乳体细胞数对干酪生产的影响

论述了原料乳中体细胞数对干酪加工、成熟和产量的影响,以期为干酪生产提供理论参考.     

Microbial and sensory changes throughout the ripening of Prato cheese made from milk with different levels of somatic cells

The objective of this research was to evaluate the effects of 2 levels of raw milk somatic cell count (SCC) on the composition of Prato cheese and on the microbiological and sensory changes of Prato cheese throughout ripening. Two groups of dairy cows were selected to obtain low-SCC (<200,000 cells/mL) and high-SCC (>700,000 cells/mL) milks, which were used to manufacture 2 vats of cheese. The pasteurized milk was evaluated according to the pH, total solids, fat, total protein, lactose, standard plate count, coliforms at 45°C, and Salmonella spp. The cheese composition was evaluated 2 d after manufacture. Lactic acid bacteria, psychrotrophic bacteria, and yeast and mold counts were carried out after 3, 9, 16, 32, and 51 d of storage. Salmonella spp., Listeria monocytogenes, and coagulase-positive Staphylococcus counts were carried out after 3, 32, and 51 d of storage. A 2 × 5 factorial design with 4 replications was performed. Sensory evaluation of the cheeses from low- and high-SCC milks was carried out for overall acceptance by using a 9-point hedonic scale after 8, 22, 35, 50, and 63 d of storage. The somatic cell levels used did not affect the total protein and salt:moisture contents of the cheeses. The pH and moisture content were higher and the clotting time was longer for cheeses from high-SCC milk. Both cheeses presented the absence of Salmonella spp. and L. monocytogenes, and the coagulase-positive Staphylococcus count was below 1 × 10² cfu/g throughout the storage time. The lactic acid bacteria count decreased significantly during the storage time for the cheeses from both low- and high-SCC milks, but at a faster rate for the cheese from high-SCC milk. Cheeses from high-SCC milk presented lower psychrotrophic bacteria counts and higher yeast and mold counts than cheeses from low-SCC milk. Cheeses from low-SCC milk showed better overall acceptance by the consumers. The lower overall acceptance of the cheeses from high-SCC milk may be associated with texture and flavor defects, probably caused by the higher proteolysis of these cheeses.     

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.     

原料乳体细胞数不同对契达干酪产出量、质构及感官的影响

用体细胞数(SCC)分别是5.6×104,48.8×104,476.1×104 mL-1的原料乳制作契达干酪,得到LSCC,MSCC,HSCC组干酪。从干酪真正产出量来看:LSCC组>MSCC组>HSCC组(P<0.05)。在干酪成熟过程中,质构与SCC在P<0.01的水平下负相关,其中硬度、剪切力相关系数分别为0.5482和1.3977。感官评定结果表明,HSCC组干酪有酸味,且组织状态软而粘。同时对干酪成熟过程中的水溶性氮和脂解进行了测定,其结果是:WSN/TN与SCC在P<0.01水平下线性相关,相关系数为0.4261;HSCC组干酪的FFA在P<0.05的水平下显著高于LSCC和MSCC组干酪,且FFA与SCC在P<0.0001的水平下正相关。     

Estimation of somatic cell count levels of hard cheeses using physicochemical composition and artificial neural networks

This study addresses the prediction of the somatic cell counts of the milk used in the production of sheep cheese using artificial neural networks. To achieve this objective, the neural network was designed using 33 parameters of the physicochemical composition of the cheeses obtained after they have been matured for 12 mo as input data. The physicochemical analysis of the cheeses revealed that the somatic cell count level of the cheese has a significant influence on the amount of protein, fat, dry extract, and fatty acids. When properly set up, the neural network allows the correct classification of the cheeses (100% of correct results in both training and test phases) and therefore their samples in each of the 3 nominal output variables (low, average, and high somatic cell counts). The fatty composition of the cheeses, individual fatty acids, and fat acidity are the variables that most affect the correct operation of the neural network.     

