The mineral profile affects the coagulation pattern and cheese-making efficiency of bovine milk

Natural variations in milk minerals, their relationships, and their associations with the coagulation process and cheese-making traits present an opportunity for the differentiation of milk destined for high-quality natural products, such as traditional specialties or Protected Designation of Origin (PDO) cheeses. The aim of this study was to quantify the effects of the native contents of Ca, P, Na, K, and Mg on 18 traits describing traditional milk coagulation properties (MCP), curd firming over time (CF_t) equation parameters, cheese yield (CY) measures, and nutrient recoveries in the curd (REC) using models that either included or omitted the simultaneous effects of milk fat and casein contents. The results showed that, by including milk fat and casein and the minerals in the statistical model, we were able to determine the specific effects of each mineral on coagulation and cheese-making efficiency. In general, about two-thirds of the apparent effects of the minerals on MCP and the CF_t equation parameters are actually mediated by their association with milk composition, especially casein content, whereas only one-third of the effects are direct and independent of milk composition. In the case of cheese-making traits, the effects of the minerals were mediated only negligibly by their association with milk composition. High Ca content had a positive effect on the coagulation pattern and cheese-making traits, favoring water retention in the curd in particular. Phosphorus positively affected the cheese-making traits in that it was associated with an increase in CY in terms of curd solids, and in all the nutrient recovery traits. However, a very high P content in milk was associated with lower fat recovery in the curd. The variation in the Na content in milk only mildly affected coagulation, whereas with regard to cheese-making, protein recovery was negatively associated with high concentrations of this mineral. Potassium seemed not to be actively involved in coagulation and the cheese-making process. Magnesium content tended to slow coagulation and reduce CY measures. Further studies on the relationships of minerals with casein and protein fractions could deepen our knowledge of the role of all minerals in coagulation and the cheese-making process.     

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.     

Effect of protein composition on the cheese-making properties of milk from individual dairy cows

The objective of this study was to evaluate the effect of variations in milk protein composition on milk clotting properties and cheese yield. Milk was collected from 134 dairy cows of Swedish Red and White, Swedish Holstein, and Danish Holstein-Friesian breed at 3 sampling occasions. Concentrations of α_S1-, β-, and κ-casein (CN), α-lactalbumin, and β-lactoglobulin (LG) A and B were determined by reversed phase liquid chromatography. Cows of Swedish breeds were genotyped for genetic variants of β- and κ-CN. Model cheeses were produced from individual skimmed milk samples and the milk clotting properties were evaluated. More than 30% of the samples were poorly coagulating or noncoagulating, resulting in weak or no coagulum, respectively. Poorly and noncoagulating samples were associated with a low concentration of κ-CN and a low proportion of κ-CN in relation to total CN analyzed. Furthermore, the κ-CN concentration was higher in milk from cows with the AB genotype than the AA genotype of κ-CN. The concentrations of α_S1-, β-, and κ-CN and of β-LG B were found to be significant for the cheese yield, expressed as grams of cheese per one hundred grams of milk. The ratio of CN to total protein analyzed and the β-LG B concentration positively affected cheese yield, expressed as grams of dry cheese solids per one hundred grams of milk protein, whereas β-LG A had a negative effect. Cheese-making properties could be improved by selecting milk with high concentrations of α_S1-, β-, and κ-CN, with high κ-CN in relation to total CN and milk that contains β-LG B.     

Factors affecting variation of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process

