mid-infrared; milk; principal component; heritability
Abstract :
[en] The aim of this study was to estimate the genetic parameters of the mid-infrared (MIR) milk spectrum represented by 1,060 data points per sample. The dimensionality of traits was reduced by principal components analysis. Therefore, 46 principal components describing 99.03% of the phenotypic variability were used to create 46 new traits. Variance components were estimated using canonical transformation. Heritability ranged from 0 to 0.35. Twenty-five out of 46 studied traits showed a permanent environment variance greater than genetic variance. Eight traits showed heritability greater than 0.10. Variances of original spectral traits were obtained by back transformation. Heritabilities for each spectral data points ranged from 0.003 to 0.42. In particular, 3 MIR regions showing moderate to high heritability estimates were of potential genetic interest. Heritabilities for specific wave numbers, linked with common milk traits (e.g., lipids, lactose), were similar to those estimated for these traits. This research confirms the genetic variability of the MIR milk spectrum and, therefore, the genetic variation of milk components. The objective of this study was to better understand the genetics of milk composition and, maybe in the future, to select animals to improve milk quality.
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Bibliography
Dufour E., Mazerolles G., Devaux M.F., Duboz G., Duployer M.H., Mouhous Riou N. Phase transition of triglycerides during semi-hard cheese ripening. Int. Dairy J. 2000, 10:81-93.
Ikonen T., Ahlfors K., Kempe R., Ojala M., Ruottinen O. Genetic parameters for the milk coagulation properties and prevalence of noncoagulating milk in Finnish dairy cows. J. Dairy Sci. 1999, 82:205-214.
Jelen P. Innovative uses of milk in human nutrition and health. Proceedings of the 35th Biennal Session of ICAR, Kuopio, Finland. 2007, EAAP publication 121.
Karoui R., Mouazen A.M., Dufour E., Pillonel L., Schaller E., Picque D., De Baedemaeker J., Bosset J.-O. A comparison and joint use of NIR and MIR spectroscopic methods for the determination of some parameters in European Emmental cheese. Eur. Food Res. Technol. 2006, 223:44-50.
Mayeres P., Stoll J., Bormann J., Reents R., Gengler N. Prediction of daily milk, fat, and protein production by a random regression test-day model. J. Dairy Sci. 2004, 87:1925-1933.
Miglior F., Sewalem A., Jamrozik J., Bohmanova J., Lefebvre D.M., Moore R.K. Genetic analysis of milk urea nitrogen and lactose and their relationships with other production traits in Canadian Holstein cattle. J. Dairy Sci. 2007, 90:2468-2479.
Misztal, I. 1994. MTCAFS (MTC)-Multitrait REML Estimation of Variance Components Program by Canonical Transformation, With Support for Multiple Random Effects.edu/∼ignacy/numpub/mtc/mtcman Accessed on Sept. 23, 2008. http://nce.ads.uga/.
Misztal I. Reliable computing in estimation of variance components. J. Anim. Breed. Genet. 2008, 125:363-370.
Misztal I., Weigel K., Lawlor T.J. Approximation of estimates of (co)variance components with multiple-trait restricted maximum likelihood by multiple diagonalization for more than one random effect. J. Dairy Sci. 1995, 78:1862-1872.
Palm, R. 1998. Notes de statistique et d'informatique: L'analyse en composantes principales: Principes et applications. Notes techniques. Faculté Universitaire des Sciences Agronomiques de Gembloux. Accessed on Jan. 2, 2007. http://www.fsagx.ac.be/si/E_index.htm.
Picque D., Lefier D., Grappin R., Corrieu G. Monitoring fermentation by infrared spectrometry: Alcoholic and lactic fermentations. Anal. Chim. Acta 1993, 279:67-72.
SAS/STAT User's Guide: Version 6 1994, SAS Institute, SAS institute Inc., Cary, NC.
Searle S.R., Casella G., McCullock C.E. Variance Components 1992, John Wiley & Sons Inc., Hoboken, NJ.
Sivakesava S., Irudayaraj J. Rapid determination of tetracycline in milk by FT-MIR and FT-NIR spectroscopy. J. Dairy Sci. 2002, 85:487-493.
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.
Welper R.D., Freeman A.E. Genetic parameters for yields traits of Holsteins, including lactose and somatic cell score. J. Dairy Sci. 1992, 75:1342-1348.
William P., Norris K. Near-Infrared Technology in the Agricultural and Food Industries 2001, American Association of Cereal Chemists, St. Paul, MN.
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