不同因素对羊奶干酪出品率的影响

对影响羊奶干酪出品率主要因素进行了研究。结果表明,原料乳浓度越大,羊奶干酪出品率越高;杀菌条件以巴氏杀菌或高温短时杀菌效果较好;CaCl2添加量以0.02%～0.03%为宜;用犊牛皱胃酶或羔羊皱胃酶为凝乳酶,羊奶干酪的出品率最高。     

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.     

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.     

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.     

Quantification of volatile compounds in goat milk Jack cheese using static headspace gas chromatography

Goat milk Jack cheeses were manufactured with different levels of proteolytic endo- and exopeptidases from lysed bacterial cultures and aged for 30 wk. The aroma compounds that are potentially important in contributing the typical flavor of goat milk Jack cheese were quantified using static headspace gas chromatography. The concentrations of volatile compounds were evaluated every 6 wk throughout the aging period. Odor activity values of volatile compounds were calculated using the sensory threshold values reported in literature and their concentrations in Jack cheeses. Odor activity values of identified compounds were used to assess their potential contribution to the aroma of goat milk Jack cheeses. The odor activity values indicated that the ketones 2-hexanone, 2-heptanone, 2-nonanone, and 2,3-butanedione (diacetyl) were important odor-active compounds. The major odor-active acids found in this semi-hard goat milk cheese were butanoic, 2-methyl butanoic, pentanoic, hexanoic, and octanoic acids. Among the aldehydes, propanal and pentanal had high odor activity values and likely contributed to the aroma of this cheese. The concentrations of butanoic, pentanoic, hexanoic, heptanoic, octanoic, and nonanoic acids increased significantly in goat milk Jack cheese throughout aging. The extracted enzymes from lysed bacterial cultures that were added to the cheeses during manufacturing caused considerable increases in the concentrations of butanoic and hexanoic acids compared with the control. However, the lower concentration of peptidases resulted in an increased concentration of butanal, whereas more peptidases resulted in a lower concentration of 2-nonanone in goat milk Jack cheeses.     

Impact of milk preacidification with CO2 on cheddar cheese composition and yield

Preacidification of milk for cheese making may have a beneficial impact on increasing proteolysis during cheese aging. Unlike other acids, CO(2) can easily be removed from whey. The objectives of this work were to determine the effect of milk preacidification on Cheddar cheese composition, the recovery of individual milk components, and yield. Carbon dioxide was injected inline after the cooling section of the pasteurizer. Cheeses with and without added CO(2) were made simultaneously from the same batch of milk. This procedure was replicated 3 times. Carbon dioxide in the cheese milk was about 1600 ppm, which resulted in a milk pH of about 5.9 at 31 degrees C. The starter culture and coagulant addition rates were the same for both the CO(2) treatment and the control. The whey pH at draining of the CO(2) treatment was lower than the control. Total make time was shorter for the CO(2) treatment compared with the control. Cheese manufactured from milk acidified with CO(2) retained less of the total calcium and fat than the control cheese. The higher fat loss was primarily in the whey at draining. Preacidification with CO(2) did not alter the crude protein recovery in the cheese. The CO(2) treatment resulted in a higher added salt recovery in the cheese and produced a cheese that contained too much salt. Considering the higher added salt retention, the salt application rate could be lowered to achieve a typical cheese salt content. Cheese yield efficiency of the CO(2) treated milk was 4.4% lower than the control due to fat loss. Future work will focus on modifying the make procedure to achieve a normal fat loss into the whey when CO(2) is added to milk.     

Impact of seasonal changes in ovine milk on composition and yield of a hard-pressed cheese

A hard-pressed, brined cheese was produced from frozen ovine milk collected in February, May, and August. Solids in the milk decreased as the season progressed. This was a result of high solids in early-lactation milk and low solids in August milk because of hot weather and poorer quality pastures. Casein as a percentage of true protein and the casein to fat ratio were higher in May and August milk. Fat in the cheese from February milk was higher and total protein was lower than in May and August. Milk, whey, and press whey composition were influenced by season and followed the trends of milk composition. Fat recovery in the cheeses ranged from 83.2 to 84.2%. Protein recovery in the cheeses was not affected by season. Cheese yield from February milk was higher than from May and August milk and was a result of higher casein and fat in the milk.     