Effect of somatic cell count on proteolysis and lipolysis in pasteurized fluid milk during shelf-life storage

The general goal of this research was to provide fluid milk processors with data to enable them to estimate the economic benefits they might derive from longer fluid milk shelf-life or new marketing opportunities due to a reduction in raw milk SCC. The study objectives were: 1) to measure the time in days for pasteurized homogenized 2% milk to achieve a level of lipolysis and proteolysis caused by native milk enzymes present in milks of different somatic cell count (SCC) at 0.5 and 6 degrees C that would be sufficient to produce an off-flavor, 2) to determine whether milk fat content (i.e., 1, 2, and 3.25%) influences the level of proteolysis or lipolysis caused by native milk enzymes at 6 degrees C, and 3) to determine the time in days for milks containing 2% fat with different SCC to undergo sufficient lipolysis or proteolysis to produce an off-flavor due to the combination of the action of native milk enzymes and microbial growth at 0.5 and 6 degrees C. In experiment 1, pasteurized, homogenized milks, containing 2% fat were prepared from raw milk containing four different SCC levels from < 100,000 to > 1,000,000 cells/ml. Each of the four milks was stored at 0.5 and 6 degrees C for 61 d. In experiment 2, pasteurized, homogenized milks containing 1, 2, and 3.25% fat were prepared starting from two raw milks containing two different SCC levels, one < 100,000 and the other > 1,000,000 cells/ml. In experiment 3, pasteurized, homogenized 2% fat milks were prepared starting from raw milks containing two different SCC levels, one < 100,000 and the other > 1,000,000 cells/ml. For experiments 1 and 2, all milks were preserved with potassium dichromate to prevent microbial growth but to allow the activity of native milk proteases and lipases during storage. For experiment 3, one set of milk was preserved with potassium dichromate to prevent microbial growth but to allow the activity of native milk proteases and lipases, and a second set of milk was unpreserved during storage at 0.5 and 6 degrees C for 29 d. Based on previous work, an off-flavor due to proteolysis was detected by 50% of panelists when the decrease in casein as a percentage of true protein (CN/TP) was > 4.76%. Our data indicated (assuming 50% of consumers would detect an off-flavor when CN/TP decreases 5%) that pasteurized milk containing 2% fat would develop an off-flavor at a time long after 61 and at 54 d for the low SCC milk, and at about 54 and 19 d for the high SCC milk, at 0.5 and 6 degrees C, respectively. Previous research reported that 34% of panelists could detect an off-flavor in milk containing 2% fat due to lipolysis at a (free fatty acid) FFA concentration of 0.25 meq/kg of milk. Based on these results, it was estimated in the present study that 34% of panelists would detect an off-flavor in a 2% fat pasteurized milk with low SCC at a time long after 61 and just after 61 d at 0.5 and 6 degrees C, respectively, while for milk with high SCC, an off-flavor would be detected by 34% of panelists at slightly longer than 61 and 35 d at 0.5 and 6 degrees C, respectively. The combination of low SCC milk and low storage temperature when coupled with processing technology to produce very low initial bacteria count in fluid milk could produce fluid milk that will maintain flavor quality for more than 61 d of storage at temperatures < 6 degrees C.     

不同体细胞数原料乳对UHT贮存期间蛋白和脂肪水解的影响

不同体细胞数(21.4×104mL-1,75.8×104mL-1,118.1×104mL-1和216.2×104mL-1)原料乳生产的4组UHT乳在37℃贮存84d,对其贮存期间的蛋白水解及脂肪水解进行研究。结果表明,4组UHT乳贮存期间的蛋白水解速率无显著性差异(P>0.05),原料乳体细胞数并未对蛋白水解造成影响;4组UHT乳贮存期间的脂肪水解速率具有显著性差异(P<0.005),原料乳体细胞数与脂肪水解速率间存在极明显的正相关(R=0.9886,P<0.05)。     

