Interest in methods that routinely and accurately measure and predict animal characteristics is growing in importance, both for quality characterization of livestock products and for genetic purposes. Mid-infrared spectroscopy (MIRS) is a rapid and cost-effective tool for recording phenotypes at the population level. Mid-infrared spectroscopy is based on crossing matter by electromagnetic radiation and on the subsequent measure of energy absorption, and it is commonly used to determine traditional milk quality traits in official milk laboratories. The aim of this review was to focus on the use of MIRS to predict new milk phenotypes of economic relevance such as fatty acid and protein composition, coagulation properties, acidity, mineral composition, ketone bodies, body energy status, and methane emissions. Analysis of the literature demonstrated the feasibility of MIRS to predict these traits, with different accuracies and with margins of improvement of prediction equations. In general, the reviewed papers underlined the influence of data variability, reference method, and unit of measurement on the development of robust models. A crucial point in favor of the application of MIRS is to stimulate the exchange of data among countries to develop equations that take into account the biological variability of the studied traits under different conditions. Due to the large variability of reference methods used for MIRS calibration, it is essential to standardize the methods used within and across countries.

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Recently, a general deterioration of milk coagulation properties (MCP) has been observed in Italy; thus, the prediction of noncoagulating (NC) milk, defined as milk not forming a curd within 30min from rennet addition, is of immediate interest in the Italian cheese industry. The present study investigated the ability of mid-infrared (MIR) spectroscopy to predict NC milk using individual and bulk samples from Holstein cows. Samples were selected according to MIR analysis to cover the range of coagulation time between 5 and 60min. Milks were then analyzed for MCP through the reference instrument (Formagraph) over an extended testing period of 60min to identify coagulating and NC samples. Measured traits were rennet coagulation time, curd-firming time, and curd firmness 30 and 60min after rennet addition. Results showed no specific spectral information distinguishing NC from coagulating samples. The most accurate prediction model was developed for rennet coagulation time followed by curd-firming time and curd firmness 30min after rennet addition, whereas curd firmness 60min after enzyme addition could not be accurately predicted. Based on these findings, MIR spectroscopy might be proposed in payment systems to reward or penalize milk according to MCP. Moreover, the ability of MIR spectroscopy to predict the MCP of samples that form a curd beyond 30min from enzyme addition may be of interest for genetic improvement of coagulation traits in dairy breeds, because until now most studies have excluded NC information from genetic analysis, leading to possible biases in the estimation of genetic parameters and in the prediction of sire's merit for MCP.

The aim of the present study was to estimate genetic parameters for calcium (Ca), phosphorus (P) and titratable acidity (TA) in bovine milk predicted by mid-IR spectroscopy (MIRS). Data consisted of 2458 Italian Holstein − Friesian cows sampled once in 220 farms. Information per sample on protein and fat percentage, pH and somatic cell count, as well as test-day milk yield, was also available. (Co)variance components were estimated using univariate and bivariate animal linear mixed models. Fixed effects considered in the analyses were herd of sampling, parity, lactation stage and a two-way interaction between parity and lactation stage; an additive genetic and residual term were included in the models as random effects. Estimates of heritability for Ca, P and TA were 0.10, 0.12 and 0.26, respectively. Positive moderate to strong phenotypic correlations (0.33 to 0.82) existed between Ca, P and TA, whereas phenotypic weak to moderate correlations (0.00 to 0.45) existed between these traits with both milk quality and yield. Moderate to strong genetic correlations (0.28 to 0.92) existed between Ca, P and TA, and between these predicted traits with both fat and protein percentage (0.35 to 0.91). The existence of heritable genetic variation for Ca, P and TA, coupled with the potential to predict these components for routine cow milk testing, imply that genetic gain in these traits is indeed possible.

The objective of the present study was to investigate the effect of environmental factors, milk casein content and titratable acidity on milk coagulation properties (MCP) of samples routinely collected in the Trento province (northeast Italy) under field conditions. Rennet coagulation time (RCT, min), curd-firming time (k20, min) and curd firmness (a30, mm) were determined by Formagraph on 14 971 samples from 635 herds associated to 17 dairy factories. Besides MCP, fat, protein, and casein percentages, titratable acidity (TA), and somatic cell and bacterial counts were available. A standardised index of milk aptitude to coagulate (IAC) was derived using information of RCT and a30. An analysis of variance was conducted on MCP and IAC using a fixed effects linear model. Approximately 3% of milk samples did not form a curd within the testing time (30 min) and k20 was missing for 26% of milks. The percentage of samples without information on k20 largely differed among dairy factories (1·7-20·9%). Significant differences were estimated between the best and the worst dairy factory for RCT (-2 min), k20 (-1·2 min), a30 (+3·4 mm) and IAC (+2·6 points). Milk casein content and TA were important factors in explaining the variation of MCP and IAC, supporting the central role of these two traits on technological properties. The Trento province is heterogeneous in terms of dairy systems and this could explain the differences among dairy factories.