Mid-infrared prediction of cheese yield from milk and its genetic variability in first-parity cows

Economically, cheese yield is very important. It would be interesting to predict fresh Individual Laboratory Cheese Yield ILCYf) without the need to estimate milk components and to use empirical or theoretical formulae. In order to study the genetic variability of this trait on a large scale, mid-infrared (MIR) chemometric methods were used to predict ILCYf. A total of 258 milk samples collected in the Walloon Region of Belgium from individual cows (Holstein, Red-Holstein, Dual Purpose Belgian Blue, and Montbeliarde) were analyzed using a MIR spectrometer and ILCYf was determined for each sample. An equation to predict ILCYf from milk MIR spectra was developed using PLS regression after a first derivative pre-treatment applied to the spectra to correct the baseline drift. During the calibration process, 22 outliers were detected and removed from the calibration set. The ILCYf mean of the final calibration set was 26.8 g coagulum/100 g milk (SD-6.5). The coefficient of determination (R2 a 0.83 for the calibration with a standard error (SE) of 2.6. A cross-validation (ev) was performed Rv 0.81 with SEcv-2.8). This equation was then applied on the spectral database generated during the Walloon routine milk recording. The variances components were estimated by REML using single-trait random regression animal test-day model. The dataset used includes 51,537 predicted records from 7,870 Holstein first-parity cows, the ILCYf mean was 24.2 (SD-4.5) and ILCYf ranged from 13.6 to 40.9. Estimated daily heritabilities ranged from 0.27 at 5th day in milk to 0.55 at 231h day in milk indicating potential of selection. Further research will study phenotypic and genetic correlations between ILCYf and milk production traits.