Detailed mineral profile of milk, whey, and cheese from cows, buffaloes, goats, ewes and dromedary camels, and efficiency of recovery of minerals in their cheese.

Milk and dairy products are important in the human diet not only for the macro nutrients, such as proteins and fats, that they provide, but also for the supply of essential micronutrients, such as minerals. Minerals are present in milk in soluble form in the aqueous phase and in colloidal form associated with the macronutrients of the milk. These 2 forms affect the nutritional functions of the minerals and their contribution to the technological properties of milk during cheese-making. The aim of the present work was to study and compare the detailed mineral profiles of dairy foods (milk, whey, and cheese) obtained from cows, buffaloes, goats, ewes and dromedary camels, and to analyze the recovery in the curd of the individual minerals according to a model cheese-making procedure applied to the milk of these 5 dairy species. The detailed mineral profile of the milk samples was obtained by inductively coupled plasma - optical emission spectroscopy (ICP - OES). We divided the 21 minerals identified in the 3 different matrices into essential macro- and micro-minerals, and environmental micro-minerals, and calculated the recovery of the individual minerals in the cheeses. The complete mineral profiles and the recoveries in the cheeses were then analyzed using a linear mixed model with Species and Food, and their interaction included as fixed effects, and Sample within Species as a random effect. The mineral profiles of each food matrix were then analyzed separately with a general linear model in which only the fixed effect of Species was included. The results showed that the species could be divided into 2 groups: those producing a more diluted milk characterized by a higher content of soluble minerals (in particular K), and those with a more concentrated milk with a higher colloidal mineral content in the skim of the milk (such as Ca and P). The recoveries of the minerals in the curd were in line with the initial content in the milk, and also highlighted the fact that the influence of the brine was not limited to the Na content but to its whole mineral makeup. These results provide valuable information for the evaluation of the nutritional and technological properties of milk, and for the uses made of the byproducts of cheese making from the milk of different species.

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.

Different protein sources in concentrate feed for dairy cows affect cheese-making properties and yield.

Soybean meal (SBM) is a commonly used protein source in feed. Yeast microbial protein could be used as a substitute for SBM, but its effect on cheese-making properties and yield is not known. Norwegian Red dairy cows (n = 48) in early or mid lactation were divided in 3 groups and fed a ration consisting of grass silage and concentrate, where the concentrates were barley based but with different additional protein sources. These were: completely barley based with no additional protein source (BAR), additional protein from SBM, or additional protein from yeast (Cyberlindnera jadinii; YEA). The SBM and YEA concentrates had a higher protein content than the barley concentrate. Four batches of cheese were made from pooled milk from each of the 3 groups of dairy cows. Milk samples were collected 5 times during the experiment. Milk from cows fed BAR concentrate showed inferior cheese-making properties (lower casein content, longer renneting time, lower content of phosphorus, and lower cheese yield) compared with SBM and YEA concentrates. Overall, SBM or YEA bulk milk had similar cheese-making properties, but when investigating individual milk samples, YEA milk showed better coagulation properties.

Cheese yield and nutrients recovery in the curd predicted by Fourier-transform spectra from individual sheep milk samples.

The objectives of this study were to explore the use of Fourier-transform infrared (FTIR) spectroscopy on individual sheep milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. For each of 121 ewes from 4 farms, a laboratory model cheese was produced, and 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water) and 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in the curd were measured. Calibration equations were developed using a Bayesian approach with 2 different scenarios: (1) a random cross-validation (80% calibration; 20% validation set), and (2) a leave-one-out validation (3 farms used as calibration, and the remaining one as validation set) to assess the accuracy of prediction of samples from external farms, not included in calibration set. The best performance was obtained for predicting the yield and recovery of total solids, justifying for the practical application of the method at sheep population and dairy industry levels. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of fresh curd and recovery of energy. Insufficient accuracies were found for the recovery of protein and fat, highlighting the complex nature of the relationships among the milk nutrients and their recovery in the curd. The leave-one-out validation procedure, as expected, showed lower prediction accuracies, as a result of the characteristics of the farming systems, which were different between calibration and validation sets. In this regard, the inclusion of information related to the farm could help to improve the prediction accuracy of these traits. Overall, a large contribution to the prediction of the cheese-making traits came from the areas known as "water" and "fingerprint" regions. These findings suggest that, according to the traits studied, the inclusion of water regions for the development of the prediction equation models is fundamental to maintain a high prediction accuracy. However, further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits, to offer reliable tools applicable along the dairy ovine chain.

