Heat Stability Assessment of Milk: A Review of Traditional and Innovative Methods

It is important to differentiate milk with different thermostabilities for diverse applications in food products and for the appropriate selection of processing and maintenance of manufacturing facilities. In this review, an overview of the chemical changes in milk subjected to high-temperature heating is given. An emphasis is given to the studies of traditional and state-of-the-art strategies for assessing the milk thermostability, as well as their influencing factors. Traditional subjective and objective techniques have been used extensively in many studies for evaluating thermostability, whereas recent research has been focused on novel approaches with greater objectivity and accuracy, including innovative physical, spectroscopic, and predictive tools.

Single-step genome-wide association analyses for selected infrared-predicted cheese-making traits in Walloon Holstein cows

This study aimed to perform genome-wide association study to identify genomic regions associated with milk production and cheese-making properties (CMP) in Walloon Holstein cows. The studied traits were milk yield, fat percentage, protein percentage, casein percentage (CNP), calcium content, somatic cell score (SCS), coagulation time, curd firmness after 30 min from rennet addition, and titratable acidity. The used data have been collected from 2014 to 2020 on 78,073 first-parity (485,218 test-day records), 48,766 second-parity (284,942 test-day records), and 21,948 third-parity (105,112 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA) of 6,617 animals (1,712 males), were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 KB) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for positional candidate genes. Heritability estimates for the studied traits ranged from 0.10 (SCS) to 0.53 (CNP), 0.10 (SCS) to 0.50 (CNP), and 0.12 (SCS) to 0.49 (CNP) in the first, second, and third parity, respectively. Genome-wide association analyses identified 6 genomic regions (BTA1, BTA14 [4 regions], and BTA20) associated with the considered traits. Genes including the SLC37A1 (BTA1), SHARPIN, MROH1, DGAT1, FAM83H, TIGD5, MROH6, NAPRT, ADGRB1, GML, LYPD2, JRK (BTA14), and TRIO (BTA20) were identified as positional candidate genes for the studied CMP. The findings of this study help to unravel the genomic background of a cow's ability for cheese production and can be used for the future implementation and use of genomic evaluation to improve the cheese-making traits in Walloon Holstein cows.

Milk coagulation properties are moderately heritable in dairy cows: a meta-analysis using the random-effects model

This study aimed to conduct a meta-analysis using the random-effects model to merge published genetic parameter estimates for milk coagulation properties (MCP: comprising rennet coagulation time (RCT), curd-firming time (k20), curd firmness 30 min after rennet addition (a30), titrable acidity (TA) and milk acidity or pH) in dairy cows. Overall, 80 heritability estimates and 157 genetic correlations from 23 papers published between 1999 and 2020 were used. The heritability estimates for RCT, a30, k20, TA, and pH were 0.273, 0.303, 0.278, 0.189 and 0.276, respectively. The genetic correlation estimates between RCT-a30, RCT-pH, and RCT-TA were 0.842, 0.549 and -0.565, respectively. Genetic correlation estimates between RCT and production traits were generally low and ranged from -0.142 (between RCT and casein content) to 0.094 (between RCT and somatic cell score). Moderate and significant genetic correlations were observed between a30-pH (-0.396) and a30-TA (0.662). Also, the genetic correlation estimates between a30 and production traits were low to moderate and varied from -0.165 (between a30 and milk yield) to 0.481 (between a30 and casein content). Genetic correlation estimates between pH and production traits were low and varied from -0.190 (between pH and milk protein percentage) to 0.254 (between pH and somatic cell score). The results of this meta-analysis indicated the existence of additive genetic variation for MCP that could be used in genetic selection programs for dairy cows. Because of the moderate heritability of MCP and small genetic correlations with production traits, it could be possible to improve MCP with negligible correlated effects on production traits.

Spectroscopic technologies and data fusion: Applications for the dairy industry

Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.

Impacts on two dairy breeds of adding a third (night) cooling event under extreme ambient heat

Impacts of an additional cooling event during the night on physiological and productive parameters of two breeds (B) of dairy cows under severe heat stress conditions were determined. Fifteen Holstein (H) and 15 Jersey x Holstein (JxH) cows were assigned to one of the two cooling strategies (CS), where the difference was an added 1 h cooling event during the night (i.e., 3X and 4X cooling events) with the 4X group having the added night cooling event. Maximum and minimum average temperature/humidity indices during the study were 86 and 77 units, respectively. There were no CS x B interactions for any response variable. Body condition score (BCS) was similar between CS, but H cows had higher (P<0.05) condition than JxH. There were no differences in rectal temperature due to CS or B. Respiration rate between breeds and cooling strategies were similar throughout the day. However body surface temperatures of head, thurl, right flank, and udder were higher (P<0.05) in 3X versus 4X cows, and H cows maintained a higher (P < 0.05) temperature in thurl and right flank than JxH during PM hours. Metabolites and hormone concentrations were not affected by CS, but H cows had lower (P < 0.05) triiodothyronine and higher (P < 0.05) thyroxine, than JxH. The 4X cows had higher (P<0.05) milk yield and milk energy output than 3X cows. While H cows produced more milk than JxH, the latter had higher (P < 0.05) milk component levels. In general, JxH cows were judged to have to demonstrate a more desirable physiological response and milk composition outcome than H cows. An additional night cooling event was judged to result in more desirable physiological and productive responses than in cows without this extra night cooling event under the extremely hot and dry environmental conditions during the study.

