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.

No abstract

No 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 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.

No abstract