Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression

• Published in: Animals (Basel) 2019-09, Vol.9 (9), p.663
• Main Authors: Cellesi, Massimo, Correddu, Fabio, Manca, Maria Grazia, Serdino, Jessica, Gaspa, Giustino, Dimauro, Corrado, Macciotta, Nicolò Pietro Paolo
• Format: Article
• Language: English
• Subjects: Animal husbandry; Animal lactation; Animals; Breeding; breeding programs; breeds; Cheese; cheeses; clotting properties; Coagulation; Curd; Dairy cattle; Dairy industry; Dairy products; dairy sheep; data collection; Data smoothing; Datasets; ewe milk; ewes; firmness; Fourier transform infrared spectroscopy; individual cheese yield; Infrared analysis; Infrared spectra; Laboratories; least squares; Least squares method; Logistics; mid-infrared spectroscopy; Milk; model validation; Parameter estimation; partial least square regression; prediction; Prediction models; Proteins; rennet; Sheep; Statistical methods
• ISSN: 2076-2615; 2076-2615
• DOI: 10.3390/ani9090663

The objectives of this study were (i) the prediction of sheep milk coagulation properties (MCP) and individual laboratory cheese yield (ILCY) from mid-infrared (MIR) spectra by using partial least squares (PLS) regression, and (ii) the comparison of different data pre-treatments on prediction accuracy. Individual milk samples of 970 Sarda breed ewes were analyzed for rennet coagulation time (RCT), curd-firming time (k20), and curd firmness (a30) using the Formagraph instrument; ILCY was measured by micro-manufacturing assays. An Furier-transform Infrared (FTIR) milk-analyzer was used for the estimation of the milk gross composition and the recording of MIR spectrum. The dataset (n = 859, after the exclusion of 111 noncoagulating samples) was divided into two sub-datasets: the data of 700 ewes were used to estimate prediction model parameters, and the data of 159 ewes were used to validate the model. Four prediction scenarios were compared in the validation, differing for the use of whole or reduced MIR spectrum and the use of raw or corrected data (locally weighted scatterplot smoothing). PLS prediction statistics were moderate. The use of the reduced MIR spectrum yielded the best results for the considered traits, whereas the data correction improved the prediction ability only when the whole MIR spectrum was used. In conclusion, PLS achieves good accuracy of prediction, in particular for ILCY and RCT, and it may enable increasing the number of traits to be included in breeding programs for dairy sheep without additional costs and logistics.