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.     

Comparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy

Cheese yield is an important technological trait in the dairy industry. The aim of this study was to infer the genetic parameters of some cheese yield-related traits predicted using Fourier-transform infrared (FTIR) spectral analysis and compare the results with those obtained using an individual model cheese-producing procedure. A total of 1,264 model cheeses were produced using 1,500-mL milk samples collected from individual Brown Swiss cows, and individual measurements were taken for 10 traits: 3 cheese yield traits (fresh curd, curd total solids, and curd water as a percent of the weight of the processed milk), 4 milk nutrient recovery traits (fat, protein, total solids, and energy of the curd as a percent of the same nutrient in the processed milk), and 3 daily cheese production traits per cow (fresh curd, total solids, and water weight of the curd). Each unprocessed milk sample was analyzed using a MilkoScan FT6000 (Foss, Hillerød, Denmark) over the spectral range, from 5,000 to 900 wavenumber × cm⁻¹. The FTIR spectrum-based prediction models for the previously mentioned traits were developed using modified partial least-square regression. Cross-validation of the whole data set yielded coefficients of determination between the predicted and measured values in cross-validation of 0.65 to 0.95 for all traits, except for the recovery of fat (0.41). A 3-fold external validation was also used, in which the available data were partitioned into 2 subsets: a training set (one-third of the herds) and a testing set (two-thirds). The training set was used to develop calibration equations, whereas the testing subsets were used for external validation of the calibration equations and to estimate the heritabilities and genetic correlations of the measured and FTIR-predicted phenotypes. The coefficients of determination between the predicted and measured values in cross-validation results obtained from the training sets were very similar to those obtained from the whole data set, but the coefficient of determination of validation values for the external validation sets were much lower for all traits (0.30 to 0.73), and particularly for fat recovery (0.05 to 0.18), for the training sets compared with the full data set. For each testing subset, the (co)variance components for the measured and FTIR-predicted phenotypes were estimated using bivariate Bayesian analyses and linear models. The intraherd heritabilities for the predicted traits obtained from our internal cross-validation using the whole data set ranged from 0.085 for daily yield of curd solids to 0.576 for protein recovery, and were similar to those obtained from the measured traits (0.079 to 0.586, respectively). The heritabilities estimated from the testing data set used for external validation were more variable but similar (on average) to the corresponding values obtained from the whole data set. Moreover, the genetic correlations between the predicted and measured traits were high in general (0.791 to 0.996), and they were always higher than the corresponding phenotypic correlations (0.383 to 0.995), especially for the external validation subset. In conclusion, we herein report that application of the cross-validation technique to the whole data set tended to overestimate the predictive ability of FTIR spectra, give more precise phenotypic predictions than the calibrations obtained using smaller data sets, and yield genetic correlations similar to those obtained from the measured traits. Collectively, our findings indicate that FTIR predictions have the potential to be used as indicator traits for the rapid and inexpensive selection of dairy populations for improvement of cheese yield, milk nutrient recovery in curd, and daily cheese production per cow.     

Genome-wide association study for cheese yield and curd nutrient recovery in dairy cows

Cheese production and consumption are increasing in many countries worldwide. As a result, interest has increased in strategies for genetic selection of individuals for technological traits of milk related to cheese yield (CY) in dairy cattle breeding. However, little is known about the genetic background of a cow's ability to produce cheese. Recently, a relatively large panel (1,264 cows) of different measures of individual cow CY and milk nutrient and energy recoveries in the cheese (REC) became available. Genetic analyses showed considerable variation for CY and for aptitude to retain high proportions of fat, protein, and water in the coagulum. For the dairy industry, these characteristics are of major economic importance. Nevertheless, use of this knowledge in dairy breeding is hampered by high costs, intense labor requirement, and lack of appropriate technology. However, in the era of genomics, new possibilities are available for animal breeding and genetic improvement. For example, identification of genomic regions involved in cow CY might provide potential for marker-assisted selection. The objective of this study was to perform genome-wide association studies on different CY and REC measures. Milk and DNA samples from 1,152 Italian Brown Swiss cows were used. Three CY traits expressing the weight (wt) of fresh curd (%CY_CURD), curd solids (%CY_SOLIDS), and curd moisture (%CY_WATER) as a percentage of weight of milk processed, and 4 REC (REC_FAT, REC_PROTEIN, REC_SOLIDS, and REC_ENERGY, calculated as the % ratio between the nutrient in curd and the corresponding nutrient in processed milk) were analyzed. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2. Single marker regressions were fitted using the GenABEL R package (genome-wide association using mixed model and regression–genomic control). In total, 103 significant associations (88 single nucleotide polymorphisms) were identified in 10 chromosomes (2, 6, 9, 11, 12, 14, 18, 19, 27, 28). For REC_FAT and REC_PROTEIN, high significance peaks were identified in Bos taurus autosome (BTA) 6 and BTA11, respectively. Marker ARS-BFGL-NGS-104610 (～104.3 Mbp) was highly associated with REC_PROTEIN and Hapmap52348-rs29024684 (～87.4 Mbp), closely located to the casein genes on BTA6, with REC_FAT. Genomic regions identified may enhance marker-assisted selection in bovine cheese breeding beyond the use of protein (casein) and fat contents, whereas new knowledge will help to unravel the genomic background of a cow's ability for cheese production.     

