Prediction of milk fatty acid composition by near infrared reflectance spectroscopy

International Dairy Journal 20 (2010) 182–189

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International Dairy Journal journal homepage: www.elsevier.com/locate/idairyj

Prediction of milk fatty acid composition by near infrared reflectance spectroscopy Mauro Coppa a, b, Anne Ferlay a, Christine Leroux a, Michel Jestin a, Yves Chilliard a, Bruno Martin a, Donato Andueza a, * a b

Unite´ de Recherches sur les Herbivores, UR1213, Institut National de la Recherche Agronomique, 63122 Saint-Gene`s Champanelle, France Grazing Land Management Unit, AGROSELVITER Department, University of Turin, Via Leonardo da Vinci 44, 10095 Grugliasco, Italy

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 May 2009 Received in revised form 22 October 2009 Accepted 4 November 2009

Near-infrared reflectance spectroscopy (NIRS) (700–2500 nm) was used to predict milk fatty acid (FA) composition. Broad FA variability was ensured by using experimental cow milk derived from different feeding regimes (pasture and preserved forages with or without lipid supplements). Detailed FA composition was analyzed by gas chromatography. Predictive equations (354 samples) were developed for liquid and oven-dried milk samples using modified partial least squares with cross-validation and external validation (114 samples). Coefficient of determination in external validation (R2V) and residual predictive deviation (RPD) were good (R2V  0.88; RPD  3.26) for saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), unsaturated fatty acids (UNSAT), trans FA, trans and cis-C18:1, caproic, caprilic, capric, lauric, myristic, palmitic and oleic acids in oven-dried milk, approximate for polyunsaturated fatty acid (PUFA), stearic, vaccenic and rumenic acids (R2V  0.81; RPD  3.23) and poor for linoleic, linolenic, total n-6 and n-3 acids. The quantification was more accurate for oven-dried milk, but good results were also obtained for SFA, MUFA, palmitic and oleic acids in liquid milk. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction The fatty acid (FA) profile of cows’ milk typically contains 70% saturated FA (SFA), 25% monounsaturated FA (MUFA) and 5% polyunsaturated FA (PUFA) (Grummer, 1991; Shingfield, Chilliard, Toivonen, Kairenius, & Givens, 2008), while an optimal profile for dietary total FA would be about 30% SFA (Pascal, 1996), 60% MUFA and 10% PUFA (Hayes & Khosla, 1992). Excess consumption of C14:0 and C16:0 is known to have negative effects on cholesterolaemia and coronary heart diseases (Hayes & Khosla, 1992; Hu, Stampfer, Manson, Ascherio, et al., 1999b; Shingfield et al., 2008). In contrast, C18:3n-3 has been associated with reduced risks of ischemic heart diseases (Hu, Stampfer, Manson, Rimm, et al., 1999a) while cis9trans11-conjugated linoleic acid (CLA) inhibits degenerative cellular proliferation (Khanal & Olson, 2004). Nevertheless, milk FA profiles show significant variability and milk FA composition can be optimized for human health, especially through feeding and breeding. Thus, milk FA profiles show feeding system-dependent differences, i.e. milk fat from grazing cows had lower C14:0 and C16:0 and higher cis9-C18:1, trans11-C18:1, cis9trans11-CLA and C18:3n-3 compared to milk produced by cows fed with preserved * Corresponding author. Tel.: þ33 4 73 62 40 71; fax: þ33 4 73 62 41 18. E-mail address: [email protected] (D. Andueza). 0958-6946/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.idairyj.2009.11.003

forages (hay or silage; Dewhurst, Shingfield, Lee, & Scollan, 2006; Ferlay, Martin, Pradel, Coulon, & Chilliard, 2006; Ferlay et al., 2008). Milk fat composition also shows largely increased PUFA and decreased SFA due to oilseed-supplemented diets (Chilliard & Ferlay, 2004; Chilliard, Ferlay, & Doreau, 2001; Glasser, Ferlay, & Chilliard, 2008). Moreover, some studies have highlighted breed (i.e., Ferlay et al., 2006) and genetic variables (reviewed by Arnoult & Soyeurt, 2009) related to milk FA composition, suggesting that breed selection could potentially improve milk FA profile. Growing consumer demand to be informed on the nutritional quality of foods has made it necessary for dairy producers and transformers to characterize the nutritional composition of cows’ milk, necessitating frequent and rapid milk FA measurements. However, the reference methods for FA composition analysis involve chemical steps such as extraction by solvents and derivation of FA before analysis by gas chromatography (GC), and are generally expensive and time-intensive. Near-infrared reflectance spectroscopy (NIRS), which is non-destructive, rapid, cheap and multiparametric, is an alternative technique to the current methods used for quantification of milk FA. Mid-infrared spectroscopy (MIRS) has been successfully used to determine the FA composition of oils, butters and margarines (Safar, Bertrand, Robert, Devaux, & Genot, 1994) and to predict the cis and trans content of fats and oils (Van de Voort, Ismail, & Sedman, 1995), and more recently to