Invited review: A commentary on predictive cheese yield formulas

Predictive cheese yield formulas have evolved from one based only on casein and fat in 1895. Refinements have included moisture and salt in cheese and whey solids as separate factors, paracasein instead of casein, and exclusion of whey solids from moisture associated with cheese protein. The General, Barbano, and Van Slyke formulas were tested critically using yield and composition of milk, whey, and cheese from 22 vats of Cheddar cheese. The General formula is based on the sum of cheese components: fat, protein, moisture, salt, whey solids free of fat and protein, as well as milk salts associated with paracasein. The testing yielded unexpected revelations. It was startling that the sum of components in cheese was <100%; the mean was 99.51% (N × 6.31). The mean predicted yield was only 99.17% as a percentage of actual yields (PY%AY); PY%AY is a useful term for comparisons of yields among vats. The PY%AY correlated positively with the sum of components (SofC) in cheese. The apparent low estimation of SofC led to the idea of adjusting upwards, for each vat, the 5 measured components in the formula by the observed SofC, as a fraction. The mean of the adjusted predicted yields as percentages of actual yields was 99.99%. The adjusted forms of the General, Barbano, and Van Slyke formulas gave predicted yields equal to the actual yields. It was apparent that unadjusted yield formulas did not accurately predict yield; however, unadjusted PY%AY can be useful as a control tool for analyses of cheese and milk. It was unexpected that total milk protein in the adjusted General formula gave the same predicted yields as casein and paracasein, indicating that casein or paracasein may not always be necessary for successful yield prediction. The use of constants for recovery of fat and protein in the adjusted General formula gave adjusted predicted yields equal to actual yields, indicating that analyses of cheese for protein and fat may not always be necessary for yield prediction. Composition of cheese was estimated using a predictive formula; actual yield was needed for estimation of composition. Adjusted formulas are recommended for estimating target yields and cheese yield efficiency. Constants for solute exclusion, protein-associated milk salts, and whey solids could be used and reduced the complexity of the General formula. Normalization of fat recovery increased variability of predicted yields.     

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.     

Lipolysis and proteolysis profiles of fresh artisanal goat cheese made with raw milk with 3 different fat contents

The objective of this study was to describe the proteolysis and lipolysis profiles in goat cheese made in the Canary Islands (Spain) using raw milk with 3 different fat contents (0.5, 1.5, and 5%) and ripened for 1, 7, 14, and 28 d. β-Casein was the most abundant protein in all cheeses and at all ripening times. Quantitative analysis showed a general decrease in caseins as ripening progressed, and degradation rates were higher for α_S1-casein than for β-casein and α_S2-casein. Furthermore, the degradation rate during the experimental time decreased with lower fat contents. The α_S2-casein and α_S1-casein levels that remained in full-fat and reduced-fat cheeses were less than those in low-fat cheese. In contrast, β-casein also showed degradation along with ripening, but differences in degradation among the 3 cheese types were not significant at 28 d. The degradation products increased with the ripening time in all cheeses, but they were higher in full-fat cheese than in reduced-fat and low-fat cheeses. The free fatty acid concentration per 100 g of cheese was higher in full-fat cheese than in reduced- and low-fat cheese; however, when the results were expressed as milligrams of free fatty acids per gram of fat in cheese, then lipolysis occurred more rapidly in low-fat cheese than in reduced- and full-fat cheeses. These results may explain the atypical texture and off-flavors found in low-fat goat cheeses, likely the main causes of non-acceptance.     

Standardization of milk using cold ultrafiltration retentates for the manufacture of Swiss cheese: effect of altering coagulation conditions on yield and cheese quality

Fortification of cheesemilk with membrane retentates is often practiced by cheesemakers to increase yield. However, the higher casein (CN) content can alter coagulation characteristics, which may affect cheese yield and quality. The objective of this study was to evaluate the effect of using ultrafiltration (UF) retentates that were processed at low temperatures on the properties of Swiss cheese. Because of the faster clotting observed with fortified milks, we also investigated the effects of altering the coagulation conditions by reducing the renneting temperature (from 32.2 to 28.3°C) and allowing a longer renneting time before cutting (i.e., giving an extra 5 min). Milks with elevated total solids (TS; ∼13.4%) were made by blending whole milk retentates (26.5% TS, 7.7% CN, 11.5% fat) obtained by cold (<7°C) UF with part skim milk (11.4% TS, 2.5% CN, 2.6% fat) to obtain milk with CN:fat ratio of approximately 0.87. Control cheeses were made from part-skim milk (11.5% TS, 2.5% CN, 2.8% fat). Three types of UF fortified cheeses were manufactured by altering the renneting temperature and renneting time: high renneting temperature = 32.2°C (UFHT), low renneting temperature = 28.3°C (UFLT), and a low renneting temperature (28.3°C) plus longer cutting time (+5 min compared to UFLT; UFLTL). Cutting times, as selected by a Wisconsin licensed cheesemaker, were approximately 21, 31, 35, and 32 min for UFHT, UFLT, UFLTL, and control milks, respectively. Storage moduli of gels at cutting were lower for the UFHT and UFLT samples compared with UFLTL or control. Yield stress values of gels from the UF-fortified milks were higher than those of control milks, and decreasing the renneting temperature reduced the yield stress values. Increasing the cutting time for the gels made from the UF-fortified milks resulted in an increase in yield stress values. Yield strain values were significantly lower in gels made from control or UFLTL milks compared with gels made from UFHT or UFLT milks. Cheese composition did not differ except for fat content, which was lower in the control compared with the UF-fortified cheeses. No residual lactose or galactose remained in the cheeses after 2 mo of ripening. Fat recoveries were similar in control, UFHT, and UFLTL but lower in UFLT cheeses. Significantly higher N recoveries were obtained in the UF-fortified cheeses compared with control cheese. Because of higher fat and CN contents, cheese yield was significantly higher in UF-fortified cheeses (∼11.0 to 11.2%) compared with control cheese (∼8.5%). A significant reduction was observed in volume of whey produced from cheese made from UF-fortified milk and in these wheys, the protein was a higher proportion of the solids. During ripening, the pH values and 12% trichloroacetic acid-soluble N levels were similar for all cheeses. No differences were observed in the sensory properties of the cheeses. The use of UF retentates improved cheese yield with no significant effect on ripening or sensory quality. The faster coagulation and gel firming can be decreased by altering the renneting conditions.     

