Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 418644
Title Predicting bovine milk protein composition based on Fourier transform infrared spectra
Author(s) Rutten, M.J.M.; Bovenhuis, H.; Heck, J.M.L.; Arendonk, J.A.M. van
Source Journal of Dairy Science 94 (2011)11. - ISSN 0022-0302 - p. 5683 - 5690.
Department(s) Animal Breeding and Genomics
Product Design and Quality Management Group
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) dutch holstein-friesians - genetic-parameters - beta-lactoglobulin - coagulation properties - production traits - fat composition - dairy-cows - casein - genotypes - spectroscopy
Abstract Phenotypic information on individual protein composition of cows is important for many aspects of dairy processing with cheese production as the center of gravity. However, measuring individual protein composition is expensive and time consuming. In this study, we investigated whether protein composition can be predicted based on inexpensive and routinely measured milk Fourier transform infrared (FTIR) spectra. Based on 900 calibration and 900 validation samples that had both capillary zone electrophoresis (CZE)-determined protein composition and FTIR spectra available, low to moderate validation R2 were reached (from 0.18 for aS1-casein to 0.56 for ß-lactoglobulin). The potential usefulness of this model on the phenotypic level was investigated by means of achieved selection differentials for 25% of the best animals. For a-lactalbumin (R2 = 0.20), the selection differential amounted to 0.18 g/100 g and for casein index (R2 = 0.50) to 1.24 g/100 g. We concluded that predictions of protein composition were not accurate enough to enable selection of individual animals. However, for specific purposes when, for example, groups of animals that meet a certain threshold are to be selected, the presented model could be useful in practice on the phenotypic level. The potential usefulness of this model on the genetic level was investigated by means of genetic correlations between CZE-determined and FTIR-predicted protein composition traits. The genetic correlations ranged from 0.62 (ß-casein) to 0.97 (whey). Thus, predictions of protein composition, when used as input to estimate breeding values, provide an excellent means for genetic improvement of protein composition. In addition, estimated repeatabilities based on 3 repeated observations of predicted protein composition showed that a considerable amount of prediction error can be removed using repeated observations.
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