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.

Individual milk samples from Holstein Friesian cows were collected and analysed by inductively coupled plasma optical emission spectrometry (ICP-OES) and titration for the determination of calcium (Ca), phosphorus (P) and titratable acidity (TA) contents, respectively. Prediction models were obtained using partial least squares (PLS) regression analyses using two statistical packages. The average Ca, P and TA were 1156 mg kg(-1), 934 mg kg(-1) and 3.42 degrees SH 50 mL(-1), respectively. Pearson's correlations between Ca and P and other milk traits were significant (P < 0.05) and ranged from 0.16 to 0.53 for chemical composition traits and from 0.17 to -0.35 for milk coagulation properties (MCP). Results from the two statistical packages were comparable. Prediction models using MIR spectroscopy were satisfactory for Ca, P and TA, with coefficients of correlation of cross-validation greater than 0.73. Moreover, the study highlighted favourable relationships of these traits with milk coagulation properties. (C) 2014 Elsevier Ltd. All rights reserved.

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 fundamental in cheese production, particularly in countries where a large amount of milk is destined to cheese industry. Several studies have demonstrated the role of MCP on cheese yield and quality. Lasting years, a general worsening of MCP at the herd and animal level has been detected. The coagulation of milk is influenced by several factors such as type and quantity of clotting enzyme, acidity and calcium content of milk, and protein content and composition. Milk coagulation properties are currently determined using several instruments, the most common being Reomether, Coagulometer, Formagraph, and Optigraph, which measure rennet coagulation time (RCT, min) and curd firmness after rennet addition (a30, mm). Nevertheless these instruments have strong limitations for the use at population level, mainly because they are time-consuming, expensive and require skilled personnel. The Fourier Transform MidInfrared Spectroscopy (FTMIR) allows for a reduction of costs needed for the analysis, high throughput, and possibility of large-scale application, i.e., the implementation in milk recording programs. In 2008 the feasibility to predict MCP using FTMIR was investigated in dairy herds located in north-east Italy and the results were encouraging; correlation coefficients for the prediction models of technological properties were comparable to those used for other novel traits (e.g., protein fractions and fatty acids). The northeast of Italy is characterized by a strong synergy among the dairy chain stakeholders (farms, dairy cooperatives, milk quality labs, animal breeding companies and research institutions). Since 2009 several regional projects have been financed and coordinated by the University of Padova (Italy) with many stakeholders of the Veneto region dairy chain, achieving a consistent improvement in efficiency of the dairy sector. The projects aimed at studying the technological characteristics of milk through an innovative approach (from cow’s milk to cheese), and it was developed through the implementation of MCP calibration models to a MilkoScan which routinely analysed individual and bulk milk samples from all the associated farms and dairies of the region. Data of individual milk samples (about 200,000 records), mainly from Holstein-Friesian cows, and herd bulk milk samples (about 15,000 records) from the 3 major dairy cooperatives, were recorded. Genetic analysis was carried out on MCP and estimated breeding values were obtained. Bulk milk samples were used [1] to study the sources of variation of MCP at herd level focusing more on management and feeding characteristics, [2] to optimize the cheese production at dairy level according to technological aptitude of milk to be converted into cheese, and [3] to define new quality payment systems that take into account the MCP. Currently, the projects are undergoing and the opportunity to extend the regional prototype at national level and at different dairy species is under evaluation.

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.

Summary Over the last years, healthy food has gained interest among consumers, especially with regard to the fat content of livestock products which has been associated to the risk of cardiovascular diseases. Individual milk samples (n = 12,624) of 2,977 Holstein-Friesian (HF), Brown Swiss (BS) and Simmental (SI) cows from 39 multibreed herds were analyzed for fat content, protein content, casein content and somatic cell count using mid-infrared spectroscopy (MIRS). Daily milk yield was also recorded. Groups of fatty acids (FA), expressed as percentage of milk fat, were predicted by MIRS: they were saturated (SFA), unsaturated (UFA), monounsaturated (MUFA) and polyunsaturated (PUFA) FA. Data were analyzed with a linear mixed model including the fi xed eff ects of month of sampling, parity, days in milk (DIM), herd, breed, and interactions between parity and breed, and DIM and breed. Th e random eff ects were cow nested within breed and residual. Milk of HF cows exhibited the lowest percentage of SFA (70.45%) and the highest of UFA (31.20%), and milk of SI cows was intermediate between that of HF and BS breeds for all groups of FA. Th e values of groups of FA across DIM were similar for the diff erent breeds. Results from this study indicate that, under similar environmental and management conditions, milk of HF exhibits better FA profi le than milk of BS and SI.