Opportunities for genomic selection of cheese-making traits in Montbéliarde cows.

As part of the From'MIR project, traits related to the composition and cheese-making properties (CMP) of milk were predicted from 6.6 million mid-infrared spectra taken from 410,622 Montbéliarde cows (19,862 with genotypes). Genome-wide association studies of imputed whole-genome sequences highlighted candidate SNPs that were then added to the EuroG10K BeadChip, which is routinely used in genomic selection. In the present study, we (1) assessed the reliability of single-step genomic BLUP breeding values (ssEBVs) for cheese yields, coagulation traits, and casein and calcium content generated from test-day records of the first 3 lactations, (2) estimated realized genetic trends for these traits over the last decade, and (3) simulated different cheese-making breeding objectives and estimated the responses for CMP as well as for other traits currently selected in the Montbéliarde breed. To estimate the reliability of ssEBVs, the available data were split into 2 independent training and validation sets that respectively contained cows with the oldest and the most recent lactation data. The training set included 155,961 cows (12,850 with genotypes) and was used to predict ssEBVs of 2,125 genotyped cows in the validation set. We first tested 4 models that included either lactation (LACT) or test-day (TD) records from the first (1) or the first 3 (3) lactations, giving equal weight to all 50K SNP effects. Mean reliabilities were 61%, 62%, 63%, and 64% for the LACT1, LACT3, TD1, and TD3 models, respectively. Using the most accurate model (TD3), we then compared the reliabilities of 3 scenarios with: SNPs from the Illumina BovineSNP50 BeadChip only, equally weighted (50K); 50K SNPs plus additional candidate SNPs, equally weighted (50K+); and 50K and candidate SNPs with additional weight given to 7 to 14 candidate SNPs, depending on the trait (CAND). The 50K+ and CAND scenarios led to similar mean reliabilities (67%) and both outperformed the 50K scenario (64%), whereas the CAND scenario generated the less biased ssEBVs. To assess genetic trends, SNP effects were estimated with a single-step GBLUP based on the TD3 model and the 50K scenario applied to the whole population (2.6 million performance records from 190,261 cows and 423,348 animals in the pedigree, of which 21,874 were genotyped) and then applied to 50K genotypes of 21,171 males and 311,761 females. We detected a positive genetic trend for all CMP during the last decade, probably due to selection for an increase in milk protein and fat content in Montbéliarde cows. Finally, we compared the selection responses to 3 different breeding objectives: the current Montbéliarde total merit index (TMI) and 2 alternative scenarios that gave a weight of 70% to TMI and the remaining 30% to either milk casein content (TMI-COMP) or a combination of 3 CMP (TMI-Cheese). The TMI-Cheese scenario yielded the best responses for all the CMP analyzed, whereas values in the TMI-COMP scenario were intermediate, with a slight effect on other traits currently included in TMI. Based on these results, a program of genomic evaluation for CMP predicted from mid-infrared spectra was designed and implemented for the Montbéliarde breed.

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.

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.

Mite secretions from three traditional mite-ripened cheese types: are ripened French cheeses flavored by the mites (Acari: Astigmata)?