Cheese yield (CY) is the most important technological trait of milk, because cheese-making uses a very high proportion of the milk produced worldwide. Few studies have been carried out at the level of individual milk-producing animals due to a scarcity of appropriate procedures for model-cheese production, the complexity of cheese-making, and the frequent use of the fat and protein (or casein) contents of milk as a proxy for cheese yield. Here, we report a high-throughput cheese manufacturing process that mimics all phases of cheese-making, uses 1.5-L samples of milk from individual animals, and allows the simultaneous processing of 15 samples per run. Milk samples were heated (35°C for 40 min), inoculated with starter culture (90 min), mixed with rennet (51.2 international milk-clotting units/L of milk), and recorded for gelation time. Curds were cut twice (10 and 15 min after gelation), separated from the whey, drained (for 30 min), pressed (3 times, 20 min each, with the wheel turned each time), salted in brine (for 60 min), weighed, and sampled. Whey was collected, weighed, and sampled. Milk, curd, and whey samples were analyzed for pH, total solids, fat content, and protein content, and energy content was estimated. Three measures of percentage cheese yield (%CY) were calculated: %CY_CURD, %CY_SOLIDS, and %CY_WATER, representing the ratios between the weight of fresh curd, the total solids of the curd, and the water content of the curd, respectively, and the weight of the milk processed. In addition, 3 measures of daily cheese yield (dCY, kg/d) were defined, considering the daily milk yield. Three measures of nutrient recovery (REC) were computed: REC_FAT, REC_PROTEIN, and REC_SOLIDS, which represented the ratio between the weights of the fat, protein, and total solids in the curd, respectively, and the corresponding components in the milk. Energy recovery, REC_ENERGY, represented the energy content of the cheese compared with that in the milk. This procedure was used to process individual milk samples obtained from 1,167 Brown Swiss cows reared in 85 herds of the province of Trento (Italy). The assessed traits exhibited almost normal distributions, with the exception of REC_FAT. The average values (± SD) were as follows: %CY_CURD = 14.97 ± 1.86, %CY_SOLIDS = 7.18 ± 0.92, %CY_WATER = 7.77 ± 1.27, dCY_CURD = 3.63 ± 1.17, dCY_SOLIDS = 1.74 ± 0.57, dCY_WATER = 1.88 ± 0.63, REC_FAT = 89.79 ± 3.55, REC_PROTEIN = 78.08 ± 2.43, REC_SOLIDS = 51.88 ± 3.52, and REC_ENERGY = 67.19 ± 3.29. All traits were highly influenced by herd-test-date and days in milk of the cow, moderately influenced by parity, and weakly influenced by the utilized vat. Both %CY_CURD and dCY_CURD depended not only on the fat and protein (casein) contents of the milk, but also on their proportions retained in the curd; the water trapped in curd presented an higher variability than that of %CY_SOLIDS. All REC traits were variable and affected by days in milk and parity of the cows. The described model cheese-making procedure and the results obtained provided new insight into the phenotypic variation of cheese yield and recovery traits at the individual level.     

Effects of ultra-high pressure homogenization on the cheese-making properties of milk

The effects of single- or 2-stage ultra-high pressure homogenization (UHPH; 100 to 330 MPa) at an inlet temperature of 30°C on the cheese-making properties of bovine milk were investigated. Effects were compared with those from raw, heat-pasteurized (72°C for 15 s), and conventional homogenized-pasteurized (15 + 3 MPa, 72°C for 15 s) treatments. Rennet coagulation time, rate of curd firming, curd firmness, wet yield, and moisture content of curds were assessed. Results of particle size and distribution of milk, whey composition, and gel microstructure observed by confocal laser scanning microscopy were analyzed to understand the effect of UHPH. Single-stage UHPH at 200 and 300 MPa enhanced rennet coagulation properties. However, these properties were negatively affected by the use of the UHPH secondary stage. Increasing the pressure led to higher yields and moisture content of curds. The improvement in the cheese-making properties of milk by UHPH could be explained by changes to the protein-fat structures due to the combined effect of heat and homogenization.     

Milk protein fractions strongly affect the patterns of coagulation,curd firming,and syneresis