体细胞数对乳的影响及其控制措施

分析了乳中体细胞升高的原因，阐述了高体细胞数对乳的组成和乳制品加工的影响，并简要的说明了降低体细胞数的控制措施。     

Effects of somatic cell count and stage of lactation on the plasmin activity and cheese-making properties of ewe milk

The experiment was conducted from March to July 2002 using 5 intensively managed flocks of Southern Italy. In each flock, 2 groups of 50 ewes were created. The groups were designated LSCC (low somatic cell count [SCC]) when their milk SCC was lower than 500,000/mL and HSCC (high SCC) when their milk SCC was higher than 1,000,000/mL. Bulk milk and whey samples were analyzed for fat, total protein, lactose, casein, and whey protein contents. Renneting properties of milk were also determined. Moisture, NaCl, and nitrogen fractions were determined in fresh cheese curds. In addition, plasmin (PL) and plasminogen (PG) activities in milk and cheese were monitored. The proteolytic activity of plasmin by urea-polyacrylamide gel electrophoresis and the white blood cell (WBC) differentials were determined. The HSCC resulted in higher pH values in milk and in higher moisture and lower fat contents in fresh cheese curds. Moreover, a lower recovery of fat and whey proteins was obtained from the HSCC than from the LSCC raw milk. The crude protein and casein contents were higher in the HSCC than in the LSCC curds during early and midlactation; an opposite trend was observed in late lactation. Plasmin and PG activities underwent more marked fluctuations in the LSCC than in the HSCC curds through lactation. The results of this experiment demonstrate that the PL activity in ewe milk is markedly influenced by the SCC, although SCC is not the only parameter for predicting PL and PG evolution in ewe milk. The LSCC milk resulted in a higher proteolytic potential of Canestrato pugliese cheese curds.     

Hard ewe's milk cheese manufactured from milk of three different groups of somatic cell counts

As ovine milk production increases in the United States, somatic cell count (SCC) is increasingly used in routine ovine milk testing procedures as an indicator of flock health. Ovine milk was collected from 72 East Friesian-crossbred ewes that were machine milked twice daily. The milk was segregated and categorized into three different SCC groups: < 100,000 (group I); 100,000 to 1,000,000 (group II); and > 1,000,000 cells/ ml (group III). Milk was stored frozen at -19 degrees C for 4 mo. Milk was then thawed at 7 degrees C over a 3-d period before pasteurization and cheese making. Casein (CN) content and CN-to-true protein ratio decreased with increasing SCC group 3.99, 3.97, to 3.72% CN, and 81.43, 79.72, and 79.32% CN to true protein ratio, respectively. Milk fat varied from 5.49, 5.67, and 4.86% in groups I, II, and III, respectively. Hard ewe's milk cheese was made from each of the three different SCC groups using a Manchego cheese manufacturing protocol. As the level of SCC increased, the time required for visual flocculation increased, and it took longer to reach the desired firmness for cutting the coagulum. The fat and moisture contents were lower in the highest SCC cheeses. After 3 mo, total free fatty acids (FFA) contents were significantly higher in the highest SCC cheeses. Butyric and caprylic acids levels were significantly higher in group III cheeses at all stages of ripening. Cheese graders noted rancid or lipase flavor in the highest SCC level cheeses at each of the sampling points, and they also deducted points for more body and textural defects in these cheeses at 6 and 9 mo.     