M. Coppa et al. / International Dairy Journal 20 (2010) 182–189

predict C12:0, C14:0, C16:0, cis9,C16:1, C18:1 and SFA and MUFA in cows’ milk (Soyeurt et al., 2006). Alternatively, NIRS has also been successfully used to quantify FA composition in foods such as peanuts (Fox & Cruickshank, 2005), meat products (Gonza´lezMartı´n, Gonza´lez-Pe´rez, Alvarez-Garcı´a, & Gonza´lez-Cabrera, 2005; ˜ o, Ramı´rez, & Dı´az, 2007) or cheese (Lucas, Pla, Herna´ndez, Arin Andueza, Ferlay, & Martin, 2008). A high percentage of water in certain foods could interfere with NIRS analyses (Thyholt & Isaksson, 1997), as water has strong absorption bands in the NIR region that could limit the detection of the analytes. In addition, the energy absorbed by water is temperature-dependent (Bu¨ning-Pfaue, 2003). This is due to hydrogen bonds between the molecules, which alter the constant force for the covalent O–H bond and the frequency of the O–H absorption band. An increase in temperature causes disruption of hydrogen bonds by thermal collisions, resulting in a change in the absorption profile (Thyholt & Isaksson, 1997). Consequently, removing water from milk before NIRS analysis could make the quantification more accurate. The aim of this study was to explore the ability of NIRS to predict FA composition of milk analyzed in its native form or after ovendrying to remove milk water content. 2. Materials and methods 2.1. Samples A total of 419 individual cow’s milk and 49 bulk milk samples were analyzed. Individual milk samples were selected from cow feeding experiments that induced a large variability in FA composition, through the effect of oil supplementation (Chilliard, Ollier, Capitan, & Ferlay, 2003; Ferlay, Capitan, Ollier, & Chilliard, 2003; Ferlay, Ollier, Capitan, & Chilliard, 2003) and pasture biodiversity (Tornambe´ et al., 2007). Individual samples were thus obtained from cows grazed on grass or fed different preserved forages (hay and corn silage, with a forage-to-concentrate ratio varying from 60%:40% to 70%:30%) supplemented or not with different oils (fish, linseed or sunflower ranging from 2.5 to 6.5% of dry matter (DM) intake). Furthermore, bulk milk derived from 10 collection rounds (from 10 to 36 herds per collection round) which were sampled five times over the year (twice during winter and 3 times during the grazing period; Ferlay et al., 2008). After sampling, two milk subsamples were generated: the first was lyophilized and stored at 20  C for GC analysis, while the second was stored at 20  C until NIRS scanning. 2.2. Fatty acid analysis using the GC method FA were analyzed in lyophilized milk according to the method of Loor, Ferlay, Ollier, Doreau, and Chilliard (2005). Briefly, lipids in lyophilized milk samples were methylated directly using 2 mL 0.5 M-sodium methanolate plus 1-mL hexane at 50  C for 5 min, followed after cooling by the addition of 75 mL 12 M HCl at room temperature for 10 min. FA methyl esters (FAME) were recovered in 3 mL hexane and washed with 3 mL water. Samples were injected by an auto-sampler into a Trace-GC 2000 series gas chromatograph equipped with a flame ionization detector (Thermo Finnigan, Les Ulis, France). The FAME were separated on a 100 m  0.25 mm i.d. fusedsilica capillary column (CP-Sil 88, Chrompack, Middelburg, The Netherlands). Injector temperature was maintained at 250  C and detector temperature was 255  C. Initial oven temperature was held at 70  C for 1 min, ramped up by 5  C min1 to 100  C (held for 2 min), and then ramped up by 10  C min1 to 175  C (held for 40 min) and 5  C min1 to a final temperature of 225  C (held for 15 min). The carrier gas was hydrogen. Trans isomers of C18:1, non-conjugated

183

C18:2, and CLA isomers were identified as described by Loor et al. (2005). A butter reference standard (CRM 164, Commission of the European Communities, Community Bureau of Reference, Brussels, Belgium) was used to estimate correction factors for short-chain FA (C4:0 to C10:0). Peaks of FAME were routinely identified by comparison of retention times with authentic FAME standards (GLC 463, Nu Chek Prep Inc., Elysian, MN, USA; reference mixture 47 885, Supelco, Bellefonte PA, USA) and a mixture of C18:1, C18:2 and CLA isomers (Loor et al., 2005). Isomers of CLA were identified using authentic CLA methyl ester standards (O5632, Sigma–Aldrich), and the elution order of isomers reported in the literature (Shingfield et al., 2003). 2.3. Near-infrared spectroscopy After 2 h at room temperature, each milk sample was subdivided into 2 subsamples of 1 mL and 0.5 mL, respectively. The first subsample, kept in native form, was placed in a 50 mm-diameter, 0.2 sample thickness camlock cell and scanned at 2 nm intervals from 400 to 2498 nm using a Foss NIRSystems model 6500 NIR scanning spectrometer (Foss NIRSystems, Silver Spring, MD, USA) equipped with an autocup module and controlled via ISIscan software version 2.21 (Infrasoft International LLC, State College, PA, USA). Each transflectance spectrum was time-averaged from 32 scans and it was compared with the 32 average-measurements of a ceramic reference. The second subsample was oven-dried at 40  C for 24 h on a glass microfibre filter (Whatman GF/A, 55 mm, Cat. No. 1820 055 (Whatman International Ltd, Maidstone, UK)), and placed in a 50 mm-diameter ring cup (Thyholt & Isaksson, 1997). Spectra for these second subsamples were obtained using the same instrument in reflectance mode. 2.4. Calibrations and statistics Calibrations were performed using WinISI II version 1.60 (Infrasoft International, South Atherton St. State College, PA 16801, USA). The samples were divided into calibration (n ¼ 354) and validation (n ¼ 114) sets. Milk samples provided from 42 cows (319 milk) and 35 bulk milk samples were included in the calibration set, whereas individual milk samples obtained from 13 cows (100 milk) and 14 bulk milk samples were used to validate the models. Samples for calibration and validation were chosen in order to maintain the same proportion of different feeding types in both datasets. NIR calibration equations were obtained via modified partial least squares (MPLS) regression using the range between 700 and 2498 nm. The standard normal variate and detrend (SNVD) scatter correction procedure (Barnes, Dhanoa, & Lister, 1989) were applied to the raw data. The spectra were then transformed using a mathematical first-order gap derivation (1,4,4,1), where the first digit is the number of the derivative, the second is the gap over which the derivative is calculated, the third is the number of data points in the first smoothing, and the fourth is the number of data points in the second smoothing. Critical values (T) for removing outliers were T ¼ 2.5, and two elimination passes were allowed. On completion of calibration, the model was applied to the validation set. The statistics used to develop and evaluate the calibration models included standard error of cross-validation (SECV), coefficient of determination for cross-validation (R2CV), coefficient of determination in external validation (R2V), standard error of prediction (SEP), residual coefficient of variance (RCV) and the ratio of standard deviation of reference data to the SECV (RPD), The RPD statistic provides a basis for standardizing the standard error of prediction (SEP) (Williams & Sobering, 1993). RPD should be as high as possible. RPD values greater than 10 are equivalent or better than the reference method. It can be found in applications involving

