, Number of samples of calibration data set after elimination of outliers

, s y,x = residual SD for validation data set

=. Lea,

, CLA = conjugated linoleic acid

, ALA = ?-linolenic acid

L. Alonso, L. Fontecha, L. Lozada, M. J. Fraga, and M. Juarez, Fatty acid composition of caprine milk: Major, branched chain, and trans fatty acids, J. Dairy Sci, vol.82, pp.878-884, 1999.

D. Andueza, J. Rouel, Y. Chilliard, C. Leroux, and A. Ferlay, Prediction of the goat milk fatty acids near infrared reflectance spectroscopy, Eur. J. Lipid Sci. Technol, vol.115, pp.612-620, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02649107

, Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail), ANSES, 2011.

, Impact des pratiques en alimentation animale sur la composition en acides gras des produits animaux destinés à l'Homme, ANSES, 2011.

. Aoac-international, Official Methods of Analysis, 2000.

V. M. Arnould and H. Soyeurt, Genetic variability of milk fatty acids, J. Appl. Genet, vol.50, pp.29-39, 2009.

A. Carta, S. Casu, M. G. Usai, M. Addis, M. Fiori et al., Investigating the genetic component of fatty acid content in sheep milk, Small Rumin. Res, vol.79, pp.22-28, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01193506

A. Cecchinato, M. De-marchi, L. Gallo, G. Bittante, and P. Carnier, Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk, J. Dairy Sci, vol.92, pp.5304-5313, 2009.

Y. Chilliard, A. Ferlay, J. Rouel, and G. Lamberet, A review of nutritional and physiological factors affecting goat milk lipid synthesis and lipolysis, J. Dairy Sci, vol.86, pp.1751-1770, 2003.

Y. Chilliard, F. Glasser, A. Ferlay, L. Bernard, J. Rouel et al., Diet, rumen biohydrogenation and nutritional quality of cow and goat milk fat, Eur. J. Lipid Sci. Technol, vol.109, pp.828-855, 2007.
URL : https://hal.archives-ouvertes.fr/hal-02664078

Y. Chilliard, J. Rouel, and C. Leroux, Goat's alpha-s1 casein genotype influences its milk fatty acid composition and delta-9 desaturation ratios, Anim. Feed Sci. Technol, vol.131, pp.474-487, 2006.
URL : https://hal.archives-ouvertes.fr/hal-02663227

M. Coppa, A. Ferlay, C. Chassaing, C. Agabriel, F. Glasser et al., Prediction of bulk milk fatty acid composition based on farming practices collected through on-farm surveys, J. Dairy Sci, vol.96, pp.4197-4211, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02647033

M. Coppa, A. Ferlay, C. Leroux, M. Jestin, Y. Chilliard et al., Prediction of milk fatty acid composition by near infrared reflectance spectroscopy, Int. Dairy J, vol.20, pp.182-189, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02659184

P. Croiseau, A. Legarra, F. Guillaume, S. Fritz, A. Baur et al., Fine-tuning genomic evaluations in dairy cattle through SNP preselection with the Elastic-Net algorithm, Genet. Res. (Camb.), vol.93, pp.409-417, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01000269

L. F. De-la-fuente, E. Barbosa, J. A. Carriedo, C. Gonzalo, R. Arenas et al., Factors influencing variation of fatty acid content in ovine milk, J. Dairy Sci, vol.92, pp.3791-3799, 2009.

M. De-marchi, M. Penasa, A. Cecchinato, M. Mele, P. Secchiari et al., Effectiveness of mid-infrared spectroscopy to predict fatty acid composition of Brown Swiss bovine milk, Animal, vol.5, pp.1653-1658, 2011.