Differential leukocyte count method for bovine low somatic cell count milk

Whereas many differential leukocyte count methods for high somatic cell count (SCC) milk from mastitic cows are available, only a few have been developed for low SCC milk. We have developed a flow cytometric differential leukocyte count method for low SCC milk. The procedure consists of 1) 1.5 ml of diluted milk sample (30%, vol/vol dilution with PBS), 2) centrifugation, 3) leukocyte labeling with SYTO 13 and 4) flow cytometric analysis. Four major leukocyte populations can be clearly identified in the green fluorescence-side scatter dot plot: lymphocytes and monocytes (LM), polymorphonuclear neutrophils (PMN), mature macrophages (Mphi), and cells with apoptotic features based on chromatin condensation and nuclear fragmentation. The optimal processing temperature was 20 degrees C. Significant differences among samples with similar differential leukocyte counts were found. Storage of milk samples during 2 d at 7 degrees C had no effect on differential leukocyte count. Using the new method, differential leukocyte count was performed in low SCC milk samples from cows in early, mid, and late lactation. In accordance with previous studies, PMN and Mphi percentages were lower and LM percentages were higher in early lactation than in the other stages of lactation. The percentage of cells with apoptotic features was higher in early lactation than in mid and late lactation. In conclusion, a rapid, simple, accurate, and reproducible standard procedure was developed to determine the differential leukocyte count (Mphi, PMN, LM, and cells with apoptotic features) of bovine low SCC milk.     

Predicting somatic cell count standard violations based on herd's bulk tank somatic cell count. Part I: Analyzing variation

The present study examines the relationship between the bulk tank somatic cell count (SCC) mean and sigma (an estimate of variation) and the probability of exceeding a SCC standard. Daily or every other day, bulk tank SCC data were collected for 24 mo from 1,501 herds. Mean and sigma were estimated for each herd monthly and were compared between months and herd production categories using Kruskal-Wallis nonparametric ANOVA. The effect of month on bulk tank SCC mean and sigma was significant, with estimates for all summer months and most of the spring and fall months being significantly greater than estimates of mean and sigma in December 2004. Logistic regression models were developed to examine the relationship between month and herd production and the odds of a herd exceeding a SCC standard. The odds of exceeding a bulk tank SCC standard were significantly greater in the summer months and for smaller herds. A grid was constructed determining the probability of exceeding any of 5 SCC standards (200,000 to 600,000 cells/mL, step 100,000 cells/mL) in the following month, based on the mean and sigma of the past month. The violation probability grid can be used to assess the prospect of meeting quality premium goals and to proactively encourage more consistent performance in all the processes affecting bulk tank SCC.     

FossMatic5000检测牛乳体细胞

讨论了体细胞数与乳房炎的关系及体细胞数对牛乳成分及产奶量损失的影响。主要介绍了FossoMadc 5000检测方法，该方法方便快捷适用于大型牧场监测牛群健康状况。     

牛乳体细胞数的检测方法

讨论了体细胞数与乳腺炎的关系以及体细胞数对牛乳成分及产奶量损失的影响。主要介绍了4种常用的体细胞数的检测方法，即加利福尼亚细胞数测定法(CMT)，威斯康辛乳腺炎试验(WMT)，电子体细胞计数法(DHI)和直接镜检法(CMSCC)。     

Volatile composition and proteolysis in traditionally produced mature Kashar cheese

Twelve samples of raw milk mature Kashar cheese at different stages of ripening were collected from retail outlets. The average pH, moisture, fat-in-dry matter, protein, salt-in-dry matter and titratable acidity contents of the samples were 5.33, 39.39%, 45.20%, 27.33%, 6.62% and 0.65% (as lactic acid), respectively. Indices of proteolysis varied from 10.72% to 23.75% and 7.09% to 12.26% for pH 4.6-soluble and 12% trichloroacetic acid-soluble nitrogen fractions, respectively, and total free amino acid concentrations ranged from 6.36 to 36.03 mg Leu g⁻¹ of cheese. The cheeses were analysed for volatile compounds by Solid Phase Microextraction and Gas Chromatography-Mass Spectrometry (GC-MS). A total of 113 compounds were detected and identified belonging to the following chemical groups: acids (eleven), esters (sixteen), ketones (sixteen), aldehydes (six), alcohols (twenty-seven), sulphur compounds (seven), terpenes (seven) and miscellaneous compounds (twenty-three). The potential effect of each compound on the flavour profile of Kashar cheese is discussed. Acids, esters, ketones and alcohols were found at considerable levels in the samples. Kashar cheeses obtained from different retail outlets displayed some differences in terms of chemical composition, proteolysis and patterns of aroma compounds; and may be attributed to their production technologies and age-related variations.     