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ground grain, powdered materials liquid or slurries. RPD values greater than 5 are adequate for quality control, and RPD values greater than 2.5 are considered adequate for analytical purposes (Sinnaeve, Dardenne, Agneessens, & Biston, 1994). Similarly, quantifications were considered as poor, approximate, good and excellent for an R2 (both R2CV and R2V) value under 0.66, between 0.66 and 0.81, between 0.82 and 0.90 and above 0.91, respectively (Karoui et al., 2006). For FAs with an R2V higher than 0.81 in the oven-dried milk model, principal component analysis (PCA), based on the correlation matrix, was performed from the 354 milk samples used for the calibration dataset in order to visualize the main relationships between FA. SPSS for Windows (version 13.0; SPSS Inc., Chicago, IL, USA, 2004) was used for statistical treatments. 3. Results 3.1. FA analysis The ranges, mean values and standard deviations for milk FA composition in the calibration and validation sets are presented in Table 1. It was not possible to separately quantify isoC17:0 and trans9-C16:1, trans 6/7/8-C18:1 or trans16- and cis14-C18:1 due to their co-elution in GC analysis. These values were thus excluded from sums in which only one of the two FA had to be computed. The range mean and standard deviation values for each FA were similar within the calibration and validation sets (Table 1), except for trans10-C18:1 that presented lower variability in validation sets than calibration sets, and to a lesser extent C10:0, which presented higher variability in validation sets than calibration sets. 3.2. Fatty acid composition in liquid and oven-dried milk Averaged absorbance of NIR spectra for liquid and oven-dried milk is presented in Fig. 1. Liquid milk spectrum (Fig. 1a) presents two bands with maxima at 1940 and 1450 nm, which are characteristics of the water spectrum (Osborne & Fearn, 1988). Maxima in the oven-dried milk spectrum (Fig. 1b) are located at 1734, 1764, 2310 and 2344 nm and are related to FA absorption bands (Osborne & Fearn, 1988). Calibration statistics for the major sums of FA obtained for liquid and oven-dried milk are reported in Table 2. Total SFA and total MUFA (Fig. 2a), total unsaturated FA (UNSAT) and total cis-C18:1 were successfully predicted for both oven-dried and liquid milk, with R2CV, R2V and RPD values ranging from 0.89 to 0.97, from 0.86 to 0.95, and from 2.93 to 6.25, respectively. Satisfactory predictions were also obtained for total trans FA (Fig. 2a) and total trans-C18:1 with both sample formats, although the liquid format showed lower precision in external validation (R2V values of 0.75 and 0.72 respectively; Table 2). Total PUFA (Table 2), total CLA, and C16:0/ cis9,C18:1 ratio were better predicted for oven-dried milk (with R2CV and R2V values ranging from 0.85 to 0.89 and 0.65 to 0.75, respectively) than for liquid milk, which were only approximately predicted. Approximate predictions for total odd and branchedchain FA and poor predictions for odd short-chain FA, total n-3 FA and total n-6 FA were obtained with both techniques. When SECV and SEP values were compared, large differences were found for some determinations (i.e. 0.63 and 1.08 respectively for total CLA oven-dried milk, and 0.04 and 0.39 respectively for n-6 FA liquid milk) (Table 2). 3.3. Individual FA composition of oven-dried milk Since liquid milk models showed poorer predictability than oven-dried milk (Fig. 2), only the individual FA results of calibration

and prediction by cross-validation and external validation for ovendried milk are presented in Table 3. Among the individual SFA, C6:0, C8:0, C10:0, C12:0, C14:0 (Table 3) and C16:0 (Fig. 2b) were successfully predicted by NIRS calibration models, with R2CV ranging from 0.91 to 0.94, RPD ranging from 3.28 to 4.15, and R2V ranging from 0.88 to 0.91 for validation. C18:0 showed a good R2CV value (0.84). However, C18:0 was only approximately predicted within the validation set (R2V ¼ 0.80), in relation with the RPD (2.49) which was close to the limit of acceptability. Approximate predictions were obtained for C4:0, C13:0, isoC14:0, isoC15:0 and C17:0, with R2CV values between 0.72 and 0.76. RPD values for the calibration equations obtained for predicting these FA were lower than 2.5 (R2V values ranging from 0.63 to 0.69). C11:0, anteisoC15:0, C15:0, isoC16:0 and isoC18:0 were poorly predicted with R2V < 0.57. Good predictions were obtained for the main MUFA, oleic acid (cis9-C18:1; Fig. 2a and b) and trans 6/7/8-C18:1 and trans12-C18:1 (R2CV  0.87; RPD  2.78) (Table 3). Vaccenic acid (trans11-C18:1), trans16 þ cis14-C18:1 and cis9-C10:1 showed good prediction results in calibration (R2CV values of 0.89, 0.82 and 0.90, respectively) but lower R2V values (0.80, 0.72 and 0.81, respectively). Cis9-C14:1, trans4-C18:1, trans5C18:1, trans10-C18:1, trans13-C18:1, cis12-C18:1 and cis13-C18:1 were approximately predicted in calibration (R2CV ranging from 0.69 to 0.79). For all other minor MUFA poor predictions were observed. The most accurately predicted PUFA was rumenic acid (cis9trans11-C18:2; Table 3), which is the main isomer of conjugated linoleic acid (CLA) in dairy products. For this FA good R2CV and RPD values were shown (0.88 and 2.82, respectively), but its R2V (0.73) indicated a lower precision in validation. A similar trend was observed for cis9trans13-C18:2 and for C20:4n-6 (R2CV ¼ 0.83 and 0.84, respectively). These FA presented RPD values lower than 2.5 and R2V ¼ 0.54 and 0.65 respectively. The predictions obtained were approximate for trans12-C18:2 and C20:3n-6 (R2CV ¼ 0.73 and 0.75, respectively) and poor for linolenic (Fig. 2b) and linoleic acids and other PUFA. 3.4. Correlations among FA The relationships among milk FA are presented in Fig. 3. High correlations were found among short and medium-chain saturated FA such as C6:0, C8:0, C10:0, C12:0, C14:0 (Pearson’s correlation coefficients ranging from 0.88 to 0.98; data not shown) while lower correlations were found with C16:0 and SFA (Pearson’s correlation coefficients between 0.81 and 0.89). The MUFA and UNSAT were strongly correlated (Pearson’s correlation coefficients ¼ 0.99), as were cis9trans11-CLA and total CLA (Pearson’s correlation coefficients ¼ 0.99) and total trans FA and total trans-C18:1 (Pearson’s correlation coefficients ¼ 0.99). High correlations were also reported among trans isomers of C18:1 (Pearson’s correlation coefficients ranging from 0.72 to 0.97) and between them and their sum and the total trans FA (Pearson’s correlation coefficients ranging from 0.73 to 0.94). PUFA, vaccenic acid and rumenic acid were also well correlated, as were C18:0, oleic and total cis-C18:1 (Pearson’s correlation coefficients between 0.70 and 0.99). 4. Discussion 4.1. FA analysis There was particularly significant variability in the FA contents of the milk samples, covering at least the full scale of variability reported in the literature for milk from groups of cows (Chilliard & Ferlay, 2004; Chilliard et al., 2007; Glasser et al., 2008; Palmquist Beaulieu, & Barbano, 1993).