The aim of this study was to assess the role of milk protein fractions in the coagulation, curd firming, and syneresis of bovine milk. Analyses were performed on 1,271 individual milk samples from Brown Swiss cows reared in 85 herds classified into 4 types of farming systems, from the very traditional (tied cows, feed manually distributed, summer highland pasture) to the most modern (loose cows, use of total mixed rations with or without silage). Fractions α_S1-casein (CN), α_S2-CN, β-CN, κ-CN, β-lactoglobulin (LG), and α-lactalbumin (LA) and genotypes at CSN2, CSN3, and BLG were obtained by reversed-phase HPLC. The following milk coagulation properties were measured with a lactodynamograph, with the testing time extended to 60 min: rennet coagulation time (RCT, min), curd firming time (min), and curd firmness at 30 and 45 min (mm). All the curd firmness measures recorded over time (total of 240 observations/sample) were used in a 4-parameter nonlinear model to obtain parameters of coagulation, curd firming, and syneresis: RCT estimated from the equation (min), asymptotic potential curd firmness (mm), the curd firming and syneresis instant rate constants (%/min), and the maximum curd firmness value (CF_max, mm) and the time taken to reach it (min). All the aforementioned traits were analyzed with 2 linear mixed models, which tested the effects of the protein fractions expressed in different ways: in the first, quantitative model, each protein fraction was expressed as content in milk; in the second, qualitative model, each protein fraction was expressed as a percentage of total casein content. Besides proteins, additional nuisance parameters were herd (included as a random effect), daily milk production (only for the quantitative model), casein content (only for the qualitative model), dairy system, parity, days in milk, the pendulum of the lactodynamograph, and the CSN2, CSN3, and BLG genotypes. Both α_S1-CN and β-CN showed a clear and favorable effect on CF_max, where the former effect was almost double the latter. Milk coagulation ability was favorably affected by κ-CN, which reduced both the RCT and RCT estimated from the equation, increased the curd firming and syneresis instant rate constants, and allowed a higher CF_max to be reached. In contrast, α_S2-CN delayed gelation time and β-LG worsened curd firming, both resulting in a low CF_max. The results of this study suggest that modification of the relative contents of specific protein fractions can have an enormous effect on the technological behavior of bovine milk.     

A meta-analysis on the relationship between intake of nutrients and body weight with milk volume and milk protein yield in dairy cows

Previously observed strong relationships between dry matter (DM) intake and milk yield in dairy cows were the basis for this meta-analysis aimed to determine the influence of intake of specific dietary nutrients on milk yield and milk protein yield in Holstein dairy cows. Diets (563) from feeding trials published in the Journal of Dairy Science were evaluated for nutrient composition using 2 diet evaluation programs. Intake of nutrients was estimated based on DM intake and program-derived diet composition. Data were analyzed with and without the effect of stage of lactation. Models based on intake of nutrients improved prediction of milk yield and milk protein yield compared with DM intake alone. Intake of net energy of lactation was the dominant variable in milk yield prediction models derived from both diet evaluation models. Milk protein yield models also improved prediction over the DM intake model. These models were dominated by ruminally undegradable protein intake and included a number of energy-related intake variables. In most models, incorporating stage of lactation improved the model fit.     

A meta-analysis of the effects of dietary protein concentration and degradability on milk protein yield and milk N efficiency in dairy cows

Data sets from North American (NA, 739 diets) and North European (NE, 998 diets) feeding trials with dairy cows were evaluated to investigate the effects of dietary crude protein (CP) intake and ruminal degradability on milk protein yield (MPY) and efficiency of N utilization for milk protein synthesis (MNE; milk N ÷ N intake) in dairy cows. The NA diets were based on corn silage, alfalfa silage and hay, corn and barley grains, and soybean meal. The NE diets were based on grass silage, barley and oats grains, and soybean and rapeseed meals. Diets were evaluated for rumen-degradable and undegradable protein (RDP and RUP, respectively) concentrations according to NRC (2001). A mixed model regression analysis with random study effect was used to evaluate relationships between dietary CP concentration and degradability and MPY and MNE. In both data sets, CP intake alone predicted MPY reasonably well. Addition of CP degradability to the models slightly improved prediction. Models based on metabolizable protein (MP) intake predicted MPY better than the CP or the CP-CP degradability models. The best prediction models were based on total digestible nutrients (TDN) and CP intakes. Similar to the MPY models, inclusion of CP degradability in the CP (intake or concentration) models only slightly improved prediction of MNE in both data sets. Concentration of dietary CP was a better predictor of MNE than CP intake. Compared with the CP models, prediction of MNE was improved by inclusion of TDN intake or concentration. Milk yield alone was a poor predictor of MNE. The models developed from one data set were validated using the other data set. The MNE models based on TDN and CP intake performed well as indicated by small mean and slope bias. This meta-analysis demonstrated that CP concentration is the most important dietary factor influencing MNE. Ruminal CP degradability as predicted by NRC (2001) does not appear to be a significant factor in predicting MPY or MNE. Data also indicated that increasing milk yield will increase MNE provided that dietary CP concentration is not increased, but the effect is considerably smaller than the effect of reducing CP intake.     