The opisthonotal glands of Astigmata contain monoterpenes, aromatics, aliphatics, and other volatile compounds; some of these compounds act as pheromones and have antifungal effects. This study analyzed volatile compounds secreted by mites on three traditional mite-ripened cheeses from producers (Milbenkäse from Germany, Mimolette and Artisou from France). The mites obtained from various traditional ripened French cheeses (Mimolette, Laguiole, Salers, and Cantal vieux) from stores were also investigated. The gas chromatography (GC) profiles of all their hexane extracts, except the Cantal vieux one, showed almost no differences and were identical to that of Tyrolichus casei Oudemans except for trace components. Based on the GC results, the mites of Cantal vieux were identified as Acarus siro L. For the Artisou and Cantal vieux, not studied before, the influence of the mite secretions on their characteristics was investigated by analyzing the headspace volatiles from the cheeses. According to the results, neral secreted from T. casei is the main compound responsible for the lemon-like flavor of the mite-ripened cheeses, which is, hence, due to a component of the mite secretions rather than the fermentation of the cheese itself. Moreover, the compounds secreted by the mites are not directly added to the cheese through ripening as they were not detected in the odors of the Artisou and Cantal vieux after the mites were removed. However, the consumers of the Artisou usually eat also the cheese rind, and thus, can enjoy its lemon-like flavor fully.

Do 'cheese factory-specific' mites (Acari: Astigmata) exist in the cheese-ripening cabinet?

To determine whether the mites used in the ripening process of traditional cheeses are genetically unique to cheese factories, we investigated mites from three types of traditional cheeses, that use mites in the ripening process: 'Würchwitzer Milbenkäse' from Germany and 'Mimolette' and 'Artisou' from France. In addition, traditional ripened cheeses were purchased from cheese specialty stores in France (Mimolette) and Japan ('Laguiole' from France) as well as stores in temporary markets in France ('Salers' and 'Cantal vieux') and the mites obtained from those cheeses were analyzed in this study. Partial sequences of the 28S rRNA gene (28S) were determined and used to reconstruct a phylogenetic tree. Tyrolichus casei, the dominant cheese mite species from the ripening cabinets of three traditional cheese producers and two cheese specialty stores in France and Japan, had identical partial 28S sequences. All specimens from Cantal vieux from a store in the temporary market in France had an identical sequence with Acarus siro and Acarus immobilis in the determined region of the 28S sequences. Mite individuals from Salers from a store in the temporary markets in France shared the same haplotype as Acotyledon paradoxa. For the T. casei individuals from five different localities (19 individuals in total), the nuclear loci were obtained using MIG-seq. More than several thousand genomic regions are amplified simultaneously by multiplex PCR, and targeting regions surrounded by inter-simple sequence repeats (ISSRs) in the genome were sequenced using the MiSeq system (Illumina). SNPs extracted from this genome-wide analysis showed that no genetic structure existed in the populations from any region. Among the five samples from the three regions, which were more than 500 km apart and from completely different environments, the mites had no geographic bias, but all mite individuals were genetically nearly identical. Thus, we found no evidence to support the existence of 'cheese factory-specific' T. casei mites, at least in terms of genetic analysis.

A novel electrochemical assay for chymosin determination using a label-free peptide as a substrate.

Chymosin is a predominant enzyme in rennet and is used in cheese production because of its excellent milk-clotting activity. Herein, we proposed a facile and label-free electrochemical method for determining chymosin activity based on a peptide-based enzyme substrate. The synthesized substrate peptide for chymosin was assembled onto the surface of the Au-deposited grassy carbon electrode. The current was proportional to chymosin activity, and thus chymosin activity could be determined. The detection ranges of chymosin activity were 2.5 to 25 U mL-1. The detection limit of chymosin activity was 0.8 U mL-1. The sensing platform was used to quantify chymosin activity in commercial rennet with high selectivity, excellent stability, and satisfactory reproducibility. We developed a facile, fast, and effective electrochemical assay for detecting chymosin activity, which has potential applications in cheesemaking.

Graduate Student Literature Review: Current understanding of the influence of on-farm factors on bovine raw milk and its suitability for cheesemaking.

