Prediction of fatty acid profiles in cow, ewe, and goat milk by mid-infrared spectrometry
2014
Ferrand-Calmels, M. | Palhière, Isabelle, Palhiere | Brochard, M. | Leray, O. | Astruc, Jean-Michel | Aurel, Marie-Rose | Barbey, Sarah | Bouvier, Frédéric, F. | Brunschwig, P. | Caillat, Hugues | Douguet, M. | Faucon-Lahalle, F. | Gelé, M. | Thomas, G. | Trommenschlager, Jean-Marie | Larroque, Helene, H. | Institut de l'élevage (IDELE) | Station d'Amélioration Génétique des Animaux (SAGA) ; Institut National de la Recherche Agronomique (INRA) | Institut Technique du Lait et des Produits Laitiers | Domaine expérimental de La Fage (LA FAGE) ; Institut National de la Recherche Agronomique (INRA) | Domaine Expérimental du Pin (DEP) ; Institut National de la Recherche Agronomique (INRA) | Domaine expérimental Bourges-La Sapinière (BOURGES) ; Institut National de la Recherche Agronomique (INRA) | Centre National Interprofessionnel de l'Economie Laitière (CNIEL) | Agro-Systèmes Territoires Ressources Mirecourt (ASTER Mirecourt) ; Institut National de la Recherche Agronomique (INRA) | This study received financial support from Apis-Gène (Paris, France), Centre National Interprofessionnel de l'Economie Laitière (CNIEL, Paris, France), the French Ministry of Agriculture (Paris, France), and France Génétique Elevage (Paris, France).
Chantier qualité GA
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Afficher plus [+] Moins [-]anglais. Mid-infrared (MIR) spectrometry was used to estimate the fatty acid (FA) composition in cow, ewe, and goat milk. The objectives were to compare different statistical approaches with wavelength selection to predict the milk FA composition from MIR spectra, and to develop equations for FA in cow, goat, and ewe milk. In total, a set of 349 cow milk samples, 200 ewe milk samples, and 332 goat milk samples were both analyzed by MIR and by gas chromatography, the reference method. A broad FA variability was ensured by using milk from different breeds and feeding systems. The methods studied were partial least squares regression (PLS), first-derivative pretreatment + PLS, genetic algorithm + PLS, wavelets + PLS, least absolute shrinkage and selection operator method (LASSO), and elastic net. The best results were obtained with PLS, genetic algorithm + PLS and first derivative + PLS. The residual standard deviation and the coefficient of determination in external validation were used to characterize the equations and to retain the best for each FA in each species. In all cases, the predictions were of better quality for FA found at medium to high concentrations (i.e., for saturated FA and some monounsaturated FA with a coefficient of determination in external validation >0.90). The conversion of the FA expressed in grams per 100 mL of milk to grams per 100 g of FA was possible with a small loss of accuracy for some FA.
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