Effects of protein and fat levels in milk on Cheddar cheese yield

Over a 14-month period, bulk tank milk was collected twice a week and was adjusted with cream and skim milk powder to provide six levels each of fat and protein varying from 3·0 to 4·0%. Milk samples were analyzed for total solids, fat, protein, casein, lactose and somatic cell count and were used for laboratory-scale cheesemaking. Data obtained from the milk input and the cheese output were used to determine actual, moisture adjusted, theoretical yield, and efficiency of yield. Least squares analyses of data indicated that higher cheese yields were obtained from higher fat and protein contents in milk. Higher yield efficiency was associated with higher ratios of protein to fat and casein to fat. Regression analysis indicated that a percentage increase in fat content in milk resulted in an increase of 1·23–1·37% in moisture adjusted yield in the different protein levels. For a similar increase of protein in milk, there were 1·80–2·04% increase in moisture adjusted yields in different fat levels.     

Associations between pathogen-specific cases of subclinical mastitis and milk yield,quality, protein composition,and cheese-making traits in dairy cows

The aim of this study was to investigate associations between pathogen-specific cases of subclinical mastitis and milk yield, quality, protein composition, and cheese-making traits. Forty-one multibreed herds were selected for the study, and composite milk samples were collected from 1,508 cows belonging to 3 specialized dairy breeds (Holstein Friesian, Brown Swiss, and Jersey) and 3 dual-purpose breeds of Alpine origin (Simmental, Rendena, and Grey Alpine). Milk composition [i.e., fat, protein, casein, lactose, pH, urea, and somatic cell count (SCC)] was analyzed, and separation of protein fractions was performed by reversed-phase high performance liquid chromatography. Eleven coagulation traits were measured: 5 traditional milk coagulation properties [time from rennet addition to milk gelation (RCT, min), curd-firming rate as the time to a curd firmness (CF) of 20 mm (k₂₀, min), and CF at 30, 45, and 60 min from rennet addition (a₃₀, a₄₅, and a₆₀, mm)], and 6 new curd firming and syneresis traits [potential asymptotical CF at an infinite time (CF_P, mm), curd-firming instant rate constant (k_CF, % × min^?1), curd syneresis instant rate constant (k_SR, % × min^?1), modeled RCT (RCT_eq, min), maximum CF value (CF_max, mm), and time at CF_max (t_max, min)]. We also measured 3 cheese yield traits, expressing the weights of total fresh curd (%CY_CURD), dry matter (%CY_SOLIDS), and water (%CY_WATER) in the curd as percentages of the weight of the processed milk, and 4 nutrient recovery traits (REC_PROTEIN, REC_FAT, REC_SOLIDS, and REC_ENERGY), representing the percentage ratio between each nutrient in the curd and milk. Milk samples with SCC > 100,000 cells/mL were subjected to bacteriological examination. All samples were divided into 7 clusters of udder health (UH) status: healthy (cows with milk SCC < 100,000 cells/mL and uncultured); culture-negative samples with low, medium, or high SCC; and culture-positive samples divided into contagious, environmental, and opportunistic intramammary infection (IMI). Data were analyzed using a linear mixed model. Significant variations in the casein to protein ratio and lactose content were observed in all culture-positive samples and in culture-negative samples with medium to high SCC compared to normal milk. No differences were observed among contagious, environmental, and opportunistic pathogens, suggesting an effect of inflammation rather than infection. The greatest impairment in milk quantity and composition, clotting ability, and cheese production was observed in the 2 UH status groups with the highest milk SCC (i.e., contagious IMI and culture-negative samples with high SCC), revealing a discrepancy between the bacteriological results and inflammatory status, and thus confirming the importance of SCC as an indicator of udder health and milk quality.     