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Table 1 Fatty acid (FA) composition (expressed in g 100 g1 of total FA) determined gas chromatography analysis.a Fatty acids

C4:0 C6:0 C8:0 C10:0 cis9-C10:1 C11:0 C12:0 C13:0 isoC14 C14:0 isoC15 anteisoC15 cis9-C14:1 C15:0 isoC16 C16:0 isoC17 þ trans9-C16:1 cis9-C16:1 C17:0 isoC18 cis9-C17:1 C18:0 trans4-C18:1 trans5-C18:1 trans6/7/8-C18:1 trans9-C18:1 trans10-C18:1 trans11-C18:1 trans12-C18:1 trans13-C18:1 cis9-C18:1 cis11-C18:1 cis12-C18:1 cis13-C18:1 cis15-C18:1 trans16 þ cis14-C18:1 cis9trans13-C18:2 cis9trans12-C18:2 trans11cis15-C18:2 cis9cis12-C18:2 C20:0 cis11-C20:1 C18:3n-3 cis9trans11-CLA C22:0 C20:3n-6 C20:4n-6 C20:5n-3 C24:0 C22:5n-3 SFA MUFA PUFA UNSAT S CLA S trans FA S trans-18:1 S cis18:1 S odd and branched FAb S odd short-chain FAc n-3 FA n-6 FA C16:0/cis9-C18:1 ratio

Calibration set (n ¼ 354)

SEL

Range

Mean

SD

CV

1.04–4.83 0.66–3.03 0.32–1.95 0.69–4.98 0.04–0.61 0–0.15 0–5.97 0.05–0.38 0.03–0.4 4–15.93 0.1–0.52 0.22–1.15 0.19–1.93 0.59–1.75 0.09–0.74 13.13–39.89 0.28–1.31 0.26–3.75 0.26–0.92 0–0.2 0.11–0.43 1.23–16.15 0–0.08 0–0.09 0.05–1.5 0.09–1.16 0.1–11.18 0.6–15.35 0.08–1.61 0.2–4.63 4.66–32.54 0.29–2.25 0.04–1.73 0.02–0.27 0–2.87 0.04–1.36 0.06–1.45 0–0.3 0–2.35 0.64–3.78 0.01–0.22 0–0.64 0.09–1.41 0.26–7.18 0–0.12 0–0.18 0–0.23 0–0.74 0–0.12 0–0.64 34.86–78.24 16.48–49.77 1.94–14.07 19.45–60.33 0.26–7.83 1.42–31.84 1.29–22.13 6.39–34.75 1.63–5.24 0–0.17 0.07–1.39 1.29–3.31 0.48–6.33

2.68 1.72 0.97 2.20 0.23 0.04 2.70 0.15 0.12 9.38 0.27 0.55 0.84 1.11 0.29 23.08 0.76 1.29 0.54 0.05 0.21 9.56 0.02 0.02 0.57 0.52 1.11 5.22 0.63 1.10 21.26 0.61 0.38 0.10 0.27 0.49 0.42 0.09 0.32 1.48 0.11 0.05 0.51 2.60 0.05 0.06 0.06 0.05 0.04 0.08 55.52 34.64 5.86 40.50 2.70 12.59 9.19 22.61 3.32 0.05 0.45 2.03 1.40

0.775 0.643 0.424 1.088 0.136 0.023 1.216 0.065 0.057 3.182 0.099 0.186 0.376 0.227 0.112 7.044 0.216 0.435 0.139 0.029 0.063 3.040 0.022 0.024 0.404 0.316 1.528 3.517 0.401 0.731 6.541 0.245 0.341 0.050 0.15 0.287 0.284 0.083 0.487 0.439 0.029 0.096 0.219 1.737 0.019 0.032 0.040 0.091 0.021 0.080 12.122 9.947 2.356 11.872 1.834 7.889 5.585 6.723 0.817 0.038 0.238 0.388 1.155

28.9 37.5 43.8 49.5 58.8 59.9 45.1 43.3 46.7 33.9 36.7 33.9 44.5 20.4 38.9 30.5 28.5 33.7 25.9 61.4 29.9 31.8 95.2 101.2 71.4 61.1 137.4 67.4 63.8 66.6 30.8 40.3 89.8 50.9 55.5 58.5 67.8 94.5 153.5 29.6 27.0 193.8 43.0 66.8 35.1 56.3 61.4 166.7 54.4 99.4 21.8 28.7 40.2 29.3 67.9 62.6 60.8 29.7 24.6 77.6 53.1 19.1 82.7

0.11 0.08 0.06 0.13 – – 0.13 – – 0.25 – – 0.03 – – 0.19 – 0.04 – – – 0.18 – – 0.01 0.01 – – – – 0.03 0.48 – – – – 0.01 – – 0.03 – – 0.03 0.01 – – – – – 0.01 0.40 0.46 0.10 – – 0.20 – – – – – – –

