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

Abstract

Simple SummaryConsidered that all sheep milk in Italy is destined for cheese processing, traits describing rennet coagulation aptitude should be among the most important selection goals for dairy breeds. To reduce the costs and logistics related to the large-scale recording of these traits, mid-infrared (MIR) spectroscopy could be conveniently used to generate reliable predictions without any additional cost. The aims of this research were to predict the milk coagulation properties (MCP) and individual cheese yield (ILCY) in sheep by MIR spectrometry using partial least squares regression (PLS), and to compare different data pre-treatment procedures. The prediction results observed in the present study, although moderate, suggest the possibility of adding novel phenotypes (e.g., MCP and ILCY) in breeding schemes for dairy sheep breeds. Mid-infrared spectroscopy coupled with PLS regression could allow the prediction of phenotypes at the population level without additional costs.AbstractThe 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.

Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds

“…Individual cheese yield based on micro-cheese manufacturing (ILCY) was first described by Othmane et al (2002a) as a useful predictor of actual cheese yield. Individual cheese yield can also be predicted by spectroscopy, as Othmane (2000) and Cellesi et al (2019) showed. However, the laboratory simulation process of individual cheese manufacturing followed in this study is not completely comparable to that of the dairy industry.…”

“…As other authors have suggested, micro-cheese factoring can determine an overestimation of the actual cheese yield due to the limited amount of milk used (Puledda et al, 2017;Othmane et al, 2002b;Jaramillo et al, 2008). In any case, ILCY is the most commonly used parameter for the calculation of the individual laboratory cheese yield, and many different studies have used it as a good proxy trait to assess the cheese-making ability of dairy species (Othmane et al, 2002b,c;Cellesi et al, 2019). Because genetic selection relies on individual phenotype values, the individual proxy phenotypes here analyzed for MCP and cheese yield traits may be of great value for those breeding programs of dairy species that define the improvement of milk technological properties selection objectives.…”

Phenotypic and genetic characterization of the occurrence of noncoagulating milk in dairy sheep

“…An interesting tool to investigate reasons for occurrence of NC milks is mid-infrared (MIR) analysis; MIR spectroscopy is routinely used for predicting milk composition traits in dairy recording programs. Milk MIR spectra have also been exploited to predict other phenotypes for which the standard analysis procedures are expensive and time consuming; for example, milk fatty acid (Soyeurt et al, 2011) and mineral (Visentin et al, 2018) composition, cheese-making properties (De Marchi et al, 2009;Cellesi et al, 2019), body energy status (McParland et al, 2011), pregnancy status (Brand et al, 2021), and feed composition and intake (Klaffenböck et al, 2017;Wallén et al, 2018). The use of milk spectra to predict the milk fatty acid profile allowed the use of these phenotypes for genomic predictions in Sarda dairy sheep (Cesarani et al, 2019).…”

Low-density SNP panel for efficient imputation and genomic selection of milk production and technological traits in dairy sheep

“…Third, we selected ILCY, which provides information about cheese yield performance. The ILCY trait has previously been detailed as a useful predictor of the actual cheese yield (Othmane et al, 2002b) and declared by different studies as a good proxy trait for assessing the cheese-making ability of dairy species (Othmane et al, 2002a;Cellesi et al, 2019). Both the logK 20 and ILCY traits have been previously suggested as potential candidate traits to be considered for future inclusion in the selection index of the Assaf sheep breed to improve the cheese-making ability of this breed, and they were used together with PP due to the high reported genetic correlation of PP with ILCY (Sánchez-Mayor et al, 2019).…”