水牛乳和荷斯坦牛乳切达干酪成熟期间质量特性对比

为了了解水牛乳和荷斯坦牛乳切达干酪成熟期间的质量特性,采用了理化测定、质构测定和感官评定相结合的方法对其进行研究.结果表明:在成熟期90 d内,不同成熟温度下,两种干酪的蛋白质质量分数减少约为1％～4％,脂肪质量分数减少约为4％～10％,且成熟温度越高,质量分数下降越大,水牛乳干酪中蛋白和脂肪质量分数均高于荷斯坦牛乳干酪且两种成分的下降量也是水牛乳干酪更高;pH值呈现先下降后上升趋势.在90 d成熟期间,两种干酪的硬度先上升后下降,凝聚性上升,弹性下降.经感官评价,两种干酪在10℃成熟60天时风味最佳,水牛乳切达干酪略受偏爱.可用于水牛乳切达干酪成熟期内质量控制.     

β-Casein A1 and A2: Effects of polymorphism on the cheese-making process

Of late, “A2 milk” has gained prominence in the dairy sector due to its potential implications in human health. Consequently, the frequency of A2 homozygous animals has considerably increased in many countries. To elucidate the potential implications that beta casein (β-CN) A1 and A2 may have on cheese-making traits, it is fundamental to investigate the relationships between the genetic polymorphisms and cheese-making traits at the dairy plant level. Thus, the aim of the present study was to evaluate the relevance of the β-CN A1/A2 polymorphism on detailed protein profile and cheese-making process in bulk milk. Based on the β-CN genotype of individual cows, 5 milk pools diverging for presence of the 2 β-CN variants were obtained: (1) 100% A1; (2) 75% A1 and 25% A2; (3) 50% A1 and 50% A2; (4) 25% A1 and 75% A2; and (5) 100% A2. For each cheese-making day (n = 6), 25 L of milk (divided into 5 pools, 5 L each) were processed, for a total of 30 cheese-making processes. Cheese yield, curd nutrient recovery, whey composition, and cheese composition were assessed. For every cheese-making process, detailed milk protein fractions were determined through reversed-phase HPLC. Data were analyzed by fitting a mixed model, which included the fixed effects of the 5 different pools, the protein and fat content as a covariate, and the random effect of the cheese-making sessions. Results showed that the percentage of κ-CN significantly decreased up to 2% when the proportion of β-CN A2 in the pool was ≥25%. An increase in the relative content of β-CN A2 (≥50% of total milk processed) was also associated with a significantly lower cheese yield both 1 and 48 h after cheese production, whereas no effects were observed after 7 d of ripening. Concordantly, recovery of nutrients reflected a more efficient process when the inclusion of β-CN A2 was ≤75%. Finally, no differences in the final cheese composition obtained by the different β-CN pools were observed.     

Effect of goat milk composition on cheesemaking traits and daily cheese production

Cheese yield is strongly influenced by the composition of milk, especially fat and protein contents, and by the efficiency of the recovery of each milk component in the curd. The real effect of milk composition on cheesemaking ability of goat milk is still unknown. The aims of this study were to quantify the effects of milk composition; namely, fat, protein, and casein contents, on milk nutrient recovery in the curd, cheese yield, and average daily yield. Individual milk samples were collected from 560 goats of 6 different breeds. Each sample was analyzed in duplicate using the 9-laboratory milk cheesemaking assessment, a laboratory method that mimicked cheesemaking procedures, with milk heating, rennet addition, coagulation, curd cutting, and draining. Data were submitted to statistical analysis; results showed that the increase of milk fat content was associated with a large improvement of cheese yield because of the higher recovery of all milk nutrients in the curd, and thus a higher individual daily cheese yield. The increase of milk protein content affected the recovery of fat, total solids, and energy in the curd. Casein number, calculated as casein-to-protein ratio, did not affect protein recovery but strongly influenced the recovery of fat, showing a curvilinear pattern and the most favorable data for the intermediate values of casein number. In conclusion, increased fat and protein contents in the milk had an effect on cheese yield not only for the greater quantity of nutrients available but also for the improved efficiency of the recovery in the curd of all nutrients. These results are useful to improve knowledge on cheesemaking processes in the caprine dairy industry.     

