The aim of this study was to evaluate the effectiveness of mid-infrared spectroscopy in predicting milk protein and free amino acid (FAA) composition in bovine milk. Milk samples were collected from 7 Irish research herds and represented cows from a range of breeds, parities, and stages of lactation. Mid-infrared spectral data in the range of 900 to 5,000 cm(-1) were available for 730 milk samples; gold standard methods were used to quantify individual protein fractions and FAA of these samples with a view to predicting these gold standard protein fractions and FAA levels with available mid-infrared spectroscopy data. Separate prediction equations were developed for each trait using partial least squares regression; accuracy of prediction was assessed using both cross validation on a calibration data set (n=400 to 591 samples) and external validation on an independent data set (n=143 to 294 samples). The accuracy of prediction in external validation was the same irrespective of whether undertaken on the entire external validation data set or just within the Holstein-Friesian breed. The strongest coefficient of correlation obtained for protein fractions in external validation was 0.74, 0.69, and 0.67 for total casein, total β-lactoglobulin, and β-casein, respectively. Total proteins (i.e., total casein, total whey, and total lactoglobulin) were predicted with greater accuracy then their respective component traits; prediction accuracy using the infrared spectrum was superior to prediction using just milk protein concentration. Weak to moderate prediction accuracies were observed for FAA. The greatest coefficient of correlation in both cross validation and external validation was for Gly (0.75), indicating a moderate accuracy of prediction. Overall, the FAA prediction models overpredicted the gold standard values. Near-unity correlations existed between total casein and β-casein irrespective of whether the traits were based on the gold standard (0.92) or mid-infrared spectroscopy predictions (0.95). Weaker correlations among FAA were observed than the correlations among the protein fractions. Pearson correlations between gold standard protein fractions and the milk processing characteristics of rennet coagulation time, curd firming time, curd firmness, heat coagulating time, pH, and casein micelle size were weak to moderate and ranged from -0.48 (protein and pH) to 0.50 (total casein and a30). Pearson correlations between gold standard FAA and these milk processing characteristics were also weak to moderate and ranged from -0.60 (Val and pH) to 0.49 (Val and K20). Results from this study indicate that mid-infrared spectroscopy has the potential to predict protein fractions and some FAA in milk at a population level.

Rapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n=713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000cm(-1) were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation.

Lactose percentage (LP) in milk is currently determined in most herd-testing schemes, and globally, it is usually routinely recorded in the framework of the official milk recording procedures. However, few studies have investigated the phenotypic and genetic variability of this component. Data used in the present paper consisted of 59 811 test-day records from 4355 Holstein cows in 266 herds. Heritabilities of LP and lactose yield (LY) were estimated through single-trait repeatability animal models, whereas genetic and phenotypic correlations of LP and LY with milk composition and production traits, somatic cell score and milk freezing point were estimated using bivariate models. Fixed effects included in the analyses were herd-test-date, season of calving, parity, stage of lactation and the interaction between parity and stage of lactation. Random effects were animal additive genetic, within and across lactation permanent environment and the residual. Lactation curves of LP and LY increased from parturition to the peak of lactation and decreased thereafter, mirroring the typical curve of milk yield. Lactose percentage was greater in first- than later-parity cows. Heritabilities of LP and LY were 0.43±0.03 and 0.14±0.02, respectively, and LP and protein percentage were the most repeatable traits. Genetic correlations (r a) of LP with somatic cell score, LY and milk freezing point were −0.22±0.08, 0.28±0.08 and −0.46±0.05, respectively. Genetic relationships of LY with milk yield (r a=0.97±0.00), fat percentage (r a=−0.71±0.06), protein percentage (r a=−0.57±0.06) and protein yield (r a=0.64±0.06) were moderate to strong. Results suggest that milk LP could be considered in breeding strategies to accelerate the gain of correlated low heritable traits. Further research is needed to evaluate the feasibility of including LP in the selection index of Italian Holstein population to address country-specific needs and market demands.