O. Devos and L. Duponchel, Parallel genetic algorithm cooptimization of spectral pre-processing and wavelength selection for PLS regression, Chemom. Intell. Lab. Syst, vol.107, pp.20-58, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00593275

D. L. Donoho and I. M. Johnstone, Ideal spatial adaptation via wavelet shrinkage, Biometrika, vol.81, pp.425-455, 1994.

A. Doyon, Influence de l'alimentation sur la composition du lait de chèvre: Revue des travaux récents. Colloque sur la chèvre. L'innovation: Un outil de croissance. Centre de référence en agriculture et agroalimentaire du Québec (CRAAQ), 2005.

S. Esvan, C. Dragan, A. Varenne, J. Astruc, F. Barillet et al., PhénoFinlait, 1ers résultats: Influence de l'alimentation, de l'état physiologique et de la génétique sur la composition en acides gras des laits de vache, 2010.

F. Faucon-lahalle, M. Brochard, F. Barillet, M. Bolard, P. Brunschwig et al., PhenoFinLait (LactoScan): French national program for high scale phenotyping and genotyping to detect QTL linked with fine composition of ruminant milk, 6th International Milk Genomics Consortium Symposium, 2009.

J. A. Fernández, Merging of spectral datasets from different MIR instruments used in the routine analysis of milk, Pages 55-71 in Proc. Reference Laboratory Network Meeting, 2012.

M. Ferrand, B. Huquet, S. Barbey, F. Barillet, F. Faucon et al., Determination of fatty acid profile in cow's milk using mid-infrared spectrometry: Interest of applying a variable selection by genetic algorithms before a PLS regression, Chemom. Intell. Lab. Syst, vol.106, pp.183-189, 2010.

A. Gion, H. Larroque, M. Brochard, F. Lahalle, and D. Boichard, Genetic parameter estimation for milk fatty acids in three French dairy cattle breeds, Interbull, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01193644

H. C. Goicoechea and A. C. Olivieri, A new family of genetic algorithms for wavelength interval selection in multivariate analytical spectroscopy, J. Chemometr, vol.17, pp.338-345, 2003.

F. Grubbs, Procedures for detecting outlying observations in samples, Technometrics, vol.11, pp.1-21, 1969.

J. M. Heck, H. J. Van-valenberg, J. Dijkstra, and A. C. Van-hooijdonk, Seasonal variation in the Dutch bovine raw milk composition, J. Dairy Sci, vol.92, pp.4745-4755, 2009.

A. Höskuldsson, Variable and subset selection in PLS regression, Chemom. Intell. Lab. Syst, vol.55, pp.23-38, 2001.

C. Hurtaud and J. L. Peyraud, Effects of feeding camelina (seeds or meal) on milk fatty acid composition and butter spreadability, J. Dairy Sci, vol.90, pp.5134-5145, 2007.
URL : https://hal.archives-ouvertes.fr/hal-02663748

C. Hurtaud, J. L. Peyraud, G. Michel, D. Berthelot, and L. Delaby, Winter feeding systems and dairy cow breed have an impact on milk composition and flavor of two Protected Designation of Origin French cheeses, Journal of Dairy Science, vol.3, issue.1, pp.1327-1338, 2009.

, PredICtIOn OF FattY aCId PrOFIles In COW, eWe and GOat mIlK 19

, Standard 141C: Whole milk-Determination of milkfat, protein and lactose content-Guidance on the operation of mid-infrared instruments, IDF, 2000.

, Feeding standards for ruminants. Pages 15-22 in Ruminant Nutrition. Recommended Allowances and Feed Table, Eurotext, 1989.

. Iso-idf, International Organization for Standardization-International Dairy Federation), 2001.

. Iso-idf, ISO-IDF (International Organization for Standardization-International Dairy Federation). 2002b. Milk fat-Determination of the fatty acid composition by gas-liquid chromatography, 2002.

. Iso-idf, Milk-Definition and evaluation of the overall accuracy of alternative methods of milk analysis-Part 2: Calibration and quality control in the dairy laboratory, 2009.

J. K. Kramer, V. Fellner, M. E. Dugan, F. D. Sauer, M. M. Mossoba et al., Evaluating acid and base catalysts in the methylation of milk and rumen fatty acids with special emphasis on conjugated dienes and total trans fatty acids, Lipids, vol.32, pp.1219-1228, 1997.