Effect of milk protein standardization using different methods on the composition and yields of Cheddar cheese

Two sets of Cheddar cheese were made in which the milk protein level (%, wt/wt) was increased from 3.3 (Control A, CA) to 3.6 (set A) or from 3.3 (control B, CB) to 4.0 (set B) by the addition of phosphocasein (PC), milk protein concentrate (MPC), or freshly prepared ultrafiltered milk retentate (UFR). The cheeses were denoted CA, PCA, MPCA, and UFRA from set A, and CB, PCB, MPCB, and UFRB, from set B, respectively. The level of cheese moisture decreased significantly on increasing milk protein level from 3.3 to 3.6 or 4.0% (wt/wt), but was not affected significantly by the method of protein standardization. The percentage milk fat recovered to cheese increased significantly on increasing the level of milk protein from 3.3 to 3.6% (wt/wt) with PC, and from 3.3 to 4.0% (wt/wt) with PC, MPC, and UFR. Increasing milk protein level from 3.3 to 4.0% (wt/wt) with PC significantly increased the percentage of milk protein recovered to cheese. Actual cheese yield increased significantly with milk protein level. The yield of cheese per 100 kg of milk normalized to reference levels of fat (3.4%, wt/wt) and casein (2.53%, wt/wt) indicated no significant effects of protein content or standardization treatment on yield. However, the moisture-adjusted yield per 100 kg of milk with reference levels of fat and casein increased significantly on increasing the protein content from 3.3 to 3.6% (wt/wt) with MPC and from 3.3 to 4.0% (wt/wt) with PC, MPC, and UFR.     

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 (CF_t)], 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), CF_t (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 CF_t, 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 CF_t, 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 use of Fourier-transform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from unprocessed bovine milk samples

Cheese yield is an important technological trait in the dairy industry in many countries. The aim of this study was to evaluate the effectiveness of Fourier-transform infrared (FTIR) spectral analysis of fresh unprocessed milk samples for predicting cheese yield and nutrient recovery traits. A total of 1,264 model cheeses were obtained from 1,500-mL milk samples collected from individual Brown Swiss cows. Individual measurements of 7 new cheese yield-related traits were obtained from the laboratory cheese-making procedure, including the fresh cheese yield, total solid cheese yield, and the water retained in curd, all as a percentage of the processed milk, and nutrient recovery (fat, protein, total solids, and energy) in the curd as a percentage of the same nutrient contained in the milk. All individual milk samples were analyzed using a MilkoScan FT6000 over the spectral range from 5,000 to 900 wavenumber × cm⁻¹. Two spectral acquisitions were carried out for each sample and the results were averaged before data analysis. Different chemometric models were fitted and compared with the aim of improving the accuracy of the calibration equations for predicting these traits. The most accurate predictions were obtained for total solid cheese yield and fresh cheese yield, which exhibited coefficients of determination between the predicted and measured values in cross-validation (1-VR) of 0.95 and 0.83, respectively. A less favorable result was obtained for water retained in curd (1-VR = 0.65). Promising results were obtained for recovered protein (1-VR = 0.81), total solids (1-VR = 0.86), and energy (1-VR = 0.76), whereas recovered fat exhibited a low accuracy (1-VR = 0.41). As FTIR spectroscopy is a rapid, cheap, high-throughput technique that is already used to collect standard milk recording data, these FTIR calibrations for cheese yield and nutrient recovery highlight additional potential applications of the technique in the dairy industry, especially for monitoring cheese-making processes and milk payment systems. In addition, the prediction models can be used to provide breeding organizations with information on new phenotypes for cheese yield and milk nutrient recovery, potentially allowing these traits to be enhanced through selection.     

