PUFAs (18:3 n-3, and 20:5 n-3) were improved by 21% on average. These results showed that freeze-drying significantly improved the precision of NIRS prediction models for FAs that were present at concentrations between 20 and 400 mg/100 g of muscle
although for some FAs or families of FAs such as 20:4 n-6, total transMUFA, ant total n-3 LC PUFAs no improvement was observed. In contrast, when FA concentrations were higher than 400 mg/100 g or lower than 20 mg/100 g, no difference was observed between sample presentation types. These results could be explained by the higher intensity of absorbance values in freeze-dried samples than in fresh samples. In this way, the intensity of the signal obtained with fresh samples was not limiting for predicting FAs present in quantities higher than 400 mg/ 100 g of muscle. However, for predicting most FAs present in muscles in smaller quantities, between 20 and 400 mg/100 g of muscle, the higherintensity signal obtained with freeze-dried samples, particularly between 1500 and 2500 nm, could be a major asset for improving the precision of NIRS prediction models. For FAs present at concentrations lower than 20 mg/100 g of muscle, the significant improvement obtained when freeze-dried samples were used was not systematic. Consequently, these results suggest that NIRS seems unable to detect concentrations lower than 20 mg/100 g of muscle even when most of the water was eliminated in the freeze-drying process. Sierra et al. (2008) and Mourot et al. (2015) suggest that the NIRS prediction results for PUFAs would be improved by using population samples characterized by wide variability. However, this variability is difficult to obtain when working on beef meats because a great part of PUFAs ingested by animals is saturated by rumen biohydrogenation, limiting PUFA variability in muscles and meats. In these conditions, removing water from samples, even though this process has a cost and is laborious, could be an interesting alternative for predicting PUFAs in beef by VIS/NIRS.
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Table 2 Statistical parameters of visible-near-infrared spectroscopy models for fatty acids using ground fresh and freeze-dried beef samples from the calibration and validation databasesa. Fresh
Table 3 Bias and standard error of prediction corrected for bias (SEP(C)) for fatty acids (FAs) obtained when each sample of the validation set was predicted using the visible-near-infrared spectroscopy calibration model obtained for each determinationc.
Freeze-dried 2
2
Constituent
T
SEC
R C
SEP
R V
T
SEC
R2C
SEP
R2V
Total lipids Total FAs
5 8
0.81 0.85
0.96 0.95
1.00 1.16
0.93 0.90
9 8
0.80 0.90
0.96 0.94
1.01 1.09
0.93 0.90
Individual FAs 16:0 18:0 18:1 n-9 18:2 n-6 18:3 n-3 20:4 n-6 20:5 n-3 22:5 n-3 22:6 n-3
9 8 5 8 3 7 1 3 2
228 164 308 45.1 14.6 10.8 4.55 11.2 0.61
0.95 0.95 0.94 0.77 0.59 0.51 0.08 0.13 0.11
355 202 402 57.0 19.8 14.6 7.61 15.1 2.03
0.86 0.93 0.90 0.70 0.51 0.49 0.10 0.11 0.16
8 8 9 8 13 6 7 8 9
248 169 311 42.8 10.8 9.91 4.38 8.74 0.70
0.94 0.95 0.95 0.79 0.78 0.56 0.31 0.34 0.49
319 212 370 58.6 16.4 14.0 6.55 13.1 1.74
Fatty acid families Total SFAs Total MUFAs Total cis MUFAs Total trans MUFAs Total CLAs Total n-3 PUFAs Total n-3 LC PUFAs Total PUFAs
6 7 7 8 3 3 6 8
442 390 362 40.9 9.68 27.4 15.2 82.3
0.95 0.95 0.95 0.88 0.78 0.30 0.28 0.78
560 514 490 52.8 14.2 45.6 24.9 105
0.90 0.90 0.90 0.78 0.67 0.28 0.06 0.71
9 9 8 14 13 13 9 10