Validation set (n ¼ 114) Range

Mean

SD

CV

1.49–4.45 0.75–3.09 0.35–1.78 0.1–4.44 0.03–0.53 0–0.16 1.21–5.42 0.05–0.37 0–0.25 4.65–15.22 0.13–0.46 0.26–0.99 0.31–1.88 0.66–1.69 0.13–0.52 14.32–39.32 0–1.31 0.77–2.12 0.31–0.83 0–0.12 0.12–0.46 1.47–15.65 0–0.07 0–0.09 0.1–1.24 0.09–1.12 0.13–3.18 0.51–14.97 0.11–1.32 0–3.85 4.9–33.82 0.36–1.05 0.06–1.31 0.03–0.21 0.04–2.18 0.08–1.02 0.07–1.02 0–0.23 0–1.52 0.77–2.47 0.03–0.17 0–0.25 0.12–1.09 0.24–6.73 0.01–0.1 0.01–0.12 0–0.12 0–0.18 0–0.09 0–0.38 38.86–78 17.27–48.77 1.99–11.26 19.7–57.04 0.24–7.06 1.67–29.11 1.36–19.96 6.29–35.13 2.17–4.75 0–0.2 0.17–1.14 0.84–2.53 0.49–6.56

2.79 1.80 1.02 2.31 0.24 0.04 2.81 0.15 0.13 9.69 0.26 0.53 0.84 1.09 0.30 23.61 0.68 1.23 0.55 0.05 0.21 9.96 0.02 0.02 0.53 0.49 0.76 4.97 0.59 0.97 21.69 0.56 0.36 0.09 0.23 0.47 0.36 0.08 0.26 1.48 0.11 0.04 0.48 2.39 0.05 0.05 0.06 0.04 0.03 0.07 57.17 34.09 5.49 39.57 2.48 11.37 8.35 22.93 3.31 0.05 0.58 1.60 1.36

0.680 0.589 0.405 1.077 0.118 0.025 1.181 0.062 0.049 3.085 0.088 0.141 0.330 0.204 0.083 7.110 0.214 0.306 0.137 0.023 0.069 2.883 0.020 0.021 0.360 0.297 0.551 3.537 0.371 0.655 6.474 0.132 0.291 0.039 0.235 0.271 0.232 0.080 0.375 0.342 0.026 0.052 0.214 1.604 0.018 0.021 0.028 0.030 0.016 0.049 11.590 9.635 2.050 11.331 1.684 7.473 5.318 6.657 0.711 0.042 0.245 0.341 1.060

24.4 32.7 39.5 46.6 49.7 63.6 42.1 40.8 38.1 31.8 33.1 26.4 39.2 18.7 28.2 30.1 31.6 24.9 24.9 51.1 33.1 28.9 90.0 97.9 67.7 60.7 72.9 71.2 62.8 67.3 29.8 23.7 80.0 42.5 103.0 57.9 64.5 94.2 144.7 23.1 23.0 136.8 44.8 67.0 34.6 39.2 45.2 79.9 47.4 71.1 20.3 28.3 37.4 28.6 68.0 65.7 63.7 29.0 21.5 78.4 42.0 21.3 78.1

a Abbreviations: CV, coefficient of variation; SD, standard deviation; SEL, standard error of laboratory calculated as the standard deviation of a standard sample; - not available. SFA, saturated fatty acids; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; UNSAT, unsaturated fatty acids; CLA, conjugated linoleic acid. b S (C5:0; C7:0; C9:0; C11:0; C13:0; isoC14:0; isoC15:0; anteisoC15:0; C151:0; isoC16:0; C17:0; isoC18:0; C17:1). c S (C5:0; C7:0; C9:0).

4.2. Model comparisons In general, results were more reliable for oven-dried milk than liquid milk (Fig. 2). As previously described, water properties in

liquid milk could interfere with NIRS analyses (Thyholt & Isaksson, 1997). Consequently, removing water from the milk before NIRS analysis led to more accurate quantification. Nevertheless, liquid milk analysis can determine fat, protein, lactose and total

186

Average absorbance (log (1/R))

a

M. Coppa et al. / International Dairy Journal 20 (2010) 182–189

4.3. Sums of FA

2.500

We report well-predicted total SFA, MUFA, UNSAT, trans FA and to a lesser extent PUFA contents for the oven-dried milk samples. These results are in accordance with well-predicted SFA, MUFA, PUFA and total trans FA from cheese using NIRS (Lucas et al., 2008). NIRS has also been applied to beef, pork and rabbit meat FA, with good predictions for SFA, MUFA and PUFA (Gonza´lez-Martı´n et al., 2005; Pla et al., 2007; Realini, Duckett, & Windham, 2004). Satisfactory results in calibration were reported by Soyeurt et al. (2006) for milk SFA and MUFA (expressed in g dL1 of milk) using MIRS, but with approximate-to-poor R2CV values of FA expressed in g 100 g1 of total FA. Our high R2CV values are probably linked to the wider variability of the dataset (RPD of 6.25 vs 1.69 and 5.54 vs 2.54, for SFA and MUFA, respectively; Table 2). The regression plots of measured versus predicted values for PUFA trans FA, and SFA particularly in liquid milk, but also for PUFA in oven-dried milk (Fig. 2) shows a tendency towards a non-linear relationship. The use of regression methods that take into account the risk of non-linearity (LOCAL regression or neural networks equations) could eventually improve the NIRS calibration models for these determinations.

2.000

1.500

1.000

0.500

0.000 700

900

1100

1300

1500

1700

1900

2100

2300

2500

Wavelength (nm)

Average absorbance (log (1/R))

b

0.350 0.300 0.250 0.200 0.150

4.4. Individual FA

0.100 0.050 0.000 -0.050 700

900

1100

1300

1500

1700

1900

2100

2300

2500

Wavelength (nm)

Fig. 1. Averaged absorbance (log (1/R)) of near-infrared spectra for liquid (a) and ovendried (b) milk.

solids contents. Thus, it would be necessary to analyze liquid and oven-dried samples to obtain both results with high reliability levels, or to analyze only liquid samples if the objective was to predict certain FA only (i.e., SFA, MUFA, palmitic and oleic acids), for which good reliability is achievable using NIRS on liquid milk (Fig. 2).