Use of dry milk protein concentrate in pizza cheese manufactured by culture or direct acidification

Milk protein concentrate (MPC) contains high concentrations of casein and calcium and low concentrations of lactose. Enrichment of cheese milk with MPC should, therefore, enhance yields and improve quality. The objectives of this study were: 1) to compare pizza cheese made by culture acidification using standardized whole milk (WM) plus skim milk (SM) versus WM plus MPC; and 2) compare cheese made using WM + MPC by culture acidification to that made by direct acidification. The experimental design is as follows: vat 1 = WM + SM + culture (commercial thermophilic lactic acid bacteria), vat 2 = WM + MPC + culture, and vat 3 = WM + MPC + direct acid (2% citric acid). Each cheese milk was standardized to a protein-to-fat ratio of approximately 1.4. The experiment was repeated three times. Yield and composition of cheeses were determined by standard methods, whereas the proteolysis was assessed by urea polyacrylamide gel electrophoresis (PAGE) and water-soluble N contents. Meltability of the cheeses was determined during 1 mo of storage, in addition to pizza making. The addition of MPC improved the yields from 10.34 +/- 0.57% in vat 1 cheese to 14.50 +/- 0.84% and 16.65 +/- 2.23%, respectively, in vats 2 and 3 and cheeses. The percentage of fat and protein recoveries showed insignificant differences between the treatments, but TS recoveries were in the order, vat 2 > vat 3 > vat 1. Most of the compositional parameters were significantly affected by the different treatments. Vat 2 cheese had the highest calcium and lowest lactose contencentrations. Vat 3 cheese had the best meltability. Vat 1 cheese initially had better meltability than vat 2 cheese; however, the difference became insignificant after 28 d of storage at 4 degrees C. Vat 3 cheese had the softest texture and produced large-sized blisters when baked on pizza. The lowest and highest levels of proteolysis were found in vats 2 and 3 cheeses, respectively. The study demonstrates the use of MPC in pizza cheese manufacture with improved yield both by culture acidification as well as direct acidification.     

Genetic parameters of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process

Cheese yield (CY) is an important technological trait in the dairy industry, and the objective of this study was to estimate the genetic parameters of cheese yield in a dairy cattle population using an individual model-cheese production procedure. A total of 1,167 Brown Swiss cows belonging to 85 herds were sampled once (a maximum of 15 cows were sampled per herd on a single test day, 1 or 2 herds per week). From each cow, 1,500 mL of milk was processed according to the following steps: milk sampling and heating, culture addition, rennet addition, gelation-time recording, curd cutting, whey draining and sampling, wheel formation, pressing, salting in brine, weighing, and cheese sampling. The compositions of individual milk, whey, and curd samples were determined. Three measures of percentage cheese yield (%CY) were calculated: %CY_CURD, %CY_SOLIDS, and %CY_WATER, which represented the ratios between the weight of fresh curd, the total solids of the curd, and the water content of the curd, respectively, and the weight of the milk processed. In addition, 3 measures of daily cheese yield (dCY, kg/d) were defined, considering the daily milk yield. Three measures of nutrient recovery (REC) were computed: REC_FAT, REC_PROTEIN, and REC_SOLIDS, which represented the ratio between the weights of the fat, protein, and total solids in the curd, respectively, and the corresponding nutrient in the milk. Energy recovery, REC_ENERGY, represented the energy content of the cheese versus that in the milk. For statistical analysis, a Bayesian animal model was implemented via Gibbs sampling. The effects of parity (1 to ≥4), days in milk (6 classes), and laboratory vat (15 vats) were assigned flat priors; those of herd-test-date, animal, and residual were given Gaussian prior distributions. Intra-herd heritability estimates of %CY_CURD, _%CY_SOLIDS, and %CY_WATER ranged from 0.224 to 0.267; these were larger than the estimates obtained for milk yield (0.182) and milk fat content (0.122), and similar to that for protein content (0.275). Daily cheese yields showed heritability estimates similar to those of daily milk yield. The trait %CY_WATER showed a highly positive genetic correlation with %CY_SOLIDS (0.87), whereas their phenotypic correlation was moderate (0.37), and the fat and protein contents of milk showed high genetic correlations with %CY traits. The heritability estimates of REC_PROTEIN and REC_FAT were larger (0.490 and 0.208, respectively) than those obtained for the protein and fat contents of milk, and the genetic relationships between REC_PROTEIN and REC_FAT with milk protein and fat content were low or moderate; REC_PROTEIN and REC_FAT were moderately correlated with the %CY traits and highly correlated with REC_SOLIDS and REC_ENERGY. Both REC_SOLIDS and REC_ENERGY were heritable (0.274 and 0.232), and showed high correlations with each other (0.96) and with the %CY traits (0.83 to 0.97). Together, these findings demonstrate the existence of economically important, genetically determined variability in cheese yield that does not depend solely upon the fat and protein contents of milk, but also relies on the ability of the coagulum to retain the highest possible proportions of the available protein, fat, and water. Exploitation of this interesting genetic variation does not seem to be feasible through direct measurement of the phenotype in cows at the population level. Instead, further research is warranted to examine possible means for indirect prediction, such as through assessing the mid-infrared spectra of milk samples.     

