Milk Coagulation Properties Research Articles

Observational study on the associations between milk yield, composition, and coagulation properties with blood biomarkers of health in Holstein cows

The considerable increase in the production capacity of individual cows owing to both selective breeding and innovations in the dairy sector has posed challenges to management practices in terms of maintaining the nutritional and metabolic health status of dairy cows. In this observational study, we investigated the associations between milk yield, composition, and technological traits and a set of 21 blood biomarkers related to energy metabolism, liver function or hepatic damage, oxidative stress, and inflammation or innate immunity in a population of 1,369 high-yielding Holstein-Friesian dairy cows. The milk traits investigated in this study included 4 production traits (milk yield, fat yield, protein yield, daily milk energy output), 5 traits related to milk composition (fat, protein, casein, and lactose percentages and urea), 11 milk technological traits (5 milk coagulation properties and 6 curd-firming traits). All milk traits (i.e., production, composition, and technological traits) were analyzed according to a linear mixed model that included the days in milk, the parity order, and the blood metabolites (tested one at a time) as fixed effects and the herd and date of sampling as random effects. Our findings revealed that milk yield and daily milk energy output were positively and linearly associated with total cholesterol, nonesterified fatty acids, urea, aspartate aminotransferase, γ-glutamyl transferase, total bilirubin, albumin, and ferric-reducing antioxidant power, whereas they were negatively associated with glucose, creatinine, alkaline phosphatase, total reactive oxygen metabolites, and proinflammatory proteins (ceruloplasmin, haptoglobin, and myeloperoxidase). Regarding composition traits, the protein percentage was negatively associated with nonesterified fatty acids and β-hydroxybutyrate (BHB), while the fat percentage was positively associated with BHB, and negatively associated with paraoxonase. Moreover, we found that the lactose percentage increased with increasing cholesterol and albumin and decreased with increasing ceruloplasmin, haptoglobin, and myeloperoxidase. Milk urea increased with an increase in cholesterol, blood urea, nonesterified fatty acids, and BHB, and decreased with an increase in proinflammatory proteins. Finally, no association was found between the blood metabolites and milk coagulation properties and curd-firming traits. In conclusion, this study showed that variations in blood metabolites had strong associations with milk productivity traits, the lactose percentage, and milk urea, but no relationships with technological traits of milk. Specifically, increasing levels of proinflammatory and oxidative stress metabolites, such as ceruloplasmin, haptoglobin, myeloperoxidase, and total reactive oxygen metabolites, were shown to be associated with reductions in milk yield, daily milk energy output, lactose percentage, and milk urea. These results highlight the close connection between the metabolic and innate immunity status and production performance. This connection is not limited to specific clinical diseases or to the transition phase but manifests throughout the entire lactation. These outcomes emphasize the importance of identifying cows with subacute inflammatory and oxidative stress as a means of reducing metabolic impairments and avoiding milk fluctuations.

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.

Identification of the coagulation properties of Chinese Holstein bovine milk: Effects of milk compositions, milk protein polymorphism, and phosphorylation levels on milk coagulation ability

Milk coagulation ability plays critical roles in cheese production. To assess the effects of milk composition, protein polymorphisms, and phosphorylation levels on milk coagulation properties, milk samples were collected from 3625 Chinese Holstein cows and milk was pooled based on their coagulation properties. The results showed that good coagulating milk had shorter rennet coagulation time and higher gel firmness with tighter connections within the formed gel. Multiple factors were observed to differ among milk with different coagulation ability. For example, lower milk pH, higher amounts of proteins and casein, were related to good coagulation properties. The salt distributions also influenced milk coagulation ability. Especially the calcium content has a large influence on milk coagulation. This may be due to its influence on the aggregation of paracasein micelles in the secondary phase of rennet coagulation. Protein polymorphisms and phosphorylation degrees were found being higher in non-coagulating milk. The proteomics results showed that lower amounts of αS1-casein may result in non-coagulation. Differences of milk fat globule membrane proteins were also observed among milk samples with different coagulation ability, possibly being related to milk protein and fat synthesis, in turn influencing the coagulation process. In addition, Ser76 of αS2-CN was found being highly phosphorylated in non-coagulating milk which may be related to its binding with other caseins during the second phase of rennet coagulation. Of these factors, salt distribution, mainly calcium and magnesium, may be the most important factor affecting casein structures and thereby milk coagulation. Importantly, these factors can affect each other while forming the cheese matrix, resulting in a complex relation with milk coagulation.