Innovative Caciocavallo cheeses made from a mixture of cow milk with ewe or goat milk

This study assessed and compared the physicochemical, microbiological, and sensorial characteristics of Caciocavallo cheeses, made from cow milk and a mixture of cow with ewe or goat milk, during ripening. Different cheese-making trials were carried out on an industrial scale following the standard procedure of pasta filata cheeses, with some modifications. The percentage of the different added milk to cow milk influenced compositional and nutritional characteristics of the innovative products, leading to a satisfactory microbiological and sensorial quality.     

Farm and factory production of goat cheese whey results in distinct chemical composition

To analyze differences in fat and protein content in cheese whey (CW) manufactured in cheese-making factories and farms, goat CW samples were obtained from 60 cheese-making farms and 20 cheese factories. Gross composition of samples was analyzed by using an MIRIS device (MIRIS Inc., Uppsala, Sweden), whey protein composition was subjected to electrophoretic analysis, and fatty acid composition was analyzed via gas chromatography. Goat CW from farms contained higher dry matter content (70.6 vs. 50.8 g/L, farms vs. cheese factories, respectively) and a higher fat percentage (10.5 vs. 1.2% over dry matter, farms vs. cheese factories, respectively) than CW from cheese factories. Analysis of individual proteins showed that CW from farms contained higher concentrations of lactoferrin (0.4 vs. 0.2 mg/mL of CW, farms vs. cheese factories, respectively) and caprine serum albumin (0.6 vs. 0.4 mg/mL of whey, farms vs. cheese factories, respectively) than CW from cheese factories. No differences were observed in the fatty acid profile. The main fatty acids present in goat CW were C16:0, C18:1, C14:0, and C18:0. Thus, the origin of CW affects gross composition and the protein profile, but not the fatty acid profile.     

不同因素对羊奶干酪凝乳效果的影响

对影响羊奶干酪凝乳效果主要因素进行了研究。结果表明,原料乳浓度越大,凝乳效果越好;杀菌条件以巴氏杀菌或高温短时杀菌效果较好;添加发酵剂使乳酸度达24～25°T效果较好;CaCl2添加量以0.01%～0.03%为宜。用犊牛皱胃酶或羔羊皱胃酶作为凝乳酶,可得到较好的凝乳效果。     

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.     

Composition, yield, and functionality of reduced-fat Oaxaca cheese: effects of using skim milk or a dry milk protein concentrate

The effect of adding either skim milk or a commercial dry milk protein concentrate (MPC) to whole milk on the composition, yield, and functional properties of Mexican Oaxaca cheese were investigated. Five batches of Oaxaca cheeses were produced. One batch (the control) was produced from whole milk containing 3.5% fat and 9% nonfat solids (SNF). Two batches were produced from milk standardized with skim milk to 2.7 and 1.8% fat, maintaining the SNF content at 9%. In the other 2 batches, an MPC (40% protein content) was used to standardize the milk to a SNF content of 10 and 11%, maintaining the milk fat content at 3.5%. The use of either skim milk or MPC caused a significant decrease in the fat percentage in cheese. The use of skim milk or MPC showed a nonsignificant tendency to lower total solids and fat recoveries in cheese. Actual, dry matter, and moisture-adjusted cheese yields significantly decreased with skim milk addition, but increased with MPC addition. However, normalized yields adjusted to milk fat and protein reference levels did not show significant differences between treatments. Considering skim milk-added and control cheeses, actual yield increased with cheese milk fat content at a rate of 1.34 kg/kg of fat (R = 0.88). In addition, cheese milk fat and SNF:fat ratio proved to be strong individual predictors of cheese moisture-adjusted yield (r² ≈ 0.90). Taking into account the results obtained from control and MPC-added cheeses, a 2.0-kg cheese yield increase rate per kg of milk MPC protein was observed (R = 0.89), with TS and SNF being the strongest predictors for moisture adjusted yield (r² ≈ 0.77). Reduced-fat Oaxaca cheese functionality differed from that of controls. In unmelted reduced-fat cheeses, hardness and springiness increased. In melted reduced-fat cheeses, meltability and free oil increased, but stretchability decreased. These changes were related to differences in cheese composition, mainly fat in dry matter and calcium in SNF.     

