Individual Cheese Yield Research Articles

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

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.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.

Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle

It is becoming common to complement genome-wide association studies (GWAS) with gene-set enrichment analysis to deepen the understanding of the biological pathways affecting quantitative traits. Our objective was to conduct a gene ontology and pathway-based analysis to identify possible biological mechanisms involved in the regulation of bovine milk technological traits: coagulation properties, curd firmness modeling, individual cheese yield (CY), and milk nutrient recovery into the curd (REC) or whey loss traits. Results from 2 previous GWAS studies using 1,011 cows genotyped for 50k single nucleotide polymorphisms were used. Overall, the phenotypes analyzed consisted of 3 traditional milk coagulation property measures [RCT: rennet coagulation time defined as the time (min) from addition of enzyme to the beginning of coagulation; k20: the interval (min) from RCT to the time at which a curd firmness of 20 mm is attained; a30: a measure of the extent of curd firmness (mm) 30 min after coagulant addition], 6 curd firmness modeling traits [RCTeq: RCT estimated through the CF equation (min); CFP: potential asymptotic curd firmness (mm); kCF: curd-firming rate constant (% × min-1); kSR: syneresis rate constant (% × min-1); CFmax: maximum curd firmness (mm); and tmax: time to CFmax (min)], 3 individual CY-related traits expressing the weight of fresh curd (%CYCURD), curd solids (%CYSOLIDS), and curd moisture (%CYWATER) as a percentage of weight of milk processed and 4 milk nutrient and energy recoveries in the curd (RECFAT, RECPROTEIN, RECSOLIDS, and RECENERGY calculated as the % ratio between the nutrient in curd and the corresponding nutrient in processed milk), milk pH, and protein percentage. Each trait was analyzed separately. In total, 13,269 annotated genes were used in the analysis. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were queried for enrichment analyses. Overall, 21 Gene Ontology and 17 Kyoto Encyclopedia of Genes and Genomes categories were significantly associated (false discovery rate at 5%) with 7 traits (RCT, RCTeq, kCF, %CYSOLIDS, RECFAT, RECSOLIDS, and RECENERGY), with some being in common between traits. The significantly enriched categories included calcium signaling pathway, salivary secretion, metabolic pathways, carbohydrate digestion and absorption, the tight junction and the phosphatidylinositol pathways, as well as pathways related to the bovine mammary gland health status, and contained a total of 150 genes spanning all chromosomes but 9, 20, and 27. This study provided new insights into the regulation of bovine milk coagulation and cheese ability that were not captured by the GWAS.

Derivation of multivariate indices of milk composition, coagulation properties, and individual cheese yield in dairy sheep

Milk composition and its technological properties are traits of interest for the dairy sheep industry because almost all milk produced is processed into cheese. However, several variables define milk technological properties and a complex correlation pattern exists among them. In the present work, we measured milk composition, coagulation properties, and individual cheese yields in a sample of 991 Sarda breed ewes in 47 flocks. The work aimed to study the correlation pattern among measured variables and to obtain new synthetic indicators of milk composition and cheese-making properties. Multivariate factor analysis was carried out on individual measures of milk coagulation parameters; cheese yield; fat, protein, and lactose percentages; somatic cell score; casein percentage; NaCl content; pH; and freezing point. Four factors that were able to explain about 76% of the original variance were extracted. They were clearly interpretable: the first was associated with composition and cheese yield, the second with udder health status, the third with coagulation, and the fourth with curd characteristics. Factor scores were then analyzed by using a mixed linear model that included the fixed effect of parity, lambing month, and lactation stage, and the random effect of flock-test date. The patterns of factor scores along lactation stages were coherent with their technical meaning. A relevant effect of flock-test date was detected, especially on the 2 factors related to milk coagulation properties. Results of the present study suggest the existence of a simpler latent structure that regulates relationships between variables defining milk composition and coagulation properties in sheep. Heritability estimates for the 4 extracted factors were from low to moderate, suggesting potential use of these new variables as breeding goals.