Milk minerals and coagulation properties are important for both consumers and processors, and they can aid in increasing milk added value. However, large-scale monitoring of these traits is hampered by expensive and time-consuming reference analyses. The objective of the present study was to develop prediction models for major mineral contents (Ca, K, Mg, Na, and P) and milk coagulation properties (MCP: rennet coagulation time, curd-firming time, and curd firmness) using mid-infrared spectroscopy. Individual milk samples (n=923) of Holstein-Friesian, Brown Swiss, Alpine Grey, and Simmental cows were collected from single-breed herds between January and December 2014. Reference analysis for the determination of both mineral contents and MCP was undertaken with standardized methods. For each milk sample, the mid-infrared spectrum in the range from 900 to 5,000cm(-1) was stored. Prediction models were calibrated using partial least squares regression coupled with a wavenumber selection technique called uninformative variable elimination, to improve model accuracy, and validated both internally and externally. The average reduction of wavenumbers used in partial least squares regression was 80%, which was accompanied by an average increment of 20% of the explained variance in external validation. The proportion of explained variance in external validation was about 70% for P, K, Ca, and Mg, and it was lower (40%) for Na. Milk coagulation properties prediction models explained between 54% (rennet coagulation time) and 56% (curd-firming time) of the total variance in external validation. The ratio of standard deviation of each trait to the respective root mean square error of prediction, which is an indicator of the predictive ability of an equation, suggested that the developed models might be effective for screening and collection of milk minerals and coagulation properties at the population level. Although prediction equations were not accurate enough to be proposed for analytic purposes, mid-infrared spectroscopy predictions could be evaluated as phenotypic information to genetically improve milk minerals and MCP on a large scale.

This study aimed to investigate factors affecting protein fractions, namely α-casein (α-CN), β-casein (β-CN), κ-casein (κ-CN), β-lactoglobulin (β-LG) and α-lactalbumin (α-LA) predicted from milk infrared spectra in milk of dairy and dual-purpose cattle breeds. The dataset comprised 735,328 observations from 49,049 cows in 1782 herds. Results highlighted significant differences of protein fractions in milk of the studied breeds. Significant variations of protein fractions were found also through parities and lactation, with the latter thoroughly influencing protein fractions percentage. Interesting correlations (r) were estimated between β-CN, κ-CN and β-LG, expressed as percentage of crude protein, and milk urea nitrogen (r = 0.31, −0.20 and −0.26, respectively) and between α-LA and fat percentage (r = 0.41). The present study paves the way for future studies on the associations between protein fractions and milk technological properties, and for the estimation of genetic parameters of predicted protein composition.

Population-level phenotyping is, for some traits, hardly achievable due to limitation of reference analyses. Mid-infrared spectroscopy (MIRS) is a quick and cost-effective laboratory technique allowing the prediction of innovative milk quality features on a large scale. The present study aimed at investigating factors associated with milk Ca, K, Mg, Na and P contents predicted by MIRS models on a large multi-breed spectral data set of 123,240 test-day records. Two dairy (Holstein–Friesian and Brown Swiss) and two dual-purpose (Simmental and Alpine Grey) cattle breeds were considered. Sources of variation of predicted milk mineral contents were studied using linear mixed models, including the fixed effects of breed, month of sampling, stage of lactation, parity and interactions between the main effects. Random factors were herd nested within breed, cow nested within breed and the residual. Calcium content was greater in milk of dual-purpose than dairy cattle breeds. Simmental cows produced milk with the greatest content of Ca and Na, and milk of Holstein-Friesian had the lowest P content. Variation of content of Ca, Mg, Na and P throughout lactations exhibited an opposite trend to that of milk yield, with the lowest values around the peak of lactation. On the other hand, K content mirrored the trend of milk yield. Multiparous cows had lower content of milk minerals, with the exception of Na, compared with first parity animals. Environmental factors identified in the present study can be considered for within-breed genetic evaluation to adjust records of milk mineral contents for these effects.