新乡市生乳中菌落总数和金黄色葡萄球菌及β-内酰胺酶调查

目的了解新乡市规模化奶牛养殖场生乳中菌落总数、金黄色葡萄球菌和β-内酰胺酶的基线数据,为生乳安全性风险评估及国家标准更新提供资料。方法以新乡市3家规模化奶牛养殖场(A、B、C场)为观测基地,2016年5月—2017年4月每月采集2家生乳样品各29份和1份混合样品。分别按照GB 4789.2—2010《食品安全国家标准食品微生物学检验菌落总数测定》和GB 4789.10—2010《食品安全国家标准食品微生物学检验金黄色葡萄球菌检验》测定菌落总数和金黄色葡萄球菌,胶体金法测β-内酰胺酶残留。结果 3家奶牛养殖场单份样品菌落总数的中位数分别为13 000、4 450、130 000 CFU/ml,明显低于GB 19301—2010《食品安全国家标准生乳》中的限量(2×10~6CFU/ml),差异有统计学意义(P0.01),3家奶牛养殖场的菌落总数之间差异有统计学意义(P0.05)。各奶牛养殖场单份样品的菌落总数与混合样品比较,仅B场差异有统计学意义(P0.05)。7~8月菌落总数较高。3家奶牛养殖场样品中金黄色葡萄球菌检出率分别为1.1%(4/360)、16.7%(30/180)和0.0%(0/180),计数结果为50~42 000 CFU/ml。3家奶牛养殖场的所有样品中均检出β-内酰胺酶,检出率为6.1%~10.6%。结论 GB 19301—2010生乳菌落总数限量远高于新乡市实际情况,建议修订标准;个别奶牛养殖场生乳中存在一定程度的金黄色葡萄球菌污染,需加强监管;筛查出的β-内酰胺酶需进一步鉴定其来源,同时要加强对β-内酰胺酶检测方法的研究。     

瓶装天然矿泉水饮料的微生物：国家标准和微生物超标问题

对在市场采集的33份瓶装天然矿泉水进行了微生物卫生指标监测,发现细菌菌落总数最高含量为3.8×10~4CFU/ml,最低<10CFU/ml,平均390CFU/ml,按国家饮料的菌落总数标准有76%的样品不合格;大肠菌群有9%的样品不合格,33份样品致病菌指标全部合格,发现不同季节样品的菌落总数含量有所不同;对不同年份的样品微生物含量情况作了分析。还对瓶装天然矿泉水的微生物存在特性,卫生学意义及国标的合适性等进行了讨论。     

火腿肠加工过程中微生物风险研究

目的开展火腿肠加工过程中微生物污染风险研究,掌握卫生指示菌、主要食源性致病菌的分布特征和污染途径,为火腿肠加工过程中微生物污染风险控制提供依据。方法 2015—2017年对4家企业的712份产品相关样品(原辅料、中间产品和终产品)和环境样品(包括生产用水、空气沉降菌、人员、工具等)进行监测,选择传统分离培养方法对卫生指示菌和主要食源性致病菌进行检验,并对沙门菌进行血清学鉴定。结果原辅料中菌落总数10~5 CFU/g和大肠菌群10~3 CFU/g的样品比例分别为33.00%(33/100)和29.00%(29/100);中间产品中菌落总数10~5 CFU/g和大肠菌群10~3 CFU/g的样品比例分别为62.86%(66/105)和36.19%(38/105);终产品未检出菌落总数10~4 CFU/g的样品,大肠菌群均10 CFU/g。结论火腿肠加工过程存在微生物污染风险,本研究对掌握火腿肠加工过程的污染分布,确定关键控制点,为制定相关生产质量管理规范、确保终产品的食品安全具有重要意义。     

中国上海即食牛肉制品微生物污染情况的随机研究（英文）

本文研究了上海即食牛肉样品中微生物的污染情况。从上海的5个国际超市和5个街头摊贩随机抽取20份即食牛肉样品,采用培养法分析样本的菌落总数,并从每份样品中随机抽取10个菌落采用16S r RNA基因测序法对细菌进行分类鉴定。超市牛肉样本的菌落总数平均为(1.08±1.73)×108 CFU/g,街头摊贩样本菌落总数平均为(2.84±8.49)×10~8 CFU/g,二者在统计学上无显著性差异(p0.05),但均高于国家标准(≤8×104 CFU/g)。所有样本均未检出能引起严重食物中毒的病原体,但检测到种类多样且与食物中毒、尿路感染、肺炎、乳腺炎以及菌血症等疾病相关的多种病原菌操作分类单元(OTU)。结果表明,即食食物是一个经常被忽视的致病微生物的储存库,我国应加强对即食食物的卫生管理,从而降低即食食品引起的食源性疾病传播几率。     