Correlations of goat milk coagulation properties between dams and daughters

Goat milk coagulation properties (gMCP) are important trait for the cheesemaking industry but are rarely included in national breeding programs. In the present study, we monitored goat milk quality traits and gMCP of Slovenian Alpine goats within a single flock (N = 77), reared in the same common environment over three-year period. In addition, using pedigree data, we looked for correlations among dams, daughters (N = 32 pairs) and gMCP. Traditional gMCP measurements were introduced into a 4-parameter curd firmness over time (CFt) model. Several gMCP parameters were associated with parity and stage of lactation. Most notably, estimated rennet coagulation time (RCTeq), curd firmness at 15 (a15eq) and 20 min (a20eq) after rennet addition were strongly correlated between dams and their daughters. These results support further research of genetic parameters for gMCP and the inclusion of breeding value predictions for gMCP in national breeding programs.

A Study of Milk Composition and Coagulation Properties of Holstein-Friesian, Jersey, and Their Cross Milked Once or Twice a Day

The objective of the study was to explore the effect of breed on the composition and coagulation properties (rennet coagulation time (min), curd firming rate (min), and curd firmness (mm)) of milk from cows milked once a day or twice a day in the morning and afternoon, using a Formagraph. Thirty cows (11 Holstein-Friesian, 8 Holstein-Friesian × Jersey cross, and 11 Jersey) from a once-a-day milking herd and thirty cows (16 Holstein-Friesian, 10 Holstein-Friesian × Jersey cross, and 4 Jersey) from a twice-a-day milking herd were sampled in late lactation. The milk composition and coagulation properties were analysed for each milk sample. Jersey cows had better milk coagulation properties at each milking frequency-milking time compared to Holstein-Friesian cows. Curd firmness 30 min after the addition of rennet was positively (p < 0.05) correlated with the protein concentration. However, the correlations were inconsistent between milking frequencies and milking times, resulting in poor prediction of the changes in cheese-making potential. This study indicated that milk composition and coagulation properties were affected by breed and milking frequency. The effect of the breed could be due to the variation in the composition of the milk, but firm recommendations were hampered by a low number of samples. Further research with larger cow numbers is justified.

Effect of jujube pulp on rennet-induced coagulation properties of milk with different fat contents

The effect of jujube pulp on rennet-induced coagulation of milk with different fat contents was investigated. Whole, reduced-fat and skim milk were mixed with concentrated jujube pulp in different percentages (100:0, 90:10, 80:20, 70:30, w/w); addition of jujube pulp advanced the time point at which elasticity index began to increase, decreased the elasticity index at any given time during renneting, increased the moisture content and curd yield, disrupted the protein network, and reduced hardness, springiness and storage modulus of the curds. However, the curd colour was non-uniform with 10% jujube pulp, but became a uniform “date-red” colour with higher jujube pulp concentrations. Moreover, sensory characteristics of reduced-fat and skim milk curds were inferior to those from whole milk, whereas jujube pulp significantly improved sensory characteristics of the former, especially their hardness, smoothness and jujube flavour. This study has highlighted the potential of jujube pulp for producing unique-flavoured cheese-like products.

Efficiency of Manchega Sheep Milk Intended for Cheesemaking and Determination of Factors Causing Inefficiency.

Understanding the factors that determine and regulate cheese yield would allow, through deterministic parametric efficiency models, the determination of the most appropriate milk characteristics for the industry, and the estimation of a technological value for these characteristics. The present study aims to evaluate coagulation performance of Manchega sheep milk intended for cheesemaking and explores two models to determine milk technological efficiency. For this purpose, 1200 Manchega sheep milk samples were collected, and analyses were performed for composition, milk coagulation properties (MCP), somatic cell count (SCC), and milk color values. A first model was built based on curd yield (CE) and a second one based on dry curd yield (DCE). GLM and MANCOVA analyses were used to identify the factors that determine curd yield efficiency, which mainly depended on pH, casein, and lactose content and, to a lesser extent, on the speed of coagulation and curd firmness. When comparing both models, differences were linked to the water retention capacity of the curd. Based on this, the DCE model was considered much more accurate for prediction of coagulation efficiency in a wider variety of cheeses, as it does not seem to be affected by moisture loss.

