Application d’un algorithme génétique en spectrométrie moyen infrarouge pour estimer le profil en acides gras du lait de chèvre Use of genetic algorithm on mid-infrared spectrometric data: Application to estimate the fatty acid profile of goat milk Marion Ferrand, B. Huquet, F. Bouvier, H. Caillat, F. Barillet, M. Brochard, F. Faucon, H. Larroque, O. Leray, I. Palhière 1&2 Institut de l’Elevage, 149 rue de Bercy, 75595 Paris cedex 12 E-mail : marion.ferrand@inst-elevage.asso.fr Abstract The new challenges of the dairy industry require an accurate estimation of fine milk composition. The mid-infrared (MIR) spectrometry method appears to be a good, fast and cheap method for assessing milk fatty acid profile. Although partial least squares (PLS) regression is a very useful and powerful method to determine fine milk composition from spectra, the estimations are not always very accurate and stable over time. Therefore a genetic algorithm (GA) combined with a PLS was used to produce models with a reduced number of wavelengths and a better accuracy. Number of wavelengths to consider is reduced substantially by 4 and accuracy is increased on average by 7%. Keywords : mid-infrared (MIR) spectrometry, goat milk, fatty acid, genetic algorithms, Partial Least Squares (PLS) regression Résumé L’analyse de la composition fine en acides gras (AG) du lait en routine est un préalable à toute démarche visant à améliorer la qualité nutritionnelle ...