Predicting somatic cell count standard violations based on herd's bulk tank somatic cell count. Part II: Consistency index

The present study examines the capability of 1,501 herds in the Upper Midwest and the performance of statistical process control charts and indices as a way of monitoring and controlling milk quality on the farm. For 24 mo, daily or every other day bulk tank somatic cell count (SCC) data were collected. Consistency indices for 5 different SCC standards were developed. The indices calculate the maximum variation allowed to meet a desired SCC level at a given mean bulk tank SCC and were used to identify herds not capable of meeting a specific SCC standard. Consistency index method was compared with a test identifying future bulk tank SCC standard violators based on herds’ past violations. The performance of the consistency index test and the past violation method was evaluated by logistic regression. The comparison focused on detection probability and certainty associated with a result. For the 5 SCC levels, detection probability and certainty associated with a result ranged from 51 to 98%. Detection probability of all violators and certainty associated with a negative result was greater for the consistency index across all 5 SCC levels (by 0.7 to 7.4% and 2.1 to 5.1%, respectively). Control charts were plotted and monthly consistency indices calculated for individual farms. Charts in combination with the consistency indices would warn from 66 to 80% of the herds about an upcoming violation within 30 d before it occurred. They offer a proactive approach to maintaining consistently high milk quality. By assessing process capability and distinguishing between significant changes and random variation in bulk tank SCC, tools presented in this article encourage fact-based decisions in dairy farm milk quality management.     

Relationships between somatic cell count and intramammary infection in buffaloes

The objectives of this study were to evaluate the presence of intramammary infections (IMI) in dairy buffaloes and to examine the relationships among IMI, somatic cell counts (SCC), and milk production traits. Two farms in northern Italy were visited monthly for a complete milking season. Quarter-based milk samples were collected at each visit from 46 buffaloes. A total of 1,912 samples were assessed in this experiment. Samples were cultured for bacterial presence and were tested for SCC and percentages of milk protein and fat. In addition, daily milk yield was recorded from each buffalo. Prevalence of IMI was large; 63% of quarters were infected. No buffalo remained free from IMI throughout the course of the study. Coagulase-negative staphylococci were the most common pathogen (66% of positive samples). The SCC was distinctly greater in infected quarters; 100% of quarters with SCC >200,000 cell/mL had IMI, whereas 98% of quarters with SCC below this threshold were uninfected. The somatic cell scores (SCS) in these buffaloes were much lower than those commonly observed in dairy cattle. The mean SCS from quarters with IMI was only 2.93. The highest SCS was observed in quarters infected by streptococci. No drastic decrease in milk yield was observed among infected buffaloes relative to healthy contemporaries. The relatively low SCS and lack of a strong effect on milk yield provide evidence to discourage antibiotic treatment of buffaloes for subclinical IMI during lactation.     

Comparison of bulk-tank standard plate count and somatic cell count for Wisconsin dairy farms in three size categories

The objective of this study was to evaluate possible claims by advocates of small-scale dairy farming that milk from smaller Wisconsin farms is of higher quality than milk from larger Wisconsin farms. Reported bulk tank standard plate count (SPC) and somatic cell count (SCC) test results for Wisconsin dairy farms were obtained for February to December, 2008. Farms were sorted into 3 size categories using available size-tracking criteria: small (≤118 cows; 12,866 farms), large (119-713 cattle; 1,565 farms), and confined animal feeding operations (≥714 cattle; 160 farms). Group means were calculated (group = farm size category) for the farms’ minimum, median, mean, 90th percentile, and maximum SPC and SCC. Statistical analysis showed that group means for median, mean, 90th percentile, and maximum SPC and SCC were almost always significantly higher for the small farm category than for the large farm and confined animal feeding operations farm categories. With SPC and SCC as quality criteria and the 3 farm size categories of ≤118, 119 to 713, and ≥714 cattle, the claim of Wisconsin smaller farms producing higher quality milk than Wisconsin larger farms cannot be supported.     