Acid and Rennet Coagulation Properties of A2 Milk.

This study investigated the acid and rennet milk coagulation properties of A2 milk (β-casein (CN) A2A2 genotype), in comparison to a control milk (blend of A2A1/A1A1/A2A2 genotypes). Acid and rennet coagulation were evaluated using the Optigraph® system, measuring the coagulation time, aggregation rate, and gel density or curd firmness. The acidification kinetics were monitored using a CINAC® system, evaluating the time to reach pH 4.6, the acidification rate, the maximum acidification rate, the time required to reach it, and the latency time. The water-holding capacity of acid milk gels and the potential yield, total solids, and syneresis of enzymatic gels were also evaluated. Some variables were highly influenced by the farm factor, showing the importance of the effect of extrinsic parameters. Acid and enzymatic coagulation times were not affected in either milk. The A2 milk presented higher acid gel density and latency time than the control milk. Although the differences in water-holding capacity were not statistically significant, the A2 milk presented lower values, related with the higher gel density. The A2 milk also showed higher rennet aggregation rate and curd firmness than the control milk. Potential yield and syneresis were higher in the A2 milk, which is in accordance with the higher firmness of curd. Coagulation results and gel and curd properties indicate that it is possible to manufacture acid and rennet coagulation dairy products from A2 milk with no major differences when compared with a control milk.

Milk Coagulation Properties: A Study on Milk Protein Profile of Native and Improved Cattle Breeds/Types in Sri Lanka

This study was conducted to assess the variations of milk coagulation properties (MCP) among two native cattle types, e.g., Thamankaduwa White (TW), Lankan cattle (LC) and two improved cattle breeds, e.g., Friesian (FR) and Jersey (JS), in relation to distinctive milk protein compositions. MCP traits, including rennet coagulation time (RCT), curd firmness, meltability and yield, were measured. The milk protein profile of each breed/type was analyzed using capillary zone electrophoresis. Significant differences (p < 0.05) were observed among two native and improved cattle breeds/types in relation to RCT. Friesian and TW milk had the longest and shortest (p < 0.05) RCT, respectively. There was no significant difference in firmness among the four breeds/types. The highest (p < 0.05) coagulum yield was recorded for TW milk, followed by LC, JS and FR. TW milk had the highest (p < 0.05) meltability values. As revealed by the protein profiles, κ-casein concentration was significantly higher in TW milk compared to the other three breeds/types. None of the other milk protein fractions showed significant differences among the four breeds/types. The overall results indicate the superior MCP of TW milk, emphasizing the value of native breeds which could be exploited in the development of niche dairy products while supporting the conservation effort of the native cattle gene pool.

Relationship between β-casein genotypes (A1A1, A1A2, and A2A2) and coagulation properties of milk and the fatty acid composition and sensory characteristics of dairy products (soft cheese, sour cream, and butter)

ABSTRACT We examined how β-casein CSN2 genotypes (A1A1, A1A2, A2A2) affected milk coagulation properties, the fatty acid composition, and sensory traits of milk products (soft cheese, sour cream, and butter). We collected 300 L of milk from 96 cows in the same herd and sorted it into 100-L milk pools according to the β-casein genotype. Rennet coagulation time and curd-firming rate were fastest in A2A2 milk and slowest in A1A1 milk. Accordingly, curd firmness was highest in A2A2 milk and lowest in A1A1 milk. A1A1 milk products had higher levels of monounsaturated fatty acids and lower levels of saturated fatty acids. Polyunsaturated fatty acids were mainly found in A2A2 milk products and were lowest in A1A2 products. Although CSN2 genotypes influenced milk coagulation traits and fatty acid content of milk products, they did not influence the latter's sensory traits. Therefore, selectively breeding genotypes with preferred qualities may improve dairy products.

Low-density SNP panel for efficient imputation and genomic selection of milk production and technological traits in dairy sheep.