Relationships between dairy farm practices, the composition and properties of raw milk, and the quality of the resulting cheese are complex. In this review, we assess the effect of farm factors on the quality of bovine raw milk intended for cheesemaking. The literature reports several prominent farm-related factors that are closely associated with milk quality characteristics. We describe their effects on the composition and technological properties of raw milk and on the quality of the resulting cheese. Cow breed, composite genotype, and protein polymorphism all have noticeable effects on milk coagulation, cheese yield, and cheese composition. Feed and feeding strategy, dietary supplementation, housing and milking system, and seasonality of milk production also influence the composition and properties of raw milk, and the resulting cheese. The microbiota in raw milk is influenced by on-farm factors and by the production environment, and may influence the technological properties of the milk and the sensory profile of certain cheese types. Advances in research dealing with the technological properties of raw milk have undoubtedly improved understanding of how on-farm factors affect milk quality attributes, and have refuted the concept of one milk for all purposes. The specific conditions for milk production should be considered when the milk is intended for the production of cheese with unique characteristics. The scientific identification of these conditions would improve the current understanding of the complex associations between raw milk quality and farm and management factors. Future research that considers dairy landscapes within broader perspectives and develops multidimensional approaches to control the quality of raw milk intended for long-ripening cheese production is recommended.

Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds.

This study aimed to perform a GWAS to identify genomic regions associated with milk and cheese-making traits in Assaf and Churra dairy sheep breeds; second, it aimed to identify possible positional and functional candidate genes and their interactions through post-GWAS studies. For 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra), milk samples were collected and analyzed to determine 6 milk production and composition traits and 6 traits related to milk coagulation properties and cheese yield. The genetic profiles of the ewes were obtained using a genotyping chip array that included 50,934 SNP markers. For both milk and cheese-making traits, separate single-breed GWAS were performed using GCTA software. The set of positional candidate genes identified via GWAS was subjected to guilt-by-association-based prioritization analysis with ToppGene software. Totals of 84 and 139 chromosome-wise significant associations for the 6 milk traits and the 6 cheese-making traits were identified in this study. No significant SNPs were found in common between the 2 studied breeds, possibly due to their genetic heterogeneity of the phenotypes under study. Additionally, 63 and 176 positional candidate genes were located in the genomic intervals defined as confidence regions in relation to the significant SNPs identified for the analyzed traits for Assaf and Churra breeds. After the functional prioritization analysis, 71 genes were identified as promising positional and functional candidate genes and proposed as targets of future research to identify putative causative variants in relation to the traits under examination. In addition, this multitrait study allowed us to identify variants that have a pleiotropic effect on both milk production and cheese-related traits. The incorporation of variants among the proposed functional and positional candidate genes into genomic selection strategies represent an interesting approach for achieving rapid genetic gains, specifically for those traits difficult to measure, such as cheese-making traits.

Associations between differential somatic cell count and milk yield, quality, and technological characteristics in Holstein cows.

The aim of this study was to investigate the associations between differential somatic cell count (DSCC) and milk quality and udder health traits, and for the first time, between DSCC and milk coagulation properties and cheesemaking traits in a population of 1,264 Holstein cows reared in northern Italy. Differential somatic cell count represents the combined proportions of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in the total somatic cell count (SCC), with macrophages (MAC) making up the remaining proportion. The milk traits investigated in this study were milk yield (MY), 8 traits related to milk composition and quality (fat, protein, casein, casein index, lactose, urea, pH, and milk conductivity), 9 milk coagulation traits [3 milk coagulation properties (MCP) and 6 curd firming (CF) traits], 7 cheesemaking traits, 3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits. A linear mixed model was fitted to explore the associations between SCS combined with DSCC and the aforementioned milk traits. An additional model was run, which included DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the percentage of PMN-LYM and MAC by SCC in the milk for each cow in the data set. The unfavorable association between SCS and milk quality and technological traits was confirmed. Increased DSCC was instead associated with a linear increase in MY, casein index, and lactose proportion and a linear decrease in milk fat and milk conductivity. Accordingly, DSCC was favorably associated with all MCP and CF traits (with the exception of the time needed to achieve maximum, CF), particularly with rennet coagulation time, and it always displayed linear relationships. Differential somatic cell count was also positively associated with the recovery of milk nutrients in the curd (protein, fat, and energy), which increased linearly with increasing DSCC. The PMN-LYM count was rarely associated with milk traits, even though the pattern observed confirmed the results obtained when both SCS and DSCC were included in the model. The MAC count, however, showed the opposite pattern: MY, casein index, and lactose percentage decreased and milk conductivity increased with an increasing MAC count. No significant association was found between PMN-LYM count and MCP, CF, CY, and REC traits, whereas MAC count was unfavorably associated with MCP, CF traits, some CY traits, and all REC traits. Our results showed that the combined information derived from SCS and DSCC might be useful to monitor milk quality and cheesemaking-related traits.