Predictive formulas for different measures of cheese yield using milk composition from individual goat samples

The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CY_CURD), total solids (%CY_SOLIDS), and water retained (%CY_WATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R²_VAL) and the root mean square error of validation (RMSE_VAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R²_VAL = 0.65, 0.96, and 0.23 for %CY_CURD, %CY_SOLIDS, and %CY_WATER, respectively). The WAV procedure showed R²_VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CY_CURD and %CY_WATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production.     

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.     

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.     

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.     

Effects of protein and fat levels in milk on cheese and whey compositions

Bulk tank milk was standardised to six levels of fat (3·0, 3·2, 3·4, 3·6, 3·8, 4·0%) and similarly to six levels of protein, thus giving a total of 36 combinations in composition. Milk was analyzed for total solids, fat, protein, casein, lactose and somatic cell count and was used to make laboratory-scale cheese. Cheese samples from each batch were assayed for total solids, fat, protein and salt. Losses of milk components in the whey were also determined. Least squares analysis of data indicated that higher protein level in milk was associated with higher protein and lower fat contents in cheese. This was accompanied by lower total solids (higher moisture) in cheese. Inversely, higher fat level in milk gave higher fat and lower protein and moisture contents in cheese. Higher fat level in milk resulted in lower retention of fat in cheese and more fat losses in the whey. Higher protein level in milk gave higher fat retention in cheese and less fat losses in the whey. Regression analysis showed that cheese fat increased by 4·22%, while cheese protein decreased by 2·61% for every percentage increase in milk fat. Cheese protein increased by 2·35%, while cheese fat decreased by 6·14% per percentage increase in milk protein. Milk with protein to fat ratio close to 0·9 would produce a minimum of 50% fat in the dry matter of cheese.     

Effect of whey concentration on protein recovery in fresh ovine ricotta cheese

Ricotta cheese, particularly the ovine type, is a typical Italian dairy product obtained by heat-coagulation of the proteins in whey. The aim of this work was to investigate the influence of whey protein concentration, obtained by ultrafiltration, on yield of fresh ovine ricotta cheese. Ricotta cheeses were obtained by thermocoagulation of mixtures with protein content of 1.56, 3.10, 4.16, and 7.09 g/100 g from the mixing of skim whey and ultrafiltered skim whey. A fat-to-protein ratio of 1.1 (wt/wt) was obtained for all mixtures by adding fresh cream. The initial mixtures, as well as the final ricotta cheeses, were analyzed for their composition and by SDS-PAGE. Protein bands were quantified by QuantityOne software (Bio-Rad, Hercules, CA) and identified by liquid chromatography-tandem mass spectrometry. Significant differences in the composition of the ricotta cheese were observed depending on protein concentration. Particularly, ricotta cheese resulting from the mixture containing 7.09 g/100 g of protein presented higher moisture (72.88 ± 1.50 g/100 g) and protein (10.18 ± 0.45 g/100 g) contents than that prepared from the mixture with 1.56 g/100 g of protein (69.52 ± 1.75 and 6.70 ± 0.85 g/100 g, respectively), and fat content was lower in this sample (12.20 ± 1.60 g/100 g) compared with the other treatments, with mean values between 15.72 and 20.50 g/100 g. Each protein fraction presented a different behavior during thermocoagulation. In particular, the recovery of β-lactoglobulin and α-lactalbumin in the cheese increased as their content increased in the mixtures. It was concluded that concentrating ovine rennet whey improved the extent of heat-induced protein aggregation during the thermal coagulation process. This resulted in a better recovery of each protein fraction in the product, and in a consequent increase of ricotta cheese yield.     