The present study aimed to ascertain how different strategies for leveraging genomic information enhance the accuracy of estimated breeding values for milk and cheese-making traits and to evaluate the implementation of a low-density (LowD) SNP chip designed explicitly for that aim. Thus, milk samples from a total of 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra) were collected and analyzed to determine 3 milk production and composition traits and 2 traits related to milk coagulation properties and cheese yield. The 2 studied populations were genotyped with a customized 50K Affymetrix SNP chip (Affymetrix Inc.) containing 55,627 SNP markers. The prediction accuracies were obtained using different multitrait methodologies, such as the BLUP model based on pedigree information, the genomic BLUP (GBLUP), and the BLUP at the SNP level (SNP-BLUP), which are based on genotypic data, and the single-step GBLUP (ssGBLUP), which combines both sources of information. All of these methods were analyzed by cross-validation, comparing predictions of the whole population with the test population sets. Additionally, we describe the design of a LowD SNP chip (3K) and its prediction accuracies through the different methods mentioned previously. Furthermore, the results obtained using the LowD SNP chip were compared with those based on the 50K SNP chip data sets. Finally, we conclude that implementing genomic selection through the ssGBLUP model in the current breeding programs would increase the accuracy of the estimated breeding values compared with the BLUP methodology in the Assaf (from 0.19 to 0.39) and Churra (from 0.27 to 0.44) dairy sheep populations. The LowD SNP chip is cost-effective and has proven to be an accurate tool for estimating genomic breeding values for milk and cheese-making traits, microsatellite imputation, and parentage verification. The results presented here suggest that the routine use of this LowD SNP chip could potentially increase the genetic gains of the breeding selection programs of the 2 Spanish dairy sheep breeds considered here.

Effects of forage quantity and access-time restriction on feeding behaviour, feed efficiency, nutritional status, and dairy performance of dairy cows fed indoors

Optimising feed is a key challenge for dairy livestock systems, as forage stock shortages are increasingly frequent and feed is the biggest operating cost. The aim of this experiment was to evaluate the effects of reducing forage quantity and access time on dairy performance and animal nutritional status during indoor feeding. Twenty-seven Montbéliarde and Holstein cows were randomly allocated to three groups of nine cows balanced by breed, parity, days in milk, and milk yield. The three groups were given 3.9 kg DM/day of second-cut hay and 4.5 kg/day of concentrate and either i) ad libitum access to first-cut hay (Ad Libitum group; AL), ii) 10.5 kg/day of first-cut hay (Quantity-restricted group; QR), or iii) 10.5 kg/day of first-cut hay but with access time restricted to only 2 h in the morning and 2 h in the afternoon (Quantity-and-Time-restricted group; QTR). Milk yield, composition and coagulation properties, cow nutritional status (weight, body condition score, blood metabolites) and cow activities were recorded. The AL group ingested 10 % more feed than the QR group and 16 % more feed than the QTR group. Organic matter digestibility was lower in the AL group than in the QR and QTR groups whereas feed efficiency did not differ. Milk yield was not significantly different among the three groups. Compared to the QR and QTR groups, the AL group had significantly higher milk fat (35.9 vs 32.9 and 32.8 g/kg of milk) and milk protein content (29.5 vs 27.7 and 28.5 g/kg of milk). QR and QTR cows mobilised their body fat, resulting in a lower final body condition score, and tended to have a lower blood non-esterified fatty acid concentration than the AL group. QTR cows showed greater body fat mobilisation, but their final corrected BW was not different from AL cows. Access-time restriction did not impact fat and protein content but led to decreased casein, lactose contents and casein-to-whey protein ratio. The forage savings achieved through this feed management practice could prove economically substantial when forage prices increase. This practice can be of interest in grassland systems to overcome certain climatic hazards without having to resort to purchases or to increase the farm's forage autonomy.