Dynamics of somatic cell count patterns as a proxy for transmission of mastitis pathogens

Management of udder health is particularly focused on preventing new infections. Data from the DeLaval Online Cell Counter (DeLaval, Tumba, Sweden) may be used in forecasting to improve decision support for improved udder health management. It provides online cell counts (OCC) as a proxy for somatic cell counts from every milking at the cow level. However, these values are typically too insensitive and nonspecific to indicate subclinical intramammary infection (IMI). Our aim was to describe and evaluate use of dynamic transmission models to forecast subclinical IMI episodes using milk cultures or changes in OCC patterns over time. The latter was expressed by an elevated mastitis risk variable. Data were obtained from the dairy herd of the Norwegian University of Life Sciences (Oslo, Norway). In total, 173 cows were sampled monthly for bacteriological milk culture during a 17-mo study period and 5,330 quarter milk samples were cultured. Mastitis pathogens identified were assigned to 1 of 2 groups, Pat 1 or Pat 2. Pathogens from which a high cell count would be expected during a subclinical IMI episode were assigned to the Pat 1 group. Pathogens not in the Pat 1 group were assigned to the Pat 2 group. Staphylococcus epidermidis, Staphylococcus aureus, and Streptococcus dysgalactiae were the most common Pat 1 pathogens. Corynebacterium bovis, Staphylococcus chromogenes, and Staphylococcus haemolyticus were the most common Pat 2 pathogens. The OCC were successfully recorded from 82,182 of 96,542 milkings. The current study included 324 subclinical IMI episodes. None of the mastitis pathogens demonstrated a basic reproduction number (R₀) >1. Patterns of OCC change related to an episode of Pat 1 subclinical IMI at specificity levels of 80, 90, and 95% at sensitivity levels of 69, 59, and 48% respectively, demonstrated an R₀ >1. An existing infection was significant for transmission for several Pat 2 pathogens, but only for Staphylococcus aureus and Staphylococcus epidermidis among Pat 1 pathogens. Dynamic transmission models showed that patterns of OCC change related to an episode of Pat 1 subclinical IMI were significantly related to the same pattern occurring in susceptible cows at specificity levels of 80, 90, and 99% at sensitivity levels of 69, 48, and 8%, respectively. We conclude that changes in herd prevalence of subclinical IMI can be predicted using dynamic transmission models based on patterns of OCC change. Choice of specificity level depends on management goals and tolerance for false-positive alerts.     

Correlation between standard plate count and somatic cell count milk quality results for Wisconsin dairy producers

The objective of this study was to determine if a correlation exists between standard plate count (SPC) and somatic cell count (SCC) monthly reported results for Wisconsin dairy producers. Such a correlation may indicate that Wisconsin producers effectively controlling sanitation and milk temperature (reflected in low SPC) also have implemented good herd health management practices (reflected in low SCC). The SPC and SCC results for all grade A and B dairy producers who submitted results to the Wisconsin Department of Agriculture, Trade, and Consumer Protection, in each month of 2012 were analyzed. Grade A producer SPC results were less dispersed than grade B producer SPC results. Regression analysis showed a highly significant correlation between SPC and SCC, but the R² value was very small (0.02–0.03), suggesting that many other factors, besides SCC, influence SPC. Average SCC (across 12 mo) for grade A and B producers decreased with an increase in the number of monthly SPC results (out of 12) that were ≤25,000 cfu/mL. A chi-squared test of independence showed that the proportion of monthly SCC results >250,000 cells/mL varied significantly depending on whether the corresponding SPC result was ≤25,000 or >25,000 cfu/mL. This significant difference occurred in all months of 2012 for grade A and B producers. The results suggest that a generally consistent level of skill exists across dairy production practices affecting SPC and SCC.