Effect of somatic cell count in goat milk on yield, sensory quality, and fatty acid profile of semisoft cheese

This study investigated the effect of somatic cell count (SCC) in goat milk on yield, free fatty acid (FFA) profile, and sensory quality of semisoft cheese. Sixty Alpine goats without evidence of clinical mastitis were assigned to 3 groups with milk SCC level of <500,000 (low), 500,000 to 1,000,000 (medium), and 1,000,000 to 1,500,000 (high) cells/mL. Thirty kilograms of goat milk with mean SCC levels of 410,000 (low), 770,000 (medium), and 1,250,000 (high) cells/mL was obtained for the manufacture of semisoft cheese for 2 consecutive weeks in 3 lactation stages. The composition of milk was analyzed and cheese yield was recorded on d 1. Cheese samples on d 1, 60, and 120 were analyzed for total sensory scores, flavor, and body and texture by a panel of 3 expert judges and were also analyzed for FFA. Results indicated that milk composition did not change when milk SCC varied from 214,000 to 1,450,000 cells/mL. Milk with higher SCC had a lower standard plate count, whereas coliform count and psychrotrophic bacteria count were not affected. However, milk components (fat, protein, lactose, casein, and total solids) among the 3 groups were similar. As a result, no significant differences in the yield of semisoft goat cheeses were detected. However, total sensory scores and body and texture scores for cheeses made from the high SCC milk were lower than those for cheeses made from the low and medium SCC milks. The difference in milk SCC levels also resulted in diverse changes in cheese texture (hardness, springiness, and so on) and FFA profiles. Individual and total FFA increased significantly during ripening, regardless the SCC levels. It is concluded that SCC in goat milk did not affect the yield of semisoft cheese but did result in inferior sensory quality of aged cheeses.     

Nutritional and sensory characteristics of Minas fresh cheese made with goat milk,cow milk,or a mixture of both

This study aimed to assess and compare the nutritional, technological, and sensory characteristics of Minas fresh cheese made with goat milk, cow milk, or a mixture of the two stored in cold conditions for 21 d. The yield and centesimal composition of the cheeses were not affected by the type of milk used in their preparation. Reductions were observed in the moisture content, pH, proteolysis index, and instrumental hardness; moreover, increases were observed in the syneresis, acidity index, and depth of proteolysis index in all cheeses. The percentages of caprylic, capric, oleic, and linoleic fatty acids were higher in goat milk cheese and cheese made with a mixture of goat and cow milk compared with cow milk cheese, and a sensory evaluation revealed differences in color, flavor, and aroma between the cheeses. The preparation of Minas fresh cheese with a mixture of goat and cow milk can be a viable alternative for dairy products in the market that can be characterized as high-quality products that meet consumer demands.     

Standardization of milk using cold ultrafiltration retentates for the manufacture of parmesan cheese

The effects of using cold ultrafiltered (UF) retentates (both whole and skim milk) on the coagulation, yield, composition, and ripening of Parmesan cheese were investigated. Milks for cheese making were made by blending cold UF retentates with partially skimmed milk to obtain blends with 14.2% solids and a casein:fat ratio of 1.1. Cutting times, as selected by the cheese-maker, were approximately 15 and approximately 20 min for experimental and control milks, respectively. Storage modulus values at cutting were similar, but yield stress values were significantly higher in UF retentate standardized milks. Cheese yields were significantly higher in UF retentate standardized milks (approximately 12%) compared with control milk (cream removed) (approximately 7 to 8%). Significantly higher protein recoveries were obtained in cheeses manufactured using cold UF retentates. There were no differences in the pH and moisture contents of the cheeses prior to brining, and there was no residual lactose or galactose left in the cheeses. Using UF retentates resulted in a significant reduction in whey volume as well as a higher proportion of protein in the solids of the whey. Proteolysis, free fatty acids, and sensory properties of the cheeses were similar. The use of milk concentrated by cold UF is a promising way of improving the yield of Parmesan cheese without compromising cheese quality. The question remaining to be answered by the cheesemaker is whether it is economical to do so.     

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.