Slower proteolysis in Cheddar cheese made from high-protein cheese milk is due to an elevated whey protein content

A growing number of companies within the cheese-making industry are now using high-protein (e.g., 4–5%) milks to increase cheese yield. Previous studies have suggested that cheeses made from high-protein (both casein and whey protein; WP) milks may ripen more slowly; one suggested explanation is inhibition of residual rennet activity due to elevated WP levels. We explored the use of microfiltration (MF) to concentrate milk for cheese-making, as that would allow us to concentrate the casein while varying the WP content. Our objective was to determine if reducing the level of WP in concentrated cheese milk had any impact on cheese characteristics, including ripening, texture, and nutritional profile. Three types of 5% casein standardized and pasteurized cheese milks were prepared that had various casein:true protein (CN:TP) ratios: (a) control with CN:TP 83:100, (b) 35% WP reduced, 89:100 CN:TP, and (c) 70% WP reduced, 95:100 CN:TP. Standardized milks were preacidified to pH 6.2 with dilute lactic acid during cheese-making. Composition, proteolysis, textural, rheological, and sensory properties of cheeses were monitored over a 9-mo ripening period. The lactose, total solids, total protein, and WP contents in the 5% casein concentrated milks were reduced with increasing levels of WP removal. All milks had similar casein and total calcium levels. Cheeses had similar compositions, but, as expected, lower WP levels were observed in the cheeses where WP depletion by MF was performed on the cheese milks. Cheese yield and nitrogen recoveries were highest in cheese made with the 95:100 CN:TP milk. These enhanced recoveries were due to the higher fraction of nitrogen being casein-based solids. Microfiltration depletion of WP did not affect pH, sensory attributes, or insoluble calcium content of cheese. Proteolysis (the amount of pH 4.6 soluble nitrogen) was lower in control cheeses compared with WP-reduced cheeses. During ripening, the hardness values and the temperature of the crossover point, an indicator of the melting point of the cheese, were higher in the control cheese. It was thus likely that the higher residual WP content in the control cheese inhibited proteolysis during ripening, and the lower breakdown rate resulted in its higher hardness and melting point. There were no major differences in the concentrations of key nutrients with this WP depletion method. Cheese milk concentration by MF provides the benefit of more typical ripening rates.     

Validation of a curd-syneresis sensor over a range of milk composition and process parameters

An online visible-near-infrared sensor was used to monitor the course of syneresis during cheesemaking with the purpose of validating syneresis indices obtained using partial least squares, with cross-validation across a range of milk fat levels, gel firmness levels at cutting, curd cutting programs, stirring speeds, milk protein levels, and fat:protein ratio levels. Three series of trials were carried out in an 11-L cheese vat using recombined whole milk. Three factorial experimental designs were used, consisting of 1) 3 curd stirring speeds and 3 cutting programs; 2) 3 milk fat levels and 3 gel firmness levels at cutting; and 3) 2 milk protein levels and 3 fat:protein ratio levels, respectively. Milk was clotted under constant conditions in all experiments and the gel was cut according to the respective experimental design. Prediction models for production of whey and whey fat losses were developed in 2 of the experiments and validated in the other experiment. The best models gave standard error of prediction values of 6.6 g/100 g for yield of whey and 0.05 g/100 g for fat in whey, as compared with 4.4 and 0.013 g/100 g, respectively, for the calibration data sets. Robust models developed for predicting yield of whey and whey fat losses using a validation method have potential application in the cheese industry.     

Low‐fat Cheddar cheese made using microparticulated whey proteins: Effect on yield and cheese quality

Influence of different levels (0, 0.15, 0.35 or 0.50%) of microparticulated whey protein (MWP) on yield and quality of low‐fat (~7.3 g/100 g) Cheddar cheese was investigated. MWP improved cheese yield due to the water‐binding ability of denatured whey protein. MWP addition decreased meltability but improved the textural properties beneficial for shredding and slicing, by decreasing sensory firmness. The results emphasise the role of MWP as an inert filler within cheese matrix, in improving cheese yield and creating a softer texture without compromising the sensory or overall quality of cheese, even with moisture increases in 0.35 or 0.50% MWP cheeses.     