Effects of microbial transglutaminase levels on donkey cheese production.

Microbial transglutaminase (MTGase) is an enzyme widely used in the dairy sector to improve the functional properties of protein-based products via the formation of a network between protein molecules. The aim of this study involving cheese from the milk of donkeys was to evaluate the effects of treatment with MTGase at the concentrations of 0 (control), 5, 8 and 10 U/g milk protein on the cheese-making process parameters, as well as the physical and chemical characteristics of the resulting cheese. MTGase influenced the time of gel formation from rennet addition (P < 0.05), with a delay at the two highest concentrations, accompanied by a lower (P < 0.01) pH of cheese and the lowest (P < 0.01) loss in cheese weight at 24 h of storage. The highest gel viscosity (P < 0.01) was observed at the highest concentration of the enzyme, reaching the value of 70 mPa⋅sec after 60 min. The chemical composition and color of the cheeses were not significantly affected by the inclusion of MTGase, regardless of the enzyme concentration. These findings may be of relevance in adapting the cheese-making process and might help in the design of new dairy products from donkey's milk.

High-Resolution Mass Spectrometry and Chemometrics for the Detailed Characterization of Short Endogenous Peptides in Milk By-Products.

The process of cheese-making has long been part of human food culture and nowadays dairy represents a large sector of the food industry. Being the main byproduct of cheese-making, the revalorization of milk whey is nowadays one of the primary goals in alignment with the principles of the circular economy. In the present paper, a deep and detailed investigation of short endogenous peptides in milk and its byproducts (whole whey, skimmed whey, and whey permeate) was carried out by high-resolution mass spectrometry, with a dedicated suspect screening data acquisition and data analysis approach. A total of 79 short peptides was tentatively identified, including several sequences already known for their exerted biological activities. An unsupervised chemometric approach was then employed for highlighting the differences in the short peptide content among the four sets of samples. Whole and skimmed whey showed not merely a higher content of short bioactive peptides compared to whole milk, but also a peculiar composition of peptides that are likely generated during the process of cheese-making. The results clearly demonstrate that whey represents a valuable source of bioactive compounds and that the set-up of processes of revalorization of milk byproducts is a promising path in the obtention of high revenue-generating products from dairy industrial waste.

The effect of protective cultures on Staphylococcus aureus growth and enterotoxin production.

Staphylococcus aureus is the causative agent of staphylococcal food poisoning and is a common contaminant in milk. Despite efforts to control S. aureus, recalls and outbreaks continue to occur, highlighting the need for additional interventions. This study determined the potential for protective cultures (PC) that are commercially available to producers to control S. aureus growth in raw milk and attenuate virulence by impeding staphylococcal enterotoxin (SE) production in raw milk and laboratory medium. Cultures of Hafnia alvei and Lactococcus lactis effectively inhibited S. aureus growth in raw milk to counts ~5 log CFU/mL lower than control when cocultured following a cheesemaking time and temperature profile; two cultures of Lactobacillus plantarum inhibited growth to ~1.5 log CFU/mL less than control. Cocultures of S. aureus with Lc. lactis, H. alvei and Lb. plantarum in raw milk reduced SE levels by 24.9%, 62.4%, and 76%, respectively. Lc. lactis also decreased SE production in raw milk in the absence of PC-mediated growth inhibition. Significant reductions in SE production in the absence of pathogen growth inhibition were also achieved in laboratory medium. Together, these results demonstrate the potential for PCs to inhibit S. aureus growth and impede SE production in the absence of growth inhibition.

Characterization of milk composition, coagulation properties, and cheese-making ability of goats reared in extensive farms.