Our results found better prediction for milk FA in high concentrations than for minor FA. Quality of NIRS prediction also seems to be related to FA concentration in cheese (Lucas et al., 2008) and meat (Gonza´lez-Martı´n et al., 2005; Pla et al. 2007; Realini et al., 2004). Similarly, Soyeurt et al. (2006) found better results with MIRS on milk for the FA present in higher concentrations. In addition to the FA concentration effect on predictions, as suggested by Windham and Morrison (1998), the failure to accurately determine certain individual FA was probably due to similarities in their NIR absorption patterns, since different FA have the same absorbing molecular group (–CH2–). NIRS models developed in this study for oven-dried milk allowed a very good prediction of individual SFA (C6:0; C8:0; C10:0; C12:0; C14:0 and C16:0). For these FA, previous attempts using NIRS to predict the major FA in animal products reported conflicting results, probably due to different matrix responses and to

Table 2 Model statistics for the fatty acid (FA) composition (sums of FA) of oven-dried and liquid milk models.a Fatty acids

Oven-dried milk

Liquid milk

Calibration set

SFA MUFA PUFA UNSAT S CLA S trans FA S trans 18:1 S cis-18:1 S odd and branched FAb S odd short-chain FAc n-3 FA n-6 FA C16 - cis9-C18:1 ratio

Validation set

Calibration set

Validation set

N

T

SECV

R2CV

RPD

SEP

BIAS

R2 V

N

T

SECV

R2CV

RPD

SEP

BIAS

R2V

333 333 329 330 332 331 336 329 346 326 337 332 331

7 7 5 7 6 8 7 7 7 6 7 6 7

1.94 1.81 0.87 2.23 0.63 1.58 1.15 1.64 0.41 0.02 0.14 0.30 0.4

0.97 0.97 0.85 0.97 0.87 0.96 0.96 0.94 0.73 0.62 0.64 0.39 0.89

6.25 5.54 2.57 5.36 2.77 4.82 4.79 3.87 1.94 1.59 1.65 0.20 2.69

2.60 2.96 1.05 3.25 1.08 1.85 1.40 1.70 0.39 0.03 0.16 0.29 0.59

0.42 0.56 0.09 0.72 0.05 0.42 0.26 0.22 0.01 0.01 0.03 0.01 0.06

0.95 0.91 0.75 0.92 0.65 0.95 0.93 0.94 0.69 0.60 0.62 0.20 0.69

328 331 329 331 333 325 333 336 334 337 332 331 321

7 7 6 7 8 8 7 8 7 8 7 7 7

3.05 2.70 0.98 3.22 0.88 2.64 2.06 2.24 0.43 0.02 0.18 0.04 0.39

0.94 0.93 0.80 0.93 0.75 0.88 0.86 0.89 0.73 0.56 0.45 0.74 0.77

3.96 3.68 2.25 3.69 1.98 2.85 2.64 2.93 1.91 1.49 1.35 7.72 1.69

3.77 3.37 1.34 3.93 1.15 3.81 2.90 2.55 0.51 0.11 0.22 0.39 0.74

0.81 1.16 0.21 1.42 0.31 0.90 0.69 0.55 0.13 0.016 0.03 0.10 0.11

0.91 0.89 0.65 0.90 0.60 0.75 0.72 0.86 0.57 0.03 0.20 0.00 0.55

a Abbreviations: N, number of samples used to develop the model; T, Number of PLS terms in the model; R2CV, coefficient of determination in cross-validation; RPD, ratio of standard deviation of reference data in the calibration set to standard error of cross-validation; R2V, coefficient of determination in external validation; SEP, standard error of prediction; SECV, standard error of cross-validation; SFA, saturated fatty acids; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; UNSAT, unsaturated fatty acids; CLA, conjugated linoleic acid. b S (C5:0; C7:0; C9:0; C11:0; C13:0; isoC14:0; isoC15:0; anteisoC15:0; C151:0; isoC16:0; C17:0; isoC18:0; C17:1). c S (C5:0; C7:0; C9:0).