Impact of low concentration factor microfiltration on milk component recovery and Cheddar cheese yield

The effect of microfiltration (MF) on the composition of Cheddar cheese, fat, crude protein (CP), calcium, total solids recovery, and Cheddar cheese yield efficiency (i.e., composition adjusted yield divided by theoretical yield) was determined. Raw skim milk was microfiltered twofold using a 0.1-microm ceramic membrane at 50 degrees C. Four vats of cheese were made in one day using milk at lx, 1.26x, 1.51x, and 1.82x concentration factor (CF). An appropriate amount of cream was added to achieve a constant casein (CN)-to-fat ratio across treatments. Cheese manufacture was repeated on four different days using a randomized complete block design. The composition of the cheese was affected by MF. Moisture content of the cheese decreased with increasing MF CF. Standardization of milk to a constant CN-to-fat ratio did not eliminate the effect of MF on cheese moisture content. Fat recovery in cheese was not changed by MF. Separation of cream prior to MF, followed by the recombination of skim or MF retentate with cream resulted in lower fat recovery in cheese for control and all treatments and higher fat loss in whey when compared to previous yield experiments, when control Cheddar cheese was made from unseparated milk. Crude protein, calcium, and total solids recovery in cheese increased with increasing MF CF, due to partial removal of these components prior to cheese making. Calcium and calcium as a percentage of protein increased in the cheese, suggesting an increase in calcium retention in the cheese with increasing CF. While the actual and composition adjusted cheese yields increased with increasing MF CF, as expected, there was no effect of MF CF on cheese yield efficiency.     

Effects of technological settings on yield,curd, whey,and cheese composition during the cheese-making process from raw sheep milk in small rural dairies: Emphasis on cutting and cooking conditions

The technological conditions of cheese-making affect cheese yield and compound losses in the whey, especially the processes of cutting and cooking. Although significant compositional and functional differences have been reported among animal species, there is a lack of studies on the effects of cheese-making technology on cheese yield and losses for sheep milk. Thus, we examined the cheese-making settings in 8 small rural dairies working with raw sheep milk and their effects on cheese yield and compound losses in whey during the cheese-production season. Actual cheese yield varied in 2 to 3 kg of cheese/100 kg of milk among dairies due to the cheese-making conditions, particularly the duration of cutting and cooking and the final cooking temperature. The combination of the conditions used during cutting and cooking, especially, determined fat losses in the whey. Fat losses were increased with high-speed and short cutting time settings together with high stirring speed and long duration of cooking. Additionally, cheese-makers should adapt the cutting and cooking conditions to the seasonal variations of milk composition, especially during early summer, when fat losses in the whey are higher. Our results suggest that it could be useful to use approximately 10 to 15 min of cutting time and moderate cooking speed and duration. The data reported in this study may assist the improvement of the cheese-making process in small rural dairies using sheep milk, where facilities are limited and the role of the cheese-maker is crucial.     

Thermocalcic aggregation of milk fat globule membrane fragments from acid buttermilk cheese whey

Fragments originating from the milk fat globule membrane (MFGM), which is rich in polar lipids and membrane-specific proteins, are gaining interest for their functional and nutritional properties. Acid buttermilk cheese whey was used as a source for MFGM purification, because its MFGM content is more than 5 times higher than that of standard rennet whey. Because polar lipids are the main constituent of the MFGM and only occur in membranous structures, the polar lipid content was taken as a parameter for the total MFGM fragment content. The process of thermocalcic aggregation was evaluated on its recovery of MFGM fragments in the pellet. This method, originally intended for whey clarification and defatting, is a combination of calcium addition, a pH increase, and a thermal treatment. The influence of pH (6.5 to 8), temperature (40 to 70°C), and calcium concentration (0.1 to 0.24 g/100 g) on the pellet mass and dry matter (DM) content and on recovery of protein and polar lipids (and thus indirectly on MFGM fragments) was investigated by means of a response surface Box-Behnken orthogonal design. Reduced quadratic models were fit to the experimental data and were found to be highly significant. No outliers were observed. The recovery of MFGM fragments was found to be highly dependent on the pH, and less dependent on temperature and calcium addition. Next to MFGM proteins, whey proteins were also found to be involved in the formation of aggregates. Optimal conditions were found at 55°C, pH 7.7, and 0.205 g of calcium/L of whey. Under these conditions, 91.0% of the whey polar lipids were recovered in a firm and compact pellet of only 7.86% of the original whey mass, with a polar lipid concentration of 8.34% on pellet DM. Washing with water and centrifugation of the pellet was successful because after one washing step, virtually all sugars were removed, whereas 75.9% of the whey polar lipids could still be recovered. As such, the polar lipid content of the washed pellet increased to 10.70% on a DM basis. However, a second washing step resulted in serious losses of MFGM material.     