Effect of pasteurization on coagulation properties of bovine milk and the role of major composition traits and protein fractions

Milk coagulation properties (MCP) worsens after heat treatment, however the specific mechanisms responsible have been scarcely explored. In this study, 100 milk samples were available to i) identify the raw milk characteristics responsible for unfavorable changes in MCP after pasteurization and ii) develop infrared prediction models for pasteurized milk MCP using spectra of raw samples. The loss in coagulation ability due to pasteurization was lower when raw milk had optimal MCP, higher acidity, greater protein content and lower β-lactoglobulin content. For the four MCP, the trait measured before pasteurization (raw milk) was the most important variable influencing the corresponding trait after heating. For example, rennet coagulation time (RCT), κ-casein, protein, lactose and pH of raw milk significantly affected pasteurized milk RCT (P < 0.001). For curd firmness, each unit (mm) corresponded to 58.65 g/100 g κ-casein. In general, raw milk β-lactoglobulin unfavorably affected pasteurized milk MCP (e.g., the estimate of curd firming time was 81.39 g/100 g). Results suggested that only the prediction model of RCT (pasteurized milk) achieved an exploitable coefficient of determination in cross-validation (0.66). Our outcomes are relevant for dairy plants manufacturing cheese from pasteurized standardized milk and could support producers’ decision-making.

Impact of cheese milk cold storage on milk coagulation properties, calcium contents, and cheese yield

This study evaluated the impact of storage conditions (time, 0–48 h; temperature, 4–16 °C) of raw cheese milk on coagulation properties using diffusion wave spectroscopy. Specifically, rennet coagulation time (RCT) and curd firmness were affected by storage time and temperature. At 4–8 °C, the RCT increased with the storage time 24–48 h compared with the control, while curd firmness decreased. Cheese yield decreased remarkably at 8 °C and 24 h. At 12 °C, only the RCT increased at 48 h, while curd firmness decreased remarkably. A different finding was discovered at 16 °C: after a small RCT increase at 24 h, a significant decrease at 48 h was found, even though the pH was constant. Conversely, curd firmness was not significantly affected. Parallel to this study, free Ca2+ was analysed during cold storage, and the results were compared with total calcium analysis in whey.

Phenotypic and genetic characterization of the occurrence of noncoagulating milk in dairy sheep

Milk coagulation ability is of central importance for the sheep dairy industry because almost all sheep milk is destined for cheese processing. The occurrence of milk with impaired coagulation properties is an obstacle to cheese processing and, in turn, to the profitability of the dairy companies. In this work, we investigated the causes of noncoagulation of sheep milk; specifically, we studied the effect of milk physicochemical properties on milk coagulation status [coagulating and noncoagulating (NC) milk samples, which do or do not coagulate within 30 min, respectively], and whether mid-infrared spectroscopy (MIR) could be used to assess variability in coagulation status. We also investigated the genetic background of milk coagulation ability. Individual milk samples were collected from 996 Sarda ewes farmed in 47 flocks located in Sardinia (Italy). Considered traits were daily milk yield, milk composition traits, and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness), and MIR spectra were acquired. About 9% of samples did not coagulate within 30 min. A logistic regression approach was used to test the effect of milk-related traits on milk coagulation status. A principal component (PC) analysis was carried out on the milk MIR spectra, and PC scores were then used as covariates in a logistic regression model to assess their relationship with milk coagulation status. Results of the present work demonstrated that the probability of having NC samples increases as milk contents of proteins and chlorides and somatic cell score increase. The analysis of PC extracted from milk spectra that influenced coagulation status highlighted key regions associated with lactose and protein concentrations, and others not associated with routinely collected milk composition traits. These results suggest that the occurrence of NC is mostly related to damage of the epithelium secretory mammary cells, which occurs with the advancement of a lactation or due to unhealthy mammary gland status. Genetic analysis of milk coagulation status and of the extracted PC confirmed the genetic background of the milk coagulability of sheep milk.