Genetic and environmental relationships of different measures of individual cheese yield and curd nutrients recovery with coagulation properties of bovine milk

The aim of this study was to elucidate the relationships between various cheesemaking-related traits, namely the well-known traditional milk coagulation properties (MCP), the new curd firming and syneresis traits, the cheese yield, and the curd nutrient recoveries or whey losses (all measured at the individual level). Data were obtained from 1,167 Brown Swiss cows reared in 85 herds. A 2-L milk sample was collected once from each animal and assessed for 10 phenotypes related to changes in curd firmness (CF) over time, plus 7 cheesemaking traits. The CF-related traits included 4 traditional single-point lactodynamographic properties [rennet coagulation time (RCT, min); time to a CF of 20mm, min; and the CF 30 and 45min after rennet addition (a30 and a45, respectively)], 4 parameters used to model the 360 CF data recorded over time for each milk sample [the potential asymptotic CF at infinite time (CFP, mm); the CF instant rate constant, % × min−1; the syneresis instant rate constant, % × min−1; and the RCT obtained from modeling individual samples], and 2 traits calculated from individual equations [the maximum CF(CFmax, mm); and the time at CFmax, min]. The cheesemaking traits included 3 cheese yield traits (weights of the fresh curd, curd solids and curd moisture as percent of the weights of the processed milk) and 4milk nutrient recoveries in the curd (calculated as the percent ratios between a given nutrient in the curd versus that in the processed milk). Bayesian methodology-based multivariate analyses were used to estimate the phenotypic, additive genetic, herd/date, and residual relationships between the aforementioned traits, whereas statistical inferences were based on the marginal posterior distributions of the parameters of concern. The a45, CFP, and CFmax traits were genetically associated with all of the percent cheese yield traits (the additive genetic correlations varied from 0.752 to 0.855 for a45; 0.496 to 0.583 for CFP; and 0.750 to 0.801 for CFmax) and the nutrient recovery traits (additive genetic correlations varied from 0.296 to 0.901 for a45; 0.428 to 0.697 for CFP; and 0.412 to 0.941 for CFmax). Moreover, the nutrient recoveries for fat, solids, and energy exhibited large additive genetic correlations with the other coagulation and curd firming traits. In particular, recovery of protein and fat were found to be powerful instruments for understanding the relationships between milk technological properties and cheese quantity or quality. We observed only weak genetic relationships with the milk quality and MCP traits, suggesting that the highly heritable trait of protein recovery should perhaps be included as a genetic index when seeking to improve cheesemaking efficiency at the population level. In contrast, we found that fat recovery exhibited moderate genetic variation and could be improved through the CF over time traits, especially using those recorded during the late phase of the curd firming process. Moreover, our results demonstrated that the traditional MCP have limited relevance for predicting individual cheese yield. Therefore, their use for this purpose in the dairy industry and breeding programs seems questionable.

Milk Composition and Cheesemaking ability in Ewes: Influence of Major Milk Components and pH on Individual Cheese Yield at a Laboratory Scale in Ewes

Milk Composition and Cheesemaking ability in Ewes: Influence of Major Milk Components and pH on Individual Cheese Yield at a Laboratory Scale in Ewes

A laboratory micro-manufacturing method for assessing individual cheese yield

This study aimed to propose a micro-manufacturing method for assessing individual cheese yield (CY) from dairy milk and to provide first evaluations of the method. Two water baths (WB) provided with 8 stainless containers (SC) each were used. The cheese making procedure involved coagulation of 500 ml of milk per SC previously inoculated with starter, cutting of the curd, separation of whey through drainage and pressure and weighing of the curd residue for computing CY. Spray-dried milk powder and bulk milk were used to validate the procedure. For each type of milk, 5 cheese making sessions (CMS) were performed during 5 consecutive days by the same operator; for each trial 80 records (8 replicates x 2WB x 5CMS) were available. Sources of variation of CY were investigated separately for type of milk, and variance components were estimated using REML procedure for computing instrumental repeatability and reproducibility. Cheese yield averaged 11% and 13.4% for standard milk powder and bulk milk, respectively. Cheese making session significantly influenced CY, along with WB in the case of milk powder, while SC did not influence CY. Reproducible and repeatable measures of CY were obtained, indicating the method is suitable for assessing individual CY.