The aims of this study were to explore the variability of milk composition, coagulation properties, and cheese-making traits of the Sarda goat breed, and to investigate the effects of animal and farm factors, and the geographic area (Central-East vs. South-West) of an insular region of Italy, Sardinia. A total of 570 Sarda goats reared in 21 farms were milk-sampled during morning milking. Individual milk samples were analyzed for composition, traditional milk coagulation properties (MCP), modeled curd-firming over time parameters (CFt), and cheese-making traits (cheese yield, %CY; recovery of nutrients, %REC; daily cheese yield, dCY). Farms were classified into 2 categories based on milk energy level (MEL; high or low), defined according to the average net energy of milk daily produced by the lactating goats. Milk yield and composition were analyzed using a mixed model including the fixed effects of MEL, geographic area, days in milk, and parity, and the random effect of farm within MEL and geographic area. Data about MCP, CFt, and the cheese-making process were analyzed using the same model, with the inclusion of the effects of animal and pendulum of the lactodynamograph instrument, allowing the measure of repeatability of these traits. Results showed that animal had greater influence on coagulation and cheese-making traits compared with farm effect. Days in milk influenced milk composition, whose changes partly reflected the modifications of %CY traits. Moreover, large differences were observed between primiparous and multiparous goats: primiparous goats produced less milk of better quality (higher fat, lower somatic cell and bacterial counts) and less cheese, but with higher recovery of fat and protein in the curd, compared with multiparous goats. The repeatability was very high, for both coagulation (84.0 to 98.8%) and cheese-making traits (89.7 to 99.9%). The effect of MEL was significant for daily productions of milk and cheese, coagulation time, and recovery of protein in the curd, which were better in high-MEL farms. As regards geographic area, milk composition and percentage cheese yield were superior in the Central-East area, whereas daily milk and cheese production and MCP were better in the South-West. This result was explainable by the phenomenon of crossbreeding Sarda goats with Maltese bucks, which occurred with greater intensity in the South-West than in the Central-East area of the island. The results provided by this study could be of great interest for the goat dairy sector. Indeed, the methods described in the present study could be applicable for other farming methods, goat breeds, and geographic areas. The collection of a wide range of phenotypes at individual animal level is fundamental for the characterization of local populations and can be used to guarantee breed conservation and the persistence of traditional farming systems, and to increase farmers' profit.

Milk coagulation traits and cheese yields of purebred Holsteins and 4 generations of 3-breed rotational crossbred cows from Viking Red, Montbéliarde, and Holstein bulls.

Crossbreeding is a strategy to counter the declining fertility, resilience, and longevity of purebred Holstein (Ho) cows. However, little is known of the effects of long-term systematic rotational crossbreeding on milk technological properties and cheese yield (CY). In this study, we compared the individual milk composition, milk coagulation properties (MCP), and CY of 468 purebred Ho and 648 crossbred (CR) cows obtained from two 3-breed rotational crossbreeding systems using Viking Red (VR), Montbéliarde (Mo), and Ho sires over 4 generations. Individual milk samples were collected once from 1,116 primiparous and multiparous cows kept in 2 dairy herds, raised for the production of Grana Padano (high milk yield, total mixed ration based on corn silage) and Parmigiano Reggiano (moderate milk yield, only dry feeds) cheeses. In both herds, a 3-breed rotational mating system was used in which Ho cows were first inseminated with VR, whereas Mo and Ho semen was used in the subsequent generations. In one herd, the sequence Mo-VR-Ho was also used. Individual milk samples were analyzed for milk composition, single-point MCP, and parameters for modeling curd firming over time, whereas CY and milk nutrient recovery in the curd were assessed through a laboratory cheese-making procedure. Compared with Ho, CR cows produced 5.8% less milk, which had comparable fat but greater protein and casein contents and lower lactose contents and somatic cell scores. Milk from CR cows tended to reach a curd firmness of 20 mm more quickly and exhibited greater curd firmness at 45 and 60 min from rennet addition. Holstein and CR cows yielded milk with similar CY and recovery in the curd traits. The milk fat content, somatic cell scores, curd firmness traits, and CY of CR cows relative to the Ho cows differed in the 2 herds, and the favorable effects on the CR cows were more evident in the herd with the greatest milk yield and the worst MCP traits. Crossbred cows of the 4 generations performed similarly, with the exception of the better MCP of the milk from first-generation CR cows. The 2 rotational systems using different sire-breed sequences also performed similarly. In summary, both rotational crossbreeding programs exhibited some advantage over the Ho purebred breeding system in terms of milk composition and MCP but not CY. Future research is needed to investigate the interactions between crossbreeding schemes and dairy systems.

