Milk infrared spectra from multiple instruments improve performance of prediction models

Abstract

Milk data predicted from infrared spectra are the basis of selection programmes for dairy species, but several factors influence the robustness of prediction models, e.g., (i) variability of collected samples, (ii) number and quality of reference data, and (iii) influence traits on milk spectral fingerprint. Use of single or multiple instruments spectra from the same set of samples to develop mid infrared calibration models was investigated. Fitting statistics for milk coagulation properties were low to fair. Better results were achieved for titratable acidity and cheese yield; coefficients of determination in cross validation ranged from 0.52 to 0.77, depending on the trait analysed and training set. Merging spectral data from two instruments did not improve calibration performance but provided the best fitting statistics in external validation for most of the traits on different validation sets. Merging spectral data from different instruments is a cost-effective method to improve calibration performance and robustness.