Cheese yield and nutrients recovery in the curd predicted by Fourier- transform from individual milk

The objectives of this study were to explore the use of Fourier-transform infrared (FTIR) spectroscopy on in-dividual sheep milk samples for predicting cheese -mak-ing traits, and to test the effect of the farm variability on their prediction accuracy. For each of 121 ewes from 4 farms, a laboratory model cheese was produced, and 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water) and 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in the curd were measured. Calibration equations were developed using a Bayesian approach with 2 different scenarios: (1) a random cross-validation (80% calibration; 20% valida-tion set), and (2) a leave-one-out validation (3 farms used as calibration, and the remaining one as validation set) to assess the accuracy of prediction of samples from external farms, not included in calibration set. The best performance was obtained for predicting the yield and recovery of total solids, justifying for the practical ap-plication of the method at sheep population and dairy industry levels. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of fresh curd and recovery of energy. Insufficient accuracies were found for the recov-ery of protein and fat, highlighting the complex nature of the relationships among the milk nutrients and their recovery in the curd. The leave-one-out validation pro-cedure, as expected, showed lower prediction accuracies, as a result of the characteristics of the farming systems, which were different between calibration and validation sets. In this regard, the inclusion of information related to the farm could help to improve the prediction accuracy of these traits. Overall, a large contribution to the prediction of the cheese-making traits came from the areas known as water and fingerprint regions. These findings suggest that, according to the traits studied, the inclusion of water regions for the development of the prediction equation models is fundamental to maintain a high prediction accuracy. However, further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits, to offer reliable tools applicable along the dairy ovine chain.

Automated summary

This study aimed to explore the use of FTIR spectroscopy on individual sheep milk samples for predicting cheese-making traits and test the effect of farm variability on prediction accuracy. The best performance was obtained for predicting total solids yield and recovery. Insufficient accuracies were found for protein and fat recovery. Further studies are necessary to better understand specific absorbance peaks' role in predicting cheese-making traits. The inclusion of farm-related information could improve prediction accuracy.