M. Coppa et al. / International Dairy Journal 20 (2010) 182–189

a 85

85

SFA

75

75

65

65

55

y=1.0x - 0.46 R2 = 0.950

45 35

50

45

55

65

75

55 y=0.91x + 4.21 R2 = 0.907

45

85

45

55

65

75

Liquid milk 45

C16:0

35

35

25

25 y=1.04x - 1.70 R2 = 0.913

15

85

10 15 20 25 30 35 40 45

40

40

cis9-C18:1

40

30

30

30

30

20

20

y=0.98x + 1.3 R2 = 0.909

20

y=0.97x + 1.67 R2 = 0.891

10

10 10 12

20

30

40

10

50 12

PUFA

20

30

40

50

PUFA

Measured values

40

20

y=1.03x - 1.24 R2 = 0.864

15

10 15 20 25 30 35 40 45

MUFA

C16:0

5

5

35 50

MUFA

Oven-dried milk 45

SFA

35 35

Measured values

b

Liquid milk

Oven-dried milk

187

10

y=1.04x - 0.65 R2 = 0.929

0

0

0 5 10 15 20 25 30 35 40

0 5 10 15 20 25 30 35 40 1.2

1.2

C18:3n-3

10

8

8

0.8

0.8

6

6

0.6

0.6

4

4

0.4

y=0.92x + 0.36 R2 = 0.745

0 0 30

2

4

6

8

0 0

10 12 30

trans FA

25

25

20

20

15

15

10

10

y=1.09x - 0.60 R2 = 0.948

5 0 0

5

y=0.75x + 1.53 R2 = 0.650

2

10 15 20 25 30

2

4

6

8

1

0.2 0

5.0

0.4 y=0.90x + 0.07 R2 = 0.477

cis9trans11-CLA

5 0 0

5

10 15 20 25 30

Predicted values

y=0.69x + 0.11 R2 = 0.146

0.2 0.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 5.0

4.0

4.0

3.0

3.0

1.0 0.0

cis9trans11-CLA

2.0

2.0

y=0.97x + 1.24 R2 = 0.751

C18:3n-3

1.0

0 0.2 0.4 0.6 0.8 1 1.2

10 12

trans FA

y=1.06x - 0.68 R2 = 0.863

10

10

2

cis9-C18:1

y=0.84x + 0.49 R2 = 0.733 0.0 1.0 2.0 3.0 4.0 5.0

y=0.80x + 0.69 R2 = 0.593

1.0 0.0

0.0 1.0 2.0 3.0 4.0 5.0

Predicted values

Fig. 2. Linear regression plots of measured versus predicted values (g 100 g1 of total fatty acids (FA)) within the validation set for (a) saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), total trans FA and (b) palmitic (C16:0), oleic (cis9-C18:1), rumenic (cis-9 trans11-CLA) and linolenic (C18:3n-3) acids in ovendried and liquid milks.

variability in the FA composition of the samples. For example, Lucas et al. (2008) reported good predictions for C4:0, C6:0, C10:0, C14:0 and C16:0 but approximate predictions for C8:0, C12:0 and C18:0 in cheese. Gonza´lez-Martı´n et al. (2005) obtained good predictions for C14:0, C16:0 and C18:0 and an approximate prediction for C12:0 in pork meat, whereas Realini et al. (2004) reported poor predictions for C14:0 and C16:0 but a good prediction for C18:0 in beef meat. Using MIR spectroscopy, Soyeurt et al. (2006) did obtain quite good predictions in cows’ milk, but only for C12:0, C14:0 and C16:0 using FA expressed in g dL1 of milk, and the results with FA expressed in g 100 g1 of total FA were poor. Regarding MUFA, in agreement with our results, the literature reports good predictions for oleic acid content in animal products (Lucas et al., 2008; Pla et al., 2007; Realini et al., 2004; Windham & Morrison, 1998). Among the few data available on prediction of vaccenic acid by NIRS, Lucas et al. (2008) reported good prediction in cheese, in accordance with our results on oven-dried milk but in contrast to Pla et al. (2007) who reported poor prediction of vaccenic acid FA in rabbit meat.

The results on PUFA showing good prediction of rumenic acid in oven-dried milk are in agreement with results obtained from cheese by Lucas et al. (2008). The ability of NIRS to accurately predict linolenic acid (cis9cis12cis15-C18:3) in animal products has been reported by other authors (Dalle Zotte, Berzaghi, Jansson, & Andrighetto, 2006; Gonza´lez-Martı´n et al., 2005; Lucas et al., 2008; Realini et al., 2004), We obtained a poor prediction of linoleic acid (cis9cis12-C18:2) as was the case when using NIRS on cheese (Lucas et al., 2008) as well on other bovine products (Realini et al., 2004; Windham & Morrison, 1998). The tendency of the results from both liquid and oven-dried milk models to give certain FA (i.e., PUFA and total CLA isomers) with lower precision in external validation means that milk samples of validation sets are not well represented in the calibration set, despite the high number of samples from both populations. Improving the calibration dataset could also help reduce differences in prediction between oven-dried and liquid milk. In addition, a lack of precision in validation could also be partly linked to probable non-linear relationships of certain models (i.e., PUFA

188

M. Coppa et al. / International Dairy Journal 20 (2010) 182–189 1.2

Table 3 Model statistics for the fatty acid composition (individual FA) of oven-dried milk.a

1

Fatty acids

Calibration set

Validation set

N

SECV

R2CV

RPD

RCV

SEP

BIAS

R2V

0.8

C4:0 C6:0 C8:0 C10:0 cis9-C10:1 C11:0 C12:0 C13:0 isoC14 C14:0 isoC15 anteisoC15 cis9-C14:1 C15:0 isoC16 C16:0 isoC17 þ trans9-C16:1 cis9-C16:1 C17:0 isoC18 cis9-C17:1 C18:0 trans4-C18:1 trans5-C18:1 trans6/7/8-C18:1 trans9-C18:1 trans10-C18:1 trans11-C18:1 trans12-C18:1 trans13-C18:1 cis9-C18:1 cis11-C18:1 cis12-C18:1 cis13-C18:1 cis15-C18:1 trans16 þ cis14-C18:1 cis9trans13-C18:2 cis9trans12-C18:2 trans11cis15-C18:2 cis9cis12-C18:2 C20:0 cis11-C20:1 C18:3n-3 cis9trans11-CLA C22:0 C20:3n-6 C20:4n-6 C20:5n-3 C24:0 C22:5n-3

342 332 334 328 337 330 324 345 331 334 343 342 335 346 332 335 342 338 345 331 333 333 319 332 332 334 323 340 338 325 340 329 333 325 336 327 330 342 285 341 344 279 344 332 340 341 326 324 328 327

0.37 0.17 0.10 0.28 0.04 0.01 0.33 0.03 0.03 0.88 0.05 0.11 0.17 0.14 0.06 1.83 0.10 0.22 0.07 0.02 0.03 1.17 0.01 0.01 0.12 0.10 0.49 1.16 0.14 0.29 1.71 0.10 0.15 0.02 0.08 0.12 0.10 0.04 0.22 0.29 0.02 0.03 0.15 0.59 0.01 0.01 0.01 0.02 0.02 0.03

0.76 0.92 0.94 0.93 0.90 0.65 0.91 0.72 0.73 0.92 0.72 0.58 0.78 0.58 0.60 0.93 0.77 0.62 0.73 0.42 0.65 0.84 0.79 0.69 0.90 0.90 0.47 0.89 0.87 0.77 0.92 0.65 0.67 0.74 0.71 0.82 0.83 0.73 0.58 0.43 0.29 0.70 0.47 0.88 0.33 0.75 0.84 0.61 0.32 0.64

