Genetic and phenotypic investigation of milk composition and technological properties in dairy sheep

In the last decade, the technological and the nutritional qualities of milk have been extensively investigated because of the worldwide increasing amount of cheese produced and the consumer interest in dairy product features related to human health. In the present thesis the genetic and phenotypic variability of milk composition and technological properties of Sarda dairy ewes were investigated. The use of multivariate factor analysis on milk composition, coagulation properties and cheese yield has allowed elucidating the inner structure of the correlation patterns existing among variables that define milk quality. Four new extracted variables (latent factors) from the original traits, explained about 76% of total variance. Heritability estimates for the factors suggest their use as breeding goals. The study of lactation curve for milk traits during lactation has showed an improvement of cheese yield in late lactation, even though this result could be due to a reduction of curd draining capacity. At the end of lactation were observed the poorest cheese-making aptitude, which could be explained by physiological changes in ewes and relevant modification of milk composition. Stage of lactation, parity and month of lambing were the main factors affecting milk quality traits. The possibility to improve nutritional quality with selection programmes, in particular milk fatty acids profile, is supported by the existence of genetic variability for some fatty acids, as evidenced by our results

Use of multivariate factor analysis to characterize the fatty acid profile of buffalo milk

The suitability of multivariate factor analysis (MFA) to extract a small number of latent variables able to explain the correlation pattern among fatty acids (FA) in buffalo milk was evaluated. FA profile of milk samples from 214 Italian water buffaloes was analysed by gas chromatography. MFA, performed on the correlation matrix of 52 FA, was able to extract 10 latent factors with specific biological meaning related to a common metabolic origin for FA associated with the same factor. Scores of the factors were treated as new quantitative phenotypes to evaluate the effect of age, month of calving and lactation stage. MFA approach was effective in describing the FA profile of buffalo milk by using a low number of new latent variables that clustered FA having similar metabolic origin and function. The new variables were also useful to test the effect of environmental and individual animal factors on milk FA composition

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

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