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.

Milk coagulation properties (MCP) are an important aspect in assessing cheese-making ability. Several studies showed that favorable conditions of milk reactivity with rennet, curd formation rate, and curd strength, as well as curd syneresis, have a positive effect on the entire cheese-making process and subsequently on the ripening of cheese. Moreover, MCP were found to be heritable, but little scientific literature is available about their genetic aspects. The aims of this study were to estimate heritability of MCP and genetic correlations among MCP and milk production and quality traits. A total of 1,071 Italian Holstein cows (progeny of 54 sires) reared in 34 herds in Northern Italy were sampled from January to July 2004. Individual milk samples were collected during the morning milking and analyzed for coagulation time (RCT), curd firmness (a30), pH, titratable acidity, fat, protein, and casein contents, and somatic cell count. About 10% of individual milk samples did not coagulate in 31 min, so they were removed from the analyses. Estimates of heritability for RCT and a30 were 0.25 +/- 0.04 and 0.15 +/- 0.03, respectively. Estimates of genetic correlations between MCP traits and milk production traits were negligible except for a30 with protein and casein contents (0.44 +/- 0.10 and 0.53 +/- 0.09, respectively). Estimates of genetic correlations between MCP traits and somatic cell score were strong and favorable, as well as those between MCP and pH and titratable acidity. Selecting for high casein content, milk acidity, and low somatic cell count might be an indirect way to improve MCP without reducing milk yield and quality traits.

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This study investigated the potential application of mid-infrared spectroscopy (MIR 4,000-900 cm(-1)) for the determination of milk coagulation properties (MCP), titratable acidity (TA), and pH in Brown Swiss milk samples (n = 1,064). Because MCP directly influence the efficiency of the cheese-making process, there is strong industrial interest in developing a rapid method for their assessment. Currently, the determination of MCP involves time-consuming laboratory-based measurements, and it is not feasible to carry out these measurements on the large numbers of milk samples associated with milk recording programs. Mid-infrared spectroscopy is an objective and nondestructive technique providing rapid real-time analysis of food compositional and quality parameters. Analysis of milk rennet coagulation time (RCT, min), curd firmness (a(30), mm), TA (SH degrees/50 mL; SH degrees = Soxhlet-Henkel degree), and pH was carried out, and MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Models were developed by partial least squares regression using untreated and pretreated spectra. The MCP, TA, and pH prediction models were improved by using the combined spectral ranges of 1,600 to 900 cm(-1), 3,040 to 1,700 cm(-1), and 4,000 to 3,470 cm(-1). The root mean square errors of cross-validation for the developed models were 2.36 min (RCT, range 24.9 min), 6.86 mm (a(30), range 58 mm), 0.25 SH degrees/50 mL (TA, range 3.58 SH degrees/50 mL), and 0.07 (pH, range 1.15). The most successfully predicted attributes were TA, RCT, and pH. The model for the prediction of TA provided approximate prediction (R(2) = 0.66), whereas the predictive models developed for RCT and pH could discriminate between high and low values (R(2) = 0.59 to 0.62). It was concluded that, although the models require further development to improve their accuracy before their application in industry, MIR spectroscopy has potential application for the assessment of RCT, TA, and pH during routine milk analysis in the dairy industry. The implementation of such models could be a means of improving MCP through phenotypic-based selection programs and to amend milk payment systems to incorporate MCP into their payment criteria.