Multivariate and Genome-Wide Analysis of Mid-Infrared Spectra of Non-Coagulating Milk of Sarda Sheep Breed

Milk coagulation ability is crucial for the dairy sheep industry since the whole amount of milk is processed into cheese. Non-coagulating milk (NCM) is defined as milk not forming a curd within the testing time. In sheep milk, it has been reported in literature that up to 20% of milk is NCM. Although the clotting properties of individual milk have been widely studied, little attention has been given to NCM and genomic dissection of this trait. Mid-infrared (MIR) spectra can be exploited both to predict cheese-making aptitude and to discriminate between coagulating milk and NCM. The main goals of this work were (i) to assess the predictivity of MIR spectra for NCM classification and (ii) to conduct a genome-wide association study on coagulation ability. Milk samples from 949 Sarda ewes genotyped and phenotyped for milk coagulation properties (MCPs) served as the training dataset. The validation dataset included 662 ewes. Three classical MCPs were measured: rennet coagulation time (RCT), curd firmness (a30), and curd firming time (k20). Moreover, MIR spectra were acquired and stored in the region between 925.92 and 5,011.54 cm−1. The probability of a sample to be NCM was modeled by step-wise logistic regression on milk spectral information (LR-W), logistic regression on principal component (LR-PC), and canonical discriminant analysis of spectral wave number (DA-W). About 9% of the samples did not coagulate at 30 min. The use of LR-W gave a poorer classification of NCM. The use of LR-PC improved the percentage of correct assignment (45 ± 9%). The DA-W method allows us to reach 75.1 ± 10.3 and 76.5 ± 18.4% of correct assignments of the inner and external validation datasets, respectively. As far as GWA of NCM, 458 SNP associations and 45 candidate genes were detected. The genes retrieved from public databases were mostly linked to mammary gland metabolism, udder health status, and a milk compound also known to affect the ability of milk to coagulate. In particular, the potential involvement of CAPNs deserves further investigation.

Real-time milk analysis integrated with stacking ensemble learning as a tool for the daily prediction of cheese-making traits in Holstein cattle

Cheese-making traits in dairy cattle are important to the dairy industry but are difficult to measure at the individual level because there are limitations on collecting phenotypic information. Mid-infrared spectroscopy has its advantages, but it can only be used during monthly milk recordings. Recently, in-line devices for real-time analysis of milk quality have been developed. The AfiLab recording system (Afimilk) offers significant benefits as phenotypes can be collected from each cow at each milking session. The objective of this study was to assess the potential of integrating AfiLab real-time milk analyzer measures with the stacking ensemble learning technique using heterogeneous base learners for the in-line daily monitoring of cheese-making traits in Holstein cattle with a view to developing a precision livestock farming system for monitoring the technological quality of milk. Data and samples for wet-laboratory analyses were collected from 499 Holstein cows belonging to 2 farms where the AfiLab system was installed. The traits of concern were 9 milk coagulation traits [3 milk coagulation properties (MCP), and 6 curd firming traits (CFt)], and 7 cheese-making traits [3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits]. The near-infrared AfiLab spectral data and on-farm information (days in milk and parity) were used to assess the predictive ability of different statistical methods [elastic net (EN), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and artificial neural network (ANN)] across different cross-validation scenarios. These statistical methods were considered the base learners, which were then combined in a stacking ensemble learning. Results indicate that including information on the cows (days in milk and parity) in the AfiLab infrared prediction increased its accuracy by 10.3% for traditional MCP, 13.8% for curd firming, 9.8% for CY, and 11.2% for REC traits compared with those obtained from near-infrared AfiLab alone. The statistical approaches exhibited high prediction accuracies (R2) averaged across the cross-validation scenarios for traditional MCP (0.58 for ANN, 0.55 for EN and GBM, 0.52 for XGBoost, and 0.62 for stacking ensemble), CFt (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble), and similar R2 averages for CY and REC (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble). The ANN approach was more accurate than the other base learners (EN, GBM, and XGBoost) and improved accuracy across cross-validation scenarios on average by 7% for traditional MCP, 5% for CFt, 8% for CY, and 7% for REC. The stacking ensemble method improved prediction accuracy by 3% to 31% for traditional MCP, 2% to 26% for CFt, 1% to 38% for CY traits, and 2% to 27% for REC traits compared with the base learners. The prediction accuracies of the different approaches evaluated tended to decrease from the 10-fold cross-validation to the independent validation scenario, although there was a smaller reduction in prediction accuracy with the stacking ensemble learning technique across all the cross-validation scenarios. Our results show that combining in-line on-farm information with stacking ensemble machine learning represents an effective alternative for obtaining robust daily predictions of milk cheese-making traits.