Heritability estimates of predicted blood β-hydroxybutyrate and nonesterified fatty acids and relationships with milk traits in early-lactation Holstein cows

At the beginning of lactation, high-producing cows commonly experience an unbalanced energy status that is often responsible for the onset of metabolic disorders and impaired health and performance. Blood β-hydroxybutyrate (BHB) and nonesterified fatty acids (NEFA) are indicators of excessive fat mobilization and circulating ketone bodies. Recently, prediction models based on mid-infrared (MIR) spectroscopy have been developed to assess blood BHB and NEFA from routinely collected individual milk samples. This study aimed to estimate genetic parameters of blood BHB and NEFA predicted from milk MIR spectra and to assess their phenotypic and genetic correlations with milk production and composition traits in early-lactation Holstein cows. The data set comprised the first test-day record within lactation and spectra of individual milk samples (n = 22,718) of 13,106 Holstein cows collected from 5 to 35 d in milk (DIM). Blood BHB and NEFA were predicted from milk MIR spectra using previously developed prediction models. Genetic parameters of blood metabolites and milk traits were estimated for the whole observational period (5-35 DIM) and within 6 classes of DIM. Blood BHB and NEFA showed similar genetic variation across DIM, with the highest heritability in the first 10 d after calving (0.31 ± 0.06 and 0.19 ± 0.05 for BHB and NEFA, respectively). The genetic correlation between BHB and NEFA was moderate (0.51 ± 0.05). Genetic correlations of BHB with milk yield, SCS, protein percentage, lactose percentage, and urea nitrogen content were similar to, or at least in the same direction as, the correlations of NEFA with the same traits, whereas opposite correlations were observed with fat percentage and fat-to-protein ratio. Results of the current study suggest that blood BHB and NEFA predicted from milk MIR spectra have genetic variation that is potentially exploitable for breeding purposes. Therefore, they could be used as indicator traits of hyperketonemia in a selection index aimed to reduce the susceptibility of dairy cows to metabolic disorders in early lactation.

Genetic relationships of alternative somatic cell count traits with milk yield, composition and udder type traits in Italian Jersey cows

The aim of this study was to estimate genetic associations between alternative somatic cell count (SCC) traits and milk yield, composition and udder type traits in Italian Jersey cows. Alternative SCC traits were test-day (TD) somatic cell score (SCS) averaged over early lactation (SCS_150), standard deviation of SCS of the entire lactation (SCS_SD), a binary trait indicating absence or presence of at least one TD SCC >400,000 cells/ml in the lactation (Infection) and the ratio of the number of TD SCC >400,000 cells/ml to total number of TD in the lactation (Severity). Heritabilities of SCC traits, including lactation-mean SCS (SCS_LM), ranged from 0.038 to 0.136. Genetic correlations between SCC traits were moderate to strong, with very few exceptions. Unfavourable genetic associations between milk yield and SCS_SD and Infection indicated that high-producing cows were more susceptible to variation in SCC than low-producing animals. Cows with deep udders, loose attachments, weak ligaments and long teats were more susceptible to an increase of SCC in milk. Overall, results suggest that alternative SCC traits can be exploited to improve cow's resistance to mastitis in Italian Jersey breed.

Cattle stratified on genetic merit segregate on carcass characteristics, but there is scope for improvement

