[en] 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 crossvalidation 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.
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Bibliography
Coffey M.P., Simm G., Oldham J.D., Hill W.G., Brotherstone S. Genotype and diet effects on energy balance in the first three lactations of dairy cows. J. Dairy Sci. 2004, 87:4318-4326.
Coleman J., Pierce K.M., Berry D.P., Brennan A., Horan B. The influence of genetic selection and feed system on the reproductive performance of spring-calving dairy cows within future pasture-based production systems. J. Dairy Sci. 2009, 92:5258-5269.
Collomb M., Bühler T. Analyse de la composition en acides gras de la graisse de lait. Mitt. Lebensm. Hyg. 2000, 91:306-332.
Dal Zotto R., De Marchi M., Cecchinato A., Penasa M., Cdro M., Carnier P., Gallo L., Bittante G. Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy. J. Dairy Sci. 2008, 91:4103-4112.
Hruschka W.R. Data analysis: Wavelength selection methods. Near-Infrared Technology in the Agricultural and Food Industries 1987, 35-55. American Association of Cereal Chemists, St Paul, MN. P. Williams, K. Norris (Eds.).
Milk and milk products. Extraction methods for lipids and liposoluble compounds. ISO 14156 :2001; IDF 172:2001 2001, ISO, International Organisation for Standardisation, Geneva, Switzerland.
Milk fat. Preparation of fatty acid methyl esters. ISO 15884:2002; IDF 182:2002 2002, ISO, International Organisation for Standardisation, Geneva, Switzerland.
Jensen R.G. Handbook of Milk Composition 1995, Academic Press, San Diego, CA.
Prendiville R., Lewis E., Pierce K.M., Buckley F. Comparative grazing behavior of lactating Holstein-Friesian, Jersey, and Jersey × Holstein-Friesian dairy cows and its association with intake capacity and production efficiency. J. Dairy Sci. 2010, 93:764-774.
Rutten M.J.M., Bovenhuis H., Hettinga K.A., Van Vanlenberg H.J.F., van Arendonk J.A.M. Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer. J. Dairy Sci. 2009, 92:6202-6209.
Soyeurt H., Dardenne P., Dehareng F., Bastin C., Gengler N. Genetic parameters of saturated and monounsaturated fatty acid content and the ratio of saturated to unsaturated fatty acids in bovine milk. J. Dairy Sci. 2008, 91:3611-3626.
Soyeurt H., Dardenne P., Dehareng F., Lognay G., Veselko D., Marlier M., Bertozzi C., Mayeres P., Gengler N. Estimating fatty acid content in cow milk using mid-infrared spectrometry. J. Dairy Sci. 2006, 89:3690-3695.
Soyeurt H., Dehareng F., Mayeres P., Bertozzi C., Gengler N. Variation of delta9-desaturase activity in dairy cattle. J. Dairy Sci. 2008, 91:3211-3224.
Soyeurt H., Misztal I., Gengler N. Genetic variability of milk components based on mid-infrared spectral data. J. Dairy Sci. 2010, 93:1722-1728.
Westerhaus M.O. Improving repeatability of NIR calibrations across instruments. Proc. 3rd Int. Conf. Near Infrared Spectroscopy, Brussels, Belgium 1990, 671-674. Agricultural Research Centre, Gembloux, Belgium.
Williams C.M. Dietary fatty acids and human health. Ann. Zootech. 2000, 49:165-180.
Williams P. Near-infrared technology-Getting the best out of light 2007, PDK Grain, Nanaimo, Canada.
Williams P.C., Sobering D.C. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J. Near Infrared Spectrosc. 1993, 1:25-32.
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