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.     

Filtration of milk fat globule membrane fragments from acid buttermilk cheese whey

The proteins and polar lipids present in milk fat globule membrane (MFGM) fragments are gaining attention for their technological and nutritional properties. These MFGM fragments are preferentially enriched in side streams of the dairy industry, like butter serum, buttermilk, and whey. The objective of this study was to recover MFGM fragments from whey by tangential filtration techniques. Acid buttermilk cheese whey was chosen as a source for purification by tangential membrane filtration because it is relatively rich in MFGM-fragments and because casein micelles are absent. Polyethersulfone and cellulose acetate membranes of different pore sizes were evaluated on polar lipid and MFGM-protein retention upon filtration at 40°C. All fractions were analyzed for dry matter, ash, lipids, proteins, reducing sugars, polar lipid content by HPLC, and for the presence of MFGM proteins by sodium dodecyl sulfate-PAGE. A fouling coefficient was calculated. It was found that a thermocalcic aggregation whey pretreatment was very effective in the clarification of the whey, but resulted in low permeate fluxes and high retention of ash and whey proteins. By means of an experimental design, the influence of pH and temperature on the fouling and the retention of polar lipids (and thus MFGM fragments), proteins, and total lipids upon microfiltration with 0.15 μM cellulose acetate membrane was investigated. All models were highly significant, and no outliers were observed. By increasing the pH from 4.6 to 7.5, polar lipid retention at 50°C increased from 64 to 98%, whereas fouling of the filtration membrane was minimized. A 3-step diafiltration of acid whey under these conditions resulted in a polar lipid concentration of 6.79 g/100 g of dry matter. As such, this study shows that tangential filtration techniques are suited for the purification of MFGM fragments.     

Valuation of milk composition and genotype in cheddar cheese production using an optimization model of cheese and whey production

A mass balance optimization model was developed to determine the value of the κ-casein genotype and milk composition in Cheddar cheese and whey production. Inputs were milk, nonfat dry milk, cream, condensed skim milk, and starter and salt. The products produced were Cheddar cheese, fat-reduced whey, cream, whey cream, casein fines, demineralized whey, 34% dried whey protein, 80% dried whey protein, lactose powder, and cow feed. The costs and prices used were based on market data from March 2004 and affected the results. Inputs were separated into components consisting of whey protein, ash, casein, fat, water, and lactose and were then distributed to products through specific constraints and retention equations. A unique 2-step optimization procedure was developed to ensure that the final composition of fat-reduced whey was correct. The model was evaluated for milk compositions ranging from 1.62 to 3.59% casein, 0.41 to 1.14% whey protein, 1.89 to 5.97% fat, and 4.06 to 5.64% lactose. The κ casein genotype was represented by different retentions of milk components in Cheddar cheese and ranged from 0.715 to 0.7411 kg of casein in cheese/kg of casein in milk and from 0.7795 to 0.9210 kg of fat in cheese/kg of fat in milk. Milk composition had a greater effect on Cheddar cheese production and profit than did genotype. Cheese production was significantly different and ranged from 9,846 kg with a high-casein milk composition to 6,834 kg with a high-fat milk composition per 100,000 kg of milk. Profit (per 100,000 kg of milk) was significantly different, ranging from $70,586 for a high-fat milk composition to $16,490 for a low-fat milk composition. However, cheese production was not significantly different, and profit was significant only for the lowest profit ($40,602) with the κ-casein genotype. Results from this model analysis showed that the optimization model is useful for determining costs and prices for cheese plant inputs and products, and that it can be used to evaluate the economic value of milk components to optimize cheese plant profits.     