2.05 3.64 4.15 3.69 3.23 1.57 3.28 1.88 1.87 3.57 1.88 1.52 2.12 1.54 1.53 3.81 2.11 1.58 1.92 1.29 1.68 2.49 2.15 1.79 3.18 3.12 1.11 2.98 2.78 2.03 3.83 2.45 2.27 2.50 1.88 2.39 2.42 1.88 1.36 1.31 1.19 1.42 1.37 2.82 1.23 1.95 2.33 1.17 1.20 1.41

13.81 9.88 10.31 12.73 17.39 25.00 12.22 20.00 20.83 9.38 18.52 20.00 20.24 12.61 20.00 7.92 13.16 16.90 12.96 34.00 14.29 12.10 42.86 43.48 21.05 18.27 41.44 22.22 21.90 26.36 7.93 16.39 39.47 20.00 29.63 24.49 23.81 44.44 68.75 19.59 18.18 60.00 29.41 22.69 20.00 16.67 16.67 40.00 37.50 36.25

0.42 0.21 0.13 0.34 0.05 0.02 0.41 0.04 0.03 1.07 0.05 0.10 0.22 0.14 0.07 2.20 0.13 0.25 0.08 0.02 0.05 1.31 0.01 0.01 0.13 0.11 0.38 1.64 0.13 0.43 1.77 0.13 0.21 0.02 0.10 0.14 0.17 0.05 0.34 0.28 0.02 0.05 0.16 0.87 0.02 0.02 0.02 0.02 0.01 0.04

0.04 0.02 0.01 0.02 0.01 0.00 0.03 0.00 0.01 0.05 0.01 0.01 0.01 0.03 0.01 0.63 0.02 0.08 0.01 0.00 0.01 0.01 0.00 0.00 0.01 0.03 0.05 0.40 0.03 0.04 0.30 0.01 0.08 0.01 0.02 0.00 0.02 0.00 0.02 0.01 0.00 0.00 0.02 0.13 0.00 0.00 0.00 0.01 0.00 0.01

0.66 0.88 0.90 0.91 0.81 0.57 0.89 0.69 0.66 0.88 0.63 0.50 0.57 0.53 0.40 0.91 0.65 0.44 0.65 0.31 0.48 0.80 0.70 0.63 0.87 0.88 0.53 0.80 0.89 0.56 0.93 0.29 0.59 0.70 0.58 0.72 0.54 0.60 0.26 0.34 0.17 0.34 0.48 0.73 0.18 0.51 0.65 0.64 0.29 0.37

0.6

a Abbreviations: N, number of samples used to develop the model; R2CV, coefficient of determination in cross-validation; RCV, residual coefficient of variance; RPD, ratio of standard deviation of reference data in calibration set to standard error of cross-validation; R2V, coefficient of determination in external validation; SEP, standard error of prediction; SECV, standard error of cross-validation; CLA, conjugated linoleic acid.

for both oven-dried and liquid milk and total trans FA for liquid milk only; Fig. 2). The significant positive correlations between some FA, which are due to their common digestive or metabolic origin (reviewed by Chilliard et al., 2007) raises the hypothesis that the predictability of these FA could be due to either their actual absorbance or the correlations between FA. This is the case for C6:0, C8:0, C10:0, C12:0 and C14:0, whose reciprocal correlation coefficients were higher than their R2V. Similar patterns may be reproduced between rumenic and vaccenic acids, among trans-C18:1 isomers and between the sums of FA and their relative major constituents (i.e., total cis isomers of C18:1 and oleic acid, total SFA and palmitic acid,

C16:0/cis9-C18:1

trans11-C18:1 cis9trans11CLA trans12-C18:1 trans9-C18:1

PC2: 11.0%

0.4

C16:0 C12:0 C14:0 C10:0 0 C8:0 C6:0

0.2

cis9trans13trans6/7/8- C18:2 C18:1 trans16+cis14C18:1

-0.2 -0.4

cis9-C18:1

-0.6

C18:0 -0.8 -1 -1.2 -1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

PC1:76.5% Fig. 3. Principal component analysis (PCA) of the data obtained on the 354 milk samples corresponding to the calibration dataset; variables projected on the PC1 and PC2 describing the relationships among fatty acids that gave good prediction in external validation (R2V > 0.81) for the oven-dried dataset (CLA, conjugated linoleic acid).

total CLA isomers and rumenic acid). The prediction models for palmitic and stearic acid, trans12-C18:1 and total SFA seem to be based solely on their specific spectral absorbances. 5. Conclusions The results obtained showed that NIRS can be used to satisfactorily predict FA sums and ratios (i.e., SFA, MUFA, PUFA, UNSAT, total trans FA, total trans-C18:1 and total cis-C18:1, total CLA, and C16:0/cis9-C18:1 ratio). Good results were also obtained for individual milk FA present in medium-to-high concentrations (i.e., caproic, caprilic, capric, lauric, myristic, palmitic, stearic, oleic, vaccenic acids and cis9trans11-CLA), but the quality of prediction decreased when FA were present in low to very-low concentrations. Oven-drying milk to remove water content before NIRS analysis strongly improved the accuracy of quantification. However, the disadvantage of this oven-dried milk-based methodology is that drying the samples requires 24 h before results can be obtained. Nevertheless, it remains feasible to analyze only liquid samples if the objective is to predict the content of selected FA (i.e., SFA, MUFA, palmitic and oleic acids) for which good reliability is achievable using NIRS on liquid milk. NIRS could be used for routine measurements of milk FA composition, particularly to establish conformance with new food labeling rules, such as for total trans FA in foods. More research should be done in order to try to improve the quality of prediction for linoleic acid, linolenic acid, total n-3 FA, total n-6 FA and eventually, for the milk-based FA present in low concentrations. Differences between SECV and SEP for some FA should be reduced. Moreover, it could be useful to test other calibration methods capable of combining linear and non-linear relations in an attempt to improve prediction accuracy. Acknowledgements We thank I. Constant, C. Labonne and P. Capitan from the URH (Herbivore Research Unit UR1213, INRA, Clermont-Ferrand-Theix,

M. Coppa et al. / International Dairy Journal 20 (2010) 182–189

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