431 367 368 35.6 8.09 23.8 14.6 74.2
0.95 0.96 0.95 0.91 0.86 0.59 0.42 0.82
570 473 457 50.4 11.3 37.9 24.8 84.9
Components
Bias
SEP(C)
Fresh
Freeze-dried
Fresh
Freeze-dried
Total lipids Total FAs
0.0 0.3a
0.0 0.1b
1.0 1.1
1.0 1.1
0.88 0.92 0.91 0.67 0.67 0.55 0.17 0.32 0.22
Individual FAs 16:0 18:0 18:1 n-9 18:2 n-6 18:3 n-3 20:4 n-6 20:5 n-3 22:5 n-3 22:6 n-3
95.6 41.9 121a 10.9 5.7 3.2 2.2a 2.6 0.7a
49.1 41.4 21.4b 6.64 3.4 3.7 1.3b 1.8 0.4b
344 198 386 56.3 19.1a 14.4 7.3a 15.0 1.9
317 208 371 58.2 16.1b 13.6 6.5b 13.0 1.7
0.90 0.90 0.90 0.79 0.76 0.47 0.25 0.78
Fatty acid families Total SFAs Total MUFAs Total cis MUFAs Total trans MUFAs Total CLAs Total n-3 PUFAs Total n-3 LC PUFAs Total PUFAs
161 150a 153a 9.2 5.2 13.2a 5.2 24.9
74.0 54.1b 53.1b 4.0 2.6 7.4b 5.7 16.0
581 495 468 52.4 13.3a 43.9a 24.5 102a
569 473 457 50.6 11.8b 37.5b 24.3 85.0b
Total SFAs: 12:0 to 24:0; Total cis-MUFAs: 14:1 Δ9cis + 15:1 Δ9cis + 16:1 Δ9cis + 17:1 Δ8 and Δ9cis + 18:1 Δ6cis to Δ15cis + 20:1 Δ9cis + 22:1 Δ9cis; Total trans-MUFAs: 16:1 Δ9trans + 18:1 Δ6 to Δ16trans; total MUFAs: cis + trans MUFAs; Total n-3 PUFAs: 18:3 n-3 + 20:3 n-3 + 20:4 n-3 + 20:5 n3 + 22:3 n-3 + 22:4 n-3 + 22:5 n-3 + 22:6 n-3; Total n-3 LC PUFAs: 20:5 n3 + 22:5 n-3 + 22:6 n-3; Total CLAs: 9cis,11trans-CLA + 11cis,13transCLA + total cis,cis CLAs + total trans,trans CLAs; Total PUFAs: n-6 PUFAs + n3 PUFAs + CLAs. a Abbreviations: FA, fatty acids; SFAs, saturated fatty acids; MUFAs, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; CLAs, conjugated linolenic acids; LC long chain, 20:5 n-3 + 22:5 n- + 22:6 n-3; T, number of terms used to perform the calibration model; SEC, standard error of calibration; R2C, coefficient of determination of calibration. SEP, standard error of prediction; R2V, coefficient of determination of validation.
Total SFAs: 12:0 to 24:0; Total cis-MUFAs: 14:1 Δ9cis + 15:1 Δ9cis + 16:1 Δ9cis + 17:1 Δ8 and Δ9cis + 18:1 Δ6cis to Δ15cis + 20:1 Δ9cis + 22:1 Δ9cis; Total trans-MUFAs: 16:1 Δ9trans + 18:1 Δ6 to Δ16trans; total MUFAs: cis + trans MUFAs; Total n-3 PUFAs: 18:3 n-3 + 20:3 n-3 + 20:4 n-3 + 20:5 n3 + 22:3 n-3 + 22:4 n-3 + 22:5 n-3 + 22:6 n-3; Total n-3 LC PUFAs: 20:5 n3 + 22:5 n-3 + 22:6 n-3; Total CLAs: 9cis,11trans-CLA + 11cis,13transCLA + total cis,cis CLAs + total trans,trans CLAs; Total PUFAs: n-6 PUFAs + n3 PUFAs + CLAs. a, b Values on the same line that had different letters within statistics were significantly different (P < .05). c Abbreviations: FA, fatty acids; SFAs, saturated fatty acids; MUFAs, mono-unsaturated fatty acids; PUFA, polyunsaturated fatty acids; CLAs, conjugated linoleic acids; LC long chain, 20:5 n-3 + 22:5 n- + 22:6 n-3.
5. Conclusions
Bardou-Valette, V. Largeau, and A. Thomas for the GC analysis of fatty acids and F. Picard for the NIRS analysis. We also thank Terrena, Elivia, and Guillaume Mairese, who contributed to obtaining a significant portion of the samples used in this study and Berengere Hoez (Tellus), and Emmanuel Bedier (IDENA) to their contribution to production of some animals of this study and to obtention of samples. The beef samples used in this paper were taken from three experiments carried out in our laboratory. We thank ProSafeBeef European program (FoodCT-2006-36241), the 14th FUI (contract number F1211006E), Bretagne and Pays de la Loire Regions, the General Council of the Côtes d'Armor, Lannion-Trégor-Community, FEDER (SpecMeat Project (Number 36710), and ADEME (Number 497/CGI/ LS/IF) for funding this study.