Relationships between cheese-processing conditions and curd and cheese properties to improve the yield of Idiazabal cheese made in small artisan dairies: A multivariate approach

Very diverse cutting and cooking intensity processes are currently used in small artisan dairies to manufacture Idiazabal cheese. The combination of the technical settings used during cheese manufacturing is known to affect cheese composition and yield, as well as whey losses. However, the information regarding the effect on microstructure and texture of cheese is scarce, especially in commercial productions. Therefore, the effect of moderate- and high-intensity cutting and cooking processes on whey losses, curd-grain characteristics, microstructure and cheese properties, and yield were analyzed. Three trials were monitored in each of 2 different small dairies during the cheesemaking of Idiazabal cheese, which is a semihard cheese made from raw sheep milk. The role and know-how of the cheesemakers are crucial in these productions because they determine the cutting point and handle semi-automatic vats. The 2 dairies studied used the following settings: dairy A used moderate-intensity cutting and cooking conditions, and dairy B used high-intensity cutting and cooking settings. Multiple relationships between cheese-processing conditions and curd, whey, and cheese properties as well as yield were obtained from a partial least square regression analysis. An increased amount of fat and casein losses were generated due to a combination of an excessively firm gel at cutting point together with high-intensity cutting and cooking processes. The microstructural analysis revealed that the porosity of the protein matrix of curd grains after cooking and cheese after pressing was the main feature affected, developing a less porous structure with a more intense process. Moderate-intensity cutting and cooking processes were associated with a higher cheese yield, regardless of the longer pressing process applied. No significant differences were observed in cheese composition. After 1 mo of ripening, however, the cheese was more brittle and adhesive when the high-intensity cutting and cooking process was applied. This could be associated with the composition, characteristics, and size distribution of curd grains due to differences in the compaction degree during pressing. These results could help to modify specific conditions used in cheesemaking, especially improving the process in those small dairies where the role of the cheesemaker is crucial.     

Effects of the detailed protein composition of milk on curd yield and composition measured by model micro-cheese curd making of individual milk samples

The effect of the contents of casein (CN) and whey protein fractions on curd yield (CY) and composition was estimated using 964 individual milk samples. Contents of α_S1-CN, α_S2-CN, β-CN, γ-CN, glycosylated κ-CN (Gκ-CN), unglycosylated κ-CN, β-LG, and α-LA of individual milk samples were measured using reversed-phase HPLC. Curd yield and curd composition were measured by model micro-cheese curd making using 25 mL of milk. Dry matter CY (DMCY) was positively associated with all casein fractions but especially with α_S1-CN and β-CN. Curd moisture decreased at increasing β-CN content and increased at increasing γ-CN and Gκ-CN content. Due to their associations with moisture, Gκ-CN and β-CN were the fractions with the greatest effect on raw CY, which decreased by 0.66% per 1-standard deviation (SD) increase in the content of β-CN and increased by 0.62% per 1-SD increase in the content of Gκ-CN. The effects due to variation in percentages of the casein fractions in total casein were less marked than those exerted by contents. A 1-SD increase in β-CN percentage in casein (+3.8% in casein) exerted a slightly negative effect on DMCY (β = ?0.05%). Conversely, increasing amounts of α_S1-CN percentage were associated with a small increase in DMCY. Hence, results suggest that, at constant casein and whey protein contents in milk, the DMCY depends to a limited extent on the variation in the α_S1-CN:β-CN ratio. κ-Casein percentage did not affect DMCY, indicating that the positive relationship detected between the content of κ-CN and DMCY can be attributed to the increase in total casein resulting from the increased amount of κ-CN and not to variation in κ-CN relative content. However, milk with increased Gκ-CN percentage in κ-CN also shows increased raw CY and produces curds with increased moisture content. Curd yield increased at increasing content and relative proportion of β-LG in whey protein, but this is attributable to an improved capacity of the curd to retain water. Results obtained in this study support the hypothesis that, besides variation in total casein and whey protein contents, variation in protein composition might affect the cheese-making ability of milk, but this requires further studies.