Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries

Abstract

Increasing consumer concern exists over the relationship between food composition and human health. Because of the known effects of fatty acids on human health, the development of a quick, inexpensive, and accurate method to directly quantify the fatty acid (FA) composition in milk would be valuable for milk processors to develop a payment system for milk pertinent to their customer requirements and for farmers to adapt their feeding systems and breeding strategies accordingly. The aim of this study was (1) to confirm the ability of mid-infrared spectrometry (MIR) to quantify individual FA content in milk by using an innovative procedure of sampling (i.e., samples were collected from cows belonging to different breeds, different countries, and in different production systems); (2) to compare 6 mathematical methods to develop robust calibration equations for predicting the contents of individual FA in milk; and (3) to test interest in using the FA equations developed in milk as basis to predict FA content in fat without corrections for the slope and the bias of the developed equations. In total, 517 samples selected based on their spectral variability in 3 countries (Belgium, Ireland, and United Kingdom) from various breeds, cows, and production systems were analyzed by gas chromatography (GC). The samples presenting the largest spectral variability were used to calibrate the prediction of FA by MIR. The remaining samples were used to externally validate the 28 FA equations developed. The 6 methods were (1) partial least squares regression (PLS); (2) PLS+repeatability file (REP); (3) first derivative of spectral data+PLS; (4) first derivative+REP+PLS; (5) second derivative of spectral data+PLS; and (6) second derivative+REP+PLS. Methods were compared on the basis of the cross-validation coefficient of determination (R2cv), the ratio of standard deviation of GC values to the standard error of cross-validation (RPD), and the validation coefficient of determination (R2v). The third and fourth methods had, on average, the highest R2cv, RPD, and R2v. The final equations were built using all GC and the best accuracy was observed for the infrared predictions of C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, C18:0, C18:1 trans, C18:1 cis-9, C18:1 cis, and for some groups of FA studied in milk (saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain FA). These equations showed R2cv greater than 0.95. With R2cv equal to 0.85, the MIR prediction of polyunsaturated FA could be used to screen the cow population. As previously published, infrared predictions of FA in fat are less accurate than those developed from FA content in milk (g/dL of milk) and no better results were obtained by using milk FA predictions if no corrections for bias and slope based on reference milk samples with known contents of FA were used. These results indicate the usefulness of equations with R2cv greater than 95% in milk payment systems and the usefulness of equations with R2cv greater than 75% for animal breeding purposes.

Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds

“…These calibration equations were developed in the EU FP 7 project RobustMilk, using a data set with MIR spectra and GC results of 1236 milk samples. The methodology used to develop the calibration equations is explained by Soyeurt et al (2011) for the calibration equations, but it should be noted that in our study updated versions of the calibrations were used, which are based on 1236 instead of 517 milk samples.…”

“…Descriptive statistics of the RobustMilk calibration equations are given in Table 1. Note that this is an updated version of the prediction equations described by Soyeurt et al (2011), in the sense that the current prediction equations are based on , 4.5 times more samples. In addition to the number of samples included in the calibration data set, the mean and the standard deviation (s.d.)…”

“…During the development of the updated calibration equations, a first assessment of the robustness of the predictions was done by a cross-validation approach to calculate the R 2 cv and SECV using the same approach as described by Soyeurt et al (2011).…”

“…For the other FAs, of which the calibration equations are validated in this study, the separation during the GC analysis was similar. The FA traits were predicted on the basis of milk (g/dl) because these predictions are more accurate than on the basis of fat (g/100 g; Soyeurt et al, 2006;Rutten et al, 2009;Soyeurt et al, 2011). The accuracy of the RobustMilk predictions was evaluated using the root mean squared error of prediction (SEV), the coefficient of determination (validation R 2 ) and the ratio of the s.d.…”

“…The approach to calculate SEV is in line with the approach to calculate SEC and SECV, which are described by Soyeurt et al (2011). The prediction bias was assessed using the average prediction error ðŷ i À y i Þand slope (b 1 ) of the linear regression, with the GC values as dependent and the predicted values as independent variable.…”