The results of this study suggest that the major individual FAs or FA families in beef can be predicted using fresh samples. However, techniques applied to samples for removing their water content, such as freeze-drying, even though they require some additional manipulations, could be interesting for predicting individual PUFAs or PUFA families present in small quantities in beef and not well predicted in fresh samples. Otherwise, the VIS/NIRS prediction of FAs present in very small quantities is still not possible using fresh or freeze-dried samples. Therefore, further investigations are necessary to improve the performance of VIS/NIRS models to predict these beef FA concentrations. Declarations of Competing Interest
References
None.
Andueza, D., Agabriel, C., Constant, I., Lucas, A., & Martin, B. (2013). Using visible or near infrared spectroscopy (NIRS) on cheese to authenticate cow feeding regimes. Food Chemistry, 141(1), 209–214. Andueza, D., Mourot, B.-P., Hocquette, J.-F., & Mourot, J. (2017). Chapter 16 - Phenotyping of animals and their meat: Applications of low-power ultrasounds, near-infrared spectroscopy, Raman spectroscopy, and Hyperspectral imaging. Woodhead Publishing. Barnard, N. D., Nicholson, A., & Howard, J. L. (1995). The medical costs attributable to meat consumption. Preventive Medicine, 24(6), 646–655. Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43(5), 772–777. https://doi.org/10.1366/0003702894202201.
Acknowledgments The authors express their thanks to the staff of the INRA Experimental Unit Herbipôle (UE1414) for animal management and slaughtering. They also thank the staff of the INRA UMR Herbivores and French Livestock Institute Meat Quality Team for technical assistance, namely, L. Allais, A. Cartes, and C. Falguère for muscle sampling, D. Chadeyron, B. Fréchet, and A. Delavaud for sample preparation, S. 5
Meat Science 158 (2019) 107910
D. Andueza, et al. Bauchart, D., Durand, D., Scislowski, V., Chilliard, Y., & Gruffat, D. (2005). Effects of diets supplemented with oils on specific fatty acids of rectus abdominis muscle in Charolais fattening bulls. In J. F. Hocquette, & S. Gigli (Vol. Eds.), Indicators of milk and beef quality. Vol. 112. Indicators of milk and beef quality (pp. 431–436). EAAP. Wageningen Acad. Publishers. Berthelot, V., & Gruffat, D. (2018). Fatty acid composition of muscles INRA feeding system for ruminants. In INRA (Ed.). INRA feeding system for ruminants (pp. 193–202). Wageningen Academic Publishers. Berzaghi, P., Dalle Zotte, A., Jansson, L. M., & Andrighetto, I. (2005). Near-infrared reflectance spectroscopy as a method to predict chemical composition of breast meat and discriminate between different n-3 feeding sources. Poultry Science, 84(1), 128–136. Büning-Pfaue, H. (2003). Analysis of water in food by near infrared spectroscopy. Food Chemistry, 82(1), 107–115. Cecchinato, A., De Marchi, M., Penasa, M., Casellas, J., Schiavon, S., & Bittante, G. (2012). Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy. Journal of Animal Science, 90(2), 429–438. De Marchi, M., Berzaghi, P., Boukha, A., Mirisola, M., & Galol, L. (2007). Use of near infrared spectroscopy for assessment of beef quality traits. Italian Journal of Animal Science, 6(sup1), 421–423. Dubost, A., Micol, D., Picard, B., Lethias, C., Andueza, D., Bauchart, D., & Listrat, A. (2013). Structural and biochemical characteristics of bovine intramuscular connective tissue and beef quality. Meat Science, 95(3), 555–561. Fearn, T. (1996). Comparing standard deviations. NIR News, 7(5), 5–6. Folch, J., Lees, M., & Sloane Stanley, G. H. (1957). A simple method for the isolation and purification of total lipides from animal tissues. The Journal of Biological Chemistry, 226(1), 497–509. García-Olmo, J., De Pedro, E., Garrido-Varo, A., Jimenez, A., Salas, J., & Santolalla, M. (2000). Fatty acids analysis of Iberian pig fat by near infrared spectroscopy (NIRS). Options Méditerranéennes. Série A Séminaires Méditerranéennes, (41), 191–195. Giangiacomo, R. (2006). Study of water–sugar interactions at increasing sugar concentration by NIR spectroscopy. Food Chemistry, 96(3), 371–379. Guy, F., Prache, S., Thomas, A., Bauchart, D., & Andueza, D. (2011). Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS). Food Chemistry, 127(3), 1280–1286. Huang, Y., Andueza, D., de Oliveira, L., Zawadzki, F., & Prache, S. (2015). Comparison of visible and near infrared reflectance spectroscopy on fat to authenticate dietary history of lambs. Animal, 9(11), 1912–1920. Kennard, R. W., & Stone, L. A. (1969). Computer aided Design of Experiments. Technometrics, 11(1), 137. https://doi.org/10.2307/1266770. Lock, A. L., Harvatine, K. J., Drackley, J. K., & Bauman, D. E. (2006). Concepts in fat and fatty acid digestion in ruminants. Paper presented at the proceedings of the intermountain nutrition conference. McAfee, A. J., McSorley, E. M., Cuskelly, G. J., Moss, B. W., Wallace, J. M. W., Bonham,
M. P., & Fearon, A. M. (2010). Red meat consumption: An overview of the risks and benefits. Meat Science, 84(1), 1–13. Mourot, B. P., Gruffat, D., Durand, D., Chesneau, G., Mairesse, G., & Andueza, D. (2015). Breeds and muscle types modulate performance of near-infrared reflectance spectroscopy to predict the fatty acid composition of bovine meat. Meat Science, 99, 104–112. Osborne, B. G., & Fearn, T. (1988). Near infrared spectroscopy in food analysis. New York: Longman Scientific & Technical / John Wiley & Sons, Inc. Perez-Marin, D., Sanz, E. D., Guerrero-Ginel, J. E., & Garrido-Varo, A. (2009). A feasibility study on the use of near-infrared spectroscopy for prediction of the fatty acid profile in live Iberian pigs and carcasses. Meat Science, 83(4), 627–633. https://doi.org/10. 1016/j.meatsci.2009.07.012. Prieto, N., Andres, S., Giraldez, F. J., Mantecon, A. R., & Lavin, P. (2006). Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples. Meat Science, 74(3), 487–496. https://doi.org/10. 1016/j.meatsci.2006.04.030. Realini, C. E., Duckett, S. K., & Windham, W. R. (2004). Effect of vitamin C addition to ground beef from grass-fed or grain-fed sources on color and lipid stability, and prediction of fatty acid composition by near-infrared reflectance analysis. Meat Science, 68(1), 35–43. Scislowski, V., Durand, D., Gruffat, D., & Bauchart, D. (2004). Dietary linoleic acid-induced hypercholesterolemia and accumulation of very light HDL in steers. Lipids, 39(2), 125–133. Shenk, J. S., & Westerhaus, M. O. (1994). The application of near infrared reflectance spectroscopy (NIRS) to forage analysis. In G. C. Fahey, M. Collins, D. R. Mertens, & L. E. Moser (Eds.). Forage quality, evaluation, and utilization (pp. 406–449). Soil Science Society of America: Madison, WI American Society of Agronomy, Crop Science Society of America. Sierra, V., Aldai, N., Castro, P., Osoro, K., Coto-Montes, A., & Oliván, M. (2008). Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy. Meat Science, 78(3), 248–255. Surya, R., Helies-Toussaint, C., Martin, O. C., Gauthier, T., Gueraud, F., Tache, S., & Huc, L. (2016). Red meat and colorectal cancer: Nrf2-dependent antioxidant response contributes to the resistance of preneoplastic colon cells to fecal water of hemoglobinand beef-fed rats. Carcinogenesis, 37(6), 635–645. https://doi.org/10.1093/carcin/ bgw035. Thyholt, K., & Isaksson, T. (1997). Differentiation of frozen and unfrozen beef using nearinfrared spectroscopy. Journal of the Science of Food and Agriculture, 73(4), 525–532. Tøgersen, G., Arnesen, J. F., Nilsen, B. N., & Hildrum, K. I. (2003). On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy. Meat Science, 63(4), 515–523. Wood, J. D. (2017). Chapter 20 - meat composition and nutritional value. Woodhead Publishing.
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