月饼中菌落总数测定结果不确定度的评估

本文旨在建立月饼中菌落总数检测结果的不确定度的评定方法。方法:依据GB4789.2-2016《食品安全国家标准食品微生物菌落总数测定》进行测定和结果判断,再根据JJF1059.1-2012《测量不确定度评定与表示的规定》以及参考其他相关文献,对本实验室菌落测定过程引入的不确定度进行分量评定。结果:依据所采用的方法,得出两份样品结果分别为(1.6~1.8)×10~3CFU/g(k=2)和(2.6~2.8)×10~3CFU/g(k=2)。结论:本文所采用的评定方法,是对月饼中菌落总数加测结果不确定度的评定方法,也可以应用于日常工作中其他产品的菌落总数不确定度评定。     

茯砖茶中金花菌菌落计数方法的优化

金花菌数量是审评茯砖茶产品质量好坏的关键指标。以国标为依据,采用单因素试验和响应面分析法优化茯砖茶中金花菌菌落总数计数方法。结果表明,样品前处理的最佳条件为:筛子目数80目,无菌稀释液为0.85%NaCl溶液,振摇时间50 min。该条件下,金花菌菌落总数为(50.75±1.1)×10~5CFU/g。优化方法得到的金花菌菌落总数是国标方法的5倍左右。     

牛乳中体细胞数与乳成分和部分理化性质的相关性研究

对呼和浩特郊区一牧场30头荷斯坦乳牛进行6个月单个采样,共得452个有效样本,检测乳样包括:体细胞数、脂肪、蛋白质、乳糖、总固形物、细菌总数、比重、黏度、电导率、氯糖数、滴定酸度和pH值。结果表明,蛋白质质量分数与牛乳中的体细胞数(Somatic CellCount,SCC)SCC呈显著正相关(P<0.05);乳糖含量与SCC呈显著负相关(P<0.001);脂肪、总固形物质量分数、电导率、氯糖数与SCC呈极显著正相关(P<0.001)。细菌总数、比重、黏度、滴定酸度、pH值与SCC的相关性不显著。从5～10月,乳中体细胞数有逐渐降低趋势,9月骤然升高,其中5月和9月样品平均体细胞数较高,分别为78×104 mL-1和96×104 mL-1。10月份样品平均体细胞数最低为28×104 mL-1。     

绿豆芽生产过程中微生物的生长、分布及消毒剂处理效果评价

研究绿豆芽生产过程中微生物的生长情况及种类分布,采用2种不同类型的消毒剂进行处理及效果评价,并用16SrRNA方法对豆芽中主要微生物进行鉴定。结果表明,成品绿豆芽中的微生物总量为5.8×10~7 CFU/g,微生物的种类主要为克雷伯氏菌(Klebsiella)、不动杆菌(Acinetobacter)、肠杆菌(Enterobacter)和假单胞菌属(Pseudomonas)等。有效氯浓度1 000~3 000 mg/L的NaClO处理芽菜30 min,菌落总数分别降至1.2×10~7~1.0×10~5 CFU/g,而直接处理单独培养的模型对照组则平均降至2.3×10~4~1.0×10~3 CFU/mL。25 mg/L AgNO_3可使菌落总数降至3.8×10~5 CFU/g,消毒效果同样差于模型对照组。扫描电子显微镜观察分析芽菜表面微生物可形成明显可见的生物膜,显著强化了细菌对消毒剂的耐受力。     

平板计数法与纸片法检测食品微生物菌落总数的比较研究

目的找出平板倾注法与纸片法2种检测食品菌落总数方法之间的相关性,判断2种方法有无显著性差异。方法样品处理按GB 4789.2-2016《食品安全国家标准食品微生物学检验菌落总数测定》进行处理,平板倾注法取样品稀释液接种平皿倾注琼脂、纸片法取样品稀释液接种细菌计数纸片。用上述2种方法对市场上随机抽取30份饼干及糕点和2份质控样品进行菌落总数检测,最后做统计学分析(t-test)。结果倾注法与纸片法检测结果无显著差异(P0.05)且对不同菌落数区间(0～100CFU/g、100～1000CFU/g、1000～10000 CFU/g)都适用。2份质控样品评价结果为满意。结论纸片法可用于食品菌落总数的快速检测,缩短检测流程,提高工作效率。     

菌落总数质控样的制备及其在能力验证中的应用

目的制备均匀性和稳定性符合能力验证要求的质控样品,并将质控样品用于菌落总数的能力验证。方法本次考核样品由高浓度(3×10~4 CFU)、低浓度(1×10~4 CFU)2个样品组成,随机抽取质控样品进行稳定性及均匀性的检验,对其进行评价。本设计通过对实验室进行随机分组来发放样品,防止能力验证活动中各实验室进行串通。结果 65家实验室参加本次考核活动,整体满意率为80%,本次能力验证可以真实地反映参试单位的检测水平。结论本次菌落总数能力验证样品可用于质控样品使用。     