H. Larroque, Y. Gallard, L. Thaunat, D. Boichard, and J. J. Colleau, A crossbreeding experiment to detect quantitative trait loci in dairy cattle, Proc. 7th World Congress on Genetics Applied to Livestock Production, 2002.

R. Leardi, Application of genetic algorithm-PLS for feature selection in spectral data sets, J. Chemometr, vol.14, pp.643-655, 2000.

R. Leardi, R. Boggia, and M. Terrile, Genetic algorithm as a strategy for feature selection, J. Chemometr, vol.6, pp.267-281, 1992.

R. Leardi and A. L. Lupiáñez-gonzález, Genetic algorithms applied to feature selection in PLS regression: How and when to use them, Chemom. Intell. Lab. Syst, vol.41, pp.195-207, 1998.

O. Leray, M. Ferrand, H. Larroque, J. M. Astruc, M. Douguet et al., Harmonisation of milk analysers for fatty acid determination by FTMIR-An essential step prior to collective data use, ICAR Meeting, 2011.

. Olivier%20leray and . Pdf,

S. Mallat, A theory for multiresolution signal decomposition: The wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell, vol.11, pp.674-693, 1989.

S. Mallat, A Wavelet Tour of Signal Processing: The Sparse Way, 2008.

M. H. Maurice-van-eijndhoven, H. Soyeurt, F. Dehareng, and M. P. Calus, Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds, Animal, vol.7, pp.348-354, 2013.

B. Mevik and R. Wehrens, The pls package: Principal component and partial least squares regression in R, J. Stat. Softw, vol.18, pp.1-24, 2007.

K. Raynal-ljutovac, G. Lagriffoul, P. Paccard, I. Guillet, and Y. Chilliard, Composition of goat and sheep milk products: An update, Small Rumin. Res, vol.79, pp.57-72, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02667871

M. J. Rutten, H. Bovenhuis, K. A. Hettinga, H. J. Van-valenberg, and J. A. Van-arendonk, Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer, J. Dairy Sci, vol.92, pp.6202-6209, 2009.

L. Sanz-ceballos, E. Morales, G. De-la-torre-adarve, J. Castro, L. P. Martínez et al., Composition of goat and cow milk produced under similar conditions and analyzed by identical methodology, J. Food Compost. Anal, vol.22, pp.322-329, 2009.

A. Schennink, W. M. Stoop, M. H. Visker, J. J. Van-der-poel, H. Bovenhuis et al., Short communication: Genome-wide scan for bovine milk-fat composition. II. Quantitative trait loci for long-chain fatty acids, J. Dairy Sci, vol.92, pp.4676-4682, 2009.

H. Soyeurt, P. Dardenne, F. Dehareng, G. Lognay, G. Veselko et al., Estimating fatty acid content in cow milk using mid-infrared spectrometry, J. Dairy Sci, vol.89, pp.3690-3695, 2006.

H. Soyeurt, F. Dehareng, N. Gengler, S. Mcparland, E. Wall et al., Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries, J. Dairy Sci, vol.94, pp.1657-1667, 2011.

C. H. Spiegelman, M. J. Mcshane, M. J. Goetz, M. Motamedi, Q. L. Yue et al., Theoretical justification of wavelength selection in PLS calibration: Development of a new algorithm, Anal. Chem, vol.70, pp.35-44, 1998.

W. M. Stoop, A. Schennink, M. H. Visker, E. Mullaart, J. A. Van-arendonk et al., Genome-wide scan for bovine milk-fat composition. I. Quantitative trait loci for short-and medium-chain fatty acids, J. Dairy Sci, vol.92, pp.4664-4675, 2009.

M. Tenenhaus, La Regression PLS: Théorie et Pratique. Technip, Lassay-les-Châteaux, 2002.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.67, pp.301-320, 2005.