Use of milk protein concentrate to standardize milk composition in Italian citric Mozzarella cheese making

To better exploit manufacturing facilities and standardize cheese quality, milk composition could be standardized by fortifying its protein content with a milk protein concentrate (MPC) addition so avoiding partially skimming the milk. With this aim Mozzarella cheese was obtained adding citric acid into milk standardized at 4% protein and a fat to protein ratio of 1.0. Protein fortification was obtained adding MPC produced by ultrafiltration. Milk, whey, curd, cheese and stretching water were weighed and analysed for total solid, fat and protein content, to measure component recovery and yield. Yield increase (from 13.8% to 16.7%) was due to the higher recovery of the milk total solids and proteins in MPC cheese (48.2 and 78.3%, respectively) and to the slightly higher cheese moisture, obtained with a little modification of the cheese technology when adding MPC. Milk fat in cheese was lower than that reported in literature. Hot water stretching of the curd resulted in very low losses (1%) of protein and considerable losses (14%) of fat for both control and MPC cheeses. The likely reasons of this low recovery are discussed and it can be supposed that a further cheese yield increase is possible by changing the curd stretching procedures.     

Composition,coagulation properties,and predicted cheesemaking traits of bulk goat milk from different farming systems,breeds, and stages of production

At the global level, the quantity of goat milk produced and its gross production value have increased considerably over the last 2 decades. Although many scientific papers on this topic have been published, few studies have been carried out on bulk goat milk samples. The aim of the present study was to investigate in the field the effects of farming system, breed type, individual flock, and stage of production on the composition, coagulation properties (MCP), curd firming over time parameters (CF_t), predicted cheese yield (CY%), and nutrient recovery traits (REC) of 432 bulk milk samples from 161 commercial goat farms in Sardinia, Italy. We found that the variance due to individual flock was of the same order as the residual variance for almost all composition and cheesemaking traits. With regard to the fixed effects, the effect of farming system on bulk milk variability was not highly significant for the majority of traits (it was lower than individual flock), whereas the effects of breed type and stage of production were much higher. More specifically, the intensive farms produced milk with the best concentrations of almost all constituents, whereas extensive farms exhibited faster rennet coagulation times, a slower rate of curd firming, lower potential curd firmness, and lower percentages of fat and energy recoveries in the fresh curd. Farms rearing the local breed, Sarda, alone or together with the Maltese breed, produced milk with the best concentrations of fat and protein, superior curd firmness, and better predicted percentage of fresh curd (CY_CURD) and recovery traits. The results show the potential of both types of breed, either for their quantitative (specialized breeds) or their qualitative (local breeds) attributes. As expected, the concentrations of fat, protein fractions, and lactose were influenced by the stage of production, with samples collected in the early stage of production (in February and March) having a greater quantity of the main constituents. Somatic cells reached the highest levels in the late stage of production, which corresponds to the goats' advanced stage of lactation (June–July), although no differences were present in the logarithmic bacterial counts between the early and late stages. Regarding cheesemaking potential, bulk milk samples of the late stage were characterized by delayed rennet coagulation and curd firming times, the lowest values of curd firmness, and a general reduction in CY%, and REC traits. In conclusion, we highlight several issues regarding the effects of the most important sources of variation on bulk goat milk, and point to some critical factors relevant for improving dairy goat farming and milk production.