The study objective was to quantify the ability of genetic merit for a generated carcass index to differentiate animals on primal carcass cut weights using data from 1,446 herds on 9,414 heifers and 22,413 steers with weights for 14 different primal carcass cuts (plus 3 generated groups of cuts). The carcass genetic merit index was compromised of carcass weight (positive weight), conformation (positive weight), and fat score (negative weight), each equally weighted within the index. The association analyses were undertaken using linear mixed models; models were run with or without carcass weight as a covariate. In a further series of analyses, carcass weight and carcass fat score were both included as covariates in the models. Whether the association between primal cut yield and carcass weight differed by genetic merit stratum was also investigated. Genetic merit was associated (P < 0.001) with the weight of all cuts evaluated even when adjusted to a common carcass weight (P < 0.01); when simultaneously adjusted to a common carcass weight and fat score, genetic merit was not associated with the weight of the cuberoll or the group cuts termed minced-meat. The weight of the different primal cuts increased almost linearly within increasing genetic merit, with the exception of the rump and bavette. The difference in mean primal cut weight between the very low and very high genetic merit strata, as a proportion of the overall mean weight of that cut in the entire data set, varied from 0.05 (bavette) to 0.28 (eye of round); the average was 0.17. Following adjustment for differences in carcass weight, there was no difference in cut weight between the very low and very high strata for the rump, chuck tender, and mince cut group; the remaining cuts were heavier in the higher index animals with the exception of the cuberoll and bavette, which were lighter in the very high index animals. The association between carcass weight and the weight of each of the evaluated primal cuts differed (P < 0.05) by genetic merit stratum for all cuts evaluated with the exception of the rump, striploin, and brisket as well as the group cuts of frying and mincing. With the exception of these 5 primal (group) cuts, the regression coefficients of primal cut weight on carcass weight increased consistently for all traits with increasing genetic merit stratum, other than for the fillet, cuberoll, bavette, chuck and neck, and heel and shank.

Variation of Detailed Protein Composition of Cow Milk Predicted from a Large Database of Mid-Infrared Spectra

Simple Summary

Milk proteins are one of the most valuable milk components. The objective of the present study was to assess sources of variation of detailed protein composition predicted from infrared spectra in milk of dairy and dual-purpose cattle breeds. Results showed that protein fractions were primarily influenced by days in milk, and the relative proportion of each fraction through lactation was not constant. Protein fractions correlated with crude protein, total casein, fat and milk urea nitrogen. In perspective, mid-infrared predictions of milk fractions could be useful for the dairy sector to improve nutritional and technological properties of milk.

Abstract

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.

Circannual changes in major chemical composition of bulk dromedary camel milk as determined by FT-MIR spectroscopy, and factors of variation

We monitored the major chemical composition of bulk dromedary camel milk by FT-MIR spectroscopy over a 5-year period. The results highly correlated with those determined with reference methods (r > 0.985, p < 0.001). Production parameters showed significant (p < 0.001) seasonal and yearly changes. The overall mean fat, protein, lactose, solids-not-fat, and total solids concentrations of bulk dromedary camel milk were 2.87%, 2.94%, 4.15%, 8.00%, and 10.69%, respectively. Month of the year, year of the study, and level of production had a strong influence on bulk milk chemical composition and yield of milk components; however, the relative effect of season on composition was greater (proportion of variance app. 50%) compared to that of other factors of variation. The highest and lowest values were measured during winter and summer, respectively. Circannual variation in major milk components was associated with environmental conditions (photoperiod, temperature), whereas it was independent of nutritional factors.

Development of Fourier-transformed mid-infrared spectroscopy prediction models for major constituents of fractions of delactosated, defatted milk obtained through ultra- and nanofiltration

Milk filtration procedures are gaining relevance in the dairy industry because milk ultra- and nanofiltrates are used to increase milk processing efficiency, and as additives for products with improved nutraceutical properties. This study aimed to develop Fourier-transformed mid-infrared spectroscopy calibrations for ultra- and nanopermeate and retentate fractions of defatted and delactosated milk. A total of 154 samples from different milk fractions were collected and analyzed using reference methods to determine protein, solids-not-fat, glucose, and galactose content. The obtained values were matched with their respective Fourier-transformed mid-infrared spectroscopy spectra to develop new prediction models. Calibrations for each trait were built following 3 different approaches to get the best prediction models: (1) using the entire data set, (2) using 3 subsets based on component concentrations (level approach), and (3) using hierarchical clusters calculated with pairwise Mahalanobis distance among spectra (cluster approach). Calibrations were developed using partial least squares regression, after removing low signal-to-noise ratio wavelengths, and validated through a leave-one-out cross-validation procedure. In addition, the accuracy of the predicted values within each fraction was checked for each approach. Dividing the data set into subsets improved prediction models for each trait and for the samples in each milk fraction. Without considering milk fraction, the best improvement was observed for glucose and galactose. Glucose ratio performance deviation in cross-validation (RPD) increased from 7.42 to 11.31 and 11.06, for cluster and level approaches, respectively, whereas galactose RPD increased from 8.86 to 11.69 and 11.27 for cluster and level approaches, respectively. Considering milk fractions, the best improvement was observed for protein content, where RPD ranged from 0.08 to 6.06 for the whole data set calibration, whereas it ranged from 0.43 to 40.34 for the subset calibration approaches. Cluster and level approaches to build calibration models were comparable for samples from different fractions, suggesting that the 2 subsetting protocols should be both investigated to get the best prediction performances.