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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 R(2) were reached (from 0.18 for α(S1)-casein to 0.56 for β-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 α-lactalbumin (R(2)=0.20), the selection differential amounted to 0.18 g/100g and for casein index (R(2)=0.50) to 1.24 g/100g. 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 (β-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., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

β-Lactoglobulin (β-LG) genotypes are associated with differences in bovine milk protein composition. Therefore, β-LG genotypes are of direct relevance for the dairy industry. In this study, we predicted β-lactoglobulin genotypes based on routinely recorded milk Fourier transform infrared spectra using 500 calibration samples. The results show that 76% of the cows carrying the β-LG AA genotype, 80% of the cows carrying the β-LG AB genotype, and 66% of the cows carrying the β-LG BB genotype were predicted correctly. Furthermore, the prediction of β-LG genotypes based on Fourier transform infrared spectra showed a repeatability of 0.85. We discuss how the combined use of predicted β-LG genotypes, pedigree information, and β-LG genotypes derived using other methods could lead to further improvement in the percentage of correctly predicted β-LG genotypes. The presented methodology is easy and inexpensive and could ultimately provide β-LG genotypes at the individual cow level., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Fourier transform infrared spectroscopy is a suitable method to determine bovine milk fat composition. However, the determination of fat composition by gas chromatography, required for calibration of the infrared prediction model, is expensive and labor intensive. It has recently been shown that the number of calibration samples is strongly related to the model's validation r(2) (i.e., accuracy of prediction). However, the effect of the number of calibration samples used, and therefore validation r(2), on the estimated genetic parameters of data predicted using the model needs to be established. To this end, 235 calibration data subsets of different sizes were sampled: n=100, n=250, n=500, and n=1,000 calibration samples. Subsequently, these data subsets were used to calibrate fat composition prediction models for 2 specific fatty acids: C16:0 and C18u (where u=unsaturated). Next, genetic parameters were estimated on predicted fat composition data for these fatty acids. Strong relationships between the number of calibration samples and validation r(2), as well as strong genetic correlations were found. However, the use of n=100 calibration samples resulted in a broad range of validation r(2) values and genetic correlations. Subsequent increases of the number of calibration samples resulted in narrowing patterns for validation r(2) as well as genetic correlations. The use of n=1,000 calibration samples resulted in estimated genetic correlations varying within a range of 0.10 around the average, which seems acceptable. Genetic analyses for the human health-related fatty acids C14:0, C16:0, and C18u, and the ratio of saturated fatty acids to unsaturated fatty acids showed that replacing observations on fat composition determined by gas chromatography by predictions based on infrared spectra reduced the potential genetic gain to 98, 86, 96, and 99% for the 4 fatty acid traits, respectively, in dairy breeding schemes where progeny testing is practiced. We conclude that a relatively large number of calibration samples is required to be able to obtain genetic correlations that lie within a limited range. Considering that the routine recording of infrared spectra is relatively cheap and straightforward, we concluded that this methodology provides an excellent means for the dairy industry to genetically alter milk fat composition., (Copyright © 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

It has recently been shown that Fourier transform infrared spectroscopy has potential for the prediction of detailed milk fat composition, even based on a limited number of observations. Therefore, there seems to be an opportunity for improvement by means of using more observations. The objective of this study was to verify whether the use of more data would add to the accuracy of predicting milk fat composition. In addition, the effect of season on modeling was quantified because large differences in milk fat composition between winter and summer samples exist. We concluded that the use of 3,622 observations does increase predictability of milk fat composition based on infrared spectroscopy. However, for fatty acids with low concentrations, the use of many observations does not increase predictability to a level at which application of the model becomes obvious. Furthermore, the effect of season on validation r-square was limited but was occasionally large on prediction bias. For fatty acids that show large differences in level and standard deviation between winter and summer, a representative sample that includes observations collected in various seasons is critical for unbiased prediction. This research shows that all major fatty acids, combined groups of fatty acids, and the ratio of saturated to unsaturated fatty acids can be predicted accurately.