Mid-infrared spectrometry prediction of the cheese-making properties of raw Montbéliarde milks from herds and cheese dairy vats used for the production of Protected Designation of Origin and Protected Geographical Indication cheeses in Franche-Comté.

Franche-Comté is the primary producing region of Protected Designation of Origin cheeses in France. Normally, mid-infrared (MIR) prediction models for cheese-making property (CMP) traits are developed using individual bovine milks. However, considering the requests of all actors in the dairy sector, the present study aimed to assess the feasibility of MIR spectroscopy to develop CMP equations of Montbéliarde herd and dairy vat milks. For this purpose, 22 CMP traits were analyzed on samples collected in 2016 (half in February-March and half in May-June) from 100 commercial herds and 70 dairy vats (55 cheese dairies) located in Franche-Comté. These characteristics included 11 rennet coagulation traits and 8 lactic acidification traits measured in either soft cheese or pressed cooked cheese conditions and 3 laboratory curd yields. Models of MIR prediction for each of the 22 CMP traits were built using partial least squares regression with external validation by dividing the data set into calibration (70%) and validation (30%) sets. We confirmed that the variability of milk traits depends largely on the production scale and is higher for individual milk than for herd milk and even higher for vat milk. The best prediction models were obtained in herd milk samples for curd yields expressed in dry matter or fresh, with a coefficient of determination (R2) in external validation of 0.78 and 0.77, respectively. As with individual milk, these traits are closely related to the gross composition of the milk and therefore easier to predict by MIR spectroscopy. However, these curd yield traits were poorly predicted (R2 = 0.58) in vat milk samples due to their lower variability. In herd milk samples, prediction models of other CMP traits were poorly accurate except for the ratio of the time to obtain a standard firmness to the rennet coagulation time in soft cheese or pressed cooked cheese conditions, which showed R2 > 0.66 in external validation. Such trait is important in qualifying the behavior of milk during cheese production. Prediction models of other CMP traits for either herd or vat milk samples had poor accuracy, and further work is needed to improve their performance.

Effect of feeding hay vs. silages of various types to dairy cows on feed intake, milk composition and coagulation properties.

This research paper addresses the hypotheses (1) that milk produced from hay-fed cows differs from that of silage-fed cows and (2) that silage type has an important impact, too. Four diets differing in forage type but with equal estimated milk production potential and a forage:concentrate ratio of 0.85 : 0.15 were compared regarding their effect on feed intake, milk yield and milk properties. The forages tested were hay, grass silage, conventional short-chopped and long-chopped maize silage subjected to a novel processing technology (Shredlage®). Twenty-four dairy cows were fed two of the four diets in two consecutive runs in an incomplete (4 × 2) Latin-square design (n = 12 per diet). Each experimental period lasted 22 d, with 12 d of adaptation and 10 d of sampling. During sampling, feed intake and milk yield were recorded daily, milk composition and coagulation properties were determined four times. The composition of the diet ingredients was analysed weekly. Data were analysed with a mixed model considering feed, period and their interaction as fixed effects. Stage of lactation, milk yield and milk composition from the pre-experimental period were used as covariates in the model. Dry matter intake was lower with the long-chopped processed maize silage compared to the other three groups. There were some diet differences in intakes of net energy for lactation and absorbable protein in the duodenum, but this did not result in changes in milk yield. The milk fat content was higher with the grassland-based diets compared to the maize silage diets. No treatment effect on milk acidity and rennet coagulation properties was observed. In conclusion, there were no indications for specific physico-chemical properties of milk from a hay-based diet, and maize processing technology was not of large effect either. Future investigations should focus on sensory differentiation of the milk produced with different forages.