The β-casein (CSN2) A2 allelic variant alters milk protein profile and slightly worsens coagulation properties in Holstein cows

Milk proteins genetic variants have long attracted interest as they are associated with important issues relating to milk composition and technological properties. An important debate has recently opened at an international level on the role of β-casein (β-CN) A1 and A2 polymorphisms, toward human health. For this reason, a lot of efforts has been put into the promotion of A2 milk by companies producing and selling A1-free milk, leading the farmers and breeders to switch toward A2 milk production without paying attention on the potential effect of the processability of milk into cheese. The aim of the present work was to evaluate the effects of β-CN, specifically the A1 and A2 allelic variants, on the detailed milk protein profile and cheese-making traits in individual milk samples of 1,133 Holstein Friesian cows. The protein fractions were measured with reversed-phase (RP)-HPLC (expressed in g/L and % N), and the cheese-making traits, namely milk coagulation properties, cheese yield, and curd nutrient recoveries assessed at the individual level, with a nano-scale cheese-making procedure. The β-CN (CSN2), κ-CN (CSN3), and β-lactoglobulin (LGB) genetic variants were first identified through RP-HPLC and then confirmed through genotyping. Estimates of the effects of protein genotypes were obtained using a mixed inheritance model that considered, besides the standard nuisance variables (i.e., days in milk, parity, and herd-date), the milk protein genes located on chromosome 6 (CSN2, CSN3) and on chromosome 11 (LGB), and the polygenic background of the animals. Milk protein genes (CSN2, CSN3, and LGB) explained an important part of the additive genetic variance in the traits evaluated. The β-CN A1A1 was associated with a significantly lower production of whey proteins, particularly of β-lactoglobulin (-8.2 and -6.8% for g/L and % N, respectively) and α-lactalbumin (-4.7 and -4.4% for g/L and % N, respectively), and a higher production of β-CN (6.8 and 6.1% for g/L and % N, respectively) with respect to the A2A2 genotype. Regarding milk cheese-making ability, the A2A2 genotype showed the worst performance compared with the other genotypes, particularly with respect to the BA1, with a higher rennet coagulation time (7.1 and 28.6% compared with A1A1 and BA1, respectively) and a lower curd firmness at 30 min. Changes in milk protein composition through an increase in the frequency of the A2 allele in the production process could lead to a worsening of the coagulation and curd firming traits.

Fatty Acid Composition of Dairy Milk: A Case Study Comparing Once- and Twice-a-Day Milking of Pasture-Fed Cows at Different Stages of Lactation

In this case study, we compared the gross composition and fatty acid (FA) composition of milk from cows milked once a day (OAD) and twice a day (TAD) at different stages of lactation in real farm conditions with no control on feed. Seventy-two cows from a OAD milking herd and 181 cows from a TAD milking herd were sampled in early, mid and late lactation. Calibration equations were developed to enable the prediction of proportions of individual FAs using mid-infrared (MIR) spectroscopy. Cows milked OAD produced 25% lower daily milk yield (MY) compared to cows milked TAD. Percentages of fat and protein were 21% and 9% higher in cows milked OAD compared to cows milked TAD, respectively. The proportion of saturated fatty acids (SFA) (molecules with unbranched hydrocarbon chains and all single bonds) was significantly lower, while the proportions of de novo synthesised FAs from C8:0 to C14:0 were significantly higher, in cows milked OAD compared to cows milked TAD. OAD milking improved the energy balance of cows, which led to higher proportions of de novo synthesised FAs and lower proportions of long-chain fatty acids (16:0 and above). The proportion of SFA was significantly higher in mid lactation (ML) compared to early lactation (EL) and late lactation (LL) in cows milked OAD and TAD. In EL, the proportions of C4:0 to C12:0 FAs in cows milked OAD were significantly higher compared to the cows milked TAD due to the improved energy status of cows milked OAD. Understanding the proportions of individual FAs in cows milked OAD and TAD will enable further studies on milk fat characteristics and on butter hardness and coagulation properties of milk.

Effect of Jujube Pulp on Rennet-Induced Coagulation Properties of Milk with Different Fat Contents

Effect of Jujube Pulp on Rennet-Induced Coagulation Properties of Milk with Different Fat Contents