对GB 19301-2010《生乳》中蛋白质和菌落总数的指标验证和建议

目的完成"GB 19301-2010《生乳》跟踪评价项目"。方法 对陕西省7家乳企使用的原料生乳进行了7次采样,样本量为111份,其中夏季51份,冬季60份,采用GB 5009.5和GB 4789.2分别对样本中的蛋白质和菌落总数进行了测定。结果 所有样本的蛋白质含量都是合格的,冬季、夏季生乳蛋白质含量分别均不低于3.6 g/100 g、2.8 g/100 g,其中16%的夏季生羊乳蛋白质检测值为2.8 g/100 g,是"生乳蛋白质指标"的界限值。所有样本中,只有夏季4份样本的菌落总数超过了2.0×106CFU/m L的指标界限值,冬季、夏季生乳菌落总数平均值分别低于1.0×105CFU/m L、1.0×106CFU/m L。结论 GB 19301规定的生乳蛋白质、菌落总数指标值是科学合理的,建议将夏季生羊乳的蛋白质指标修订为≥2.6 g/100 g。     

Optimally achieving milk bulk tank somatic cell count thresholds

High somatic cell count in milk leads to reduced shelf life in fluid milk and lower processed yields in manufactured dairy products. As a result, farmers are often penalized for high bulk tank somatic cell count or paid a premium for low bulk tank somatic cell count. Many countries also require all milk from a farm to be lower than a specified regulated somatic cell count. Thus, farms often cull cows that have high somatic cell count to meet somatic cell count thresholds. Rather than naïvely cull the highest somatic cell count cows, a mathematical programming model was developed that determines the cows to be culled from the herd by maximizing the net present value of the herd, subject to meeting any specified bulk tank somatic cell count level. The model was applied to test-day cows on 2 New York State dairy farms. Results showed that the net present value of the herd was increased by using the model to meet the somatic cell count restriction compared with naïvely culling the highest somatic cell count cows. Implementation of the model would be straightforward in dairy management decision software.     

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.     

Impedimetric Determinaton of Total, Mesophilic and Psychrotrophic Zounts in Raw Milk

A rapid impedimetric determination for total, mesophilic and psychrotrophic counts in raw milk showed correlations (between impedance detection times and standard plate counts) of -0.96, -0.95, and 0.96, respectively. Mesophiles were most often seen as the predominant population, the impedimetric method allowed for these samples containing above 1.0 × 10⁵ CFU/ml to be screened out within 4 hr. Psychrotrophic levels of 1 × 10⁵ CFU/ml and above were screened within 21 hr, while total concentration of samples containing above 1 × 10⁵ CFU/ml were screened within 16 hr.     

Somatic cell recovery by microfiltration technologies: A novel strategy to study the actual impact of somatic cells on cheese matrix

The actual impact of the somatic cells in the dairy technology is still ill-defined, because the increase in milk somatic cell count, usually correlated with mastitis factors, impairs the raw milk composition, through mainly unwanted proteolysis and lipolysis. This study used microfiltration technologies for recovering high quantity of somatic cells and for clarifying their role in cheese quality. Three series of Swiss-type cheeses were manufactured by adding 0 (control), 4 × 10⁵ and 9 × 10⁵ somatic cells mL⁻¹. These cells were traced for the first time during the cheese making process by using adapted flow cytometry and real-time quantitative PCR. Proteolysis and lipolysis indices were measured throughout ripening time. Only a weak increase in lipolysis (+28%) and proteolysis (+8%) was observed in the highest somatic cell count cheese, despite 73% of the cells trapped within the cheeses. Our approach gives a new view of somatic cell role in cheese milk alteration.     

Short communication: Repeatability of differential goat bulk milk culture and associations with somatic cell count, total bacterial count, and standard plate count

The aims of this study were to assess how different bacterial groups in bulk milk are related to bulk milk somatic cell count (SCC), bulk milk total bacterial count (TBC), and bulk milk standard plate count (SPC) and to measure the repeatability of bulk milk culturing. On 53 Dutch dairy goat farms, 3 bulk milk samples were collected at intervals of 2 wk. The samples were cultured for SPC, coliform count, and staphylococcal count and for the presence of Staphylococcus aureus. Furthermore, SCC (Fossomatic 5000, Foss, Hillerød, Denmark) and TBC (BactoScan FC 150, Foss) were measured. Staphylococcal count was correlated to SCC (r = 0.40), TBC (r = 0.51), and SPC (r = 0.53). Coliform count was correlated to TBC (r = 0.33), but not to any of the other variables. Staphylococcus aureus did not correlate to SCC. The contribution of the staphylococcal count to the SPC was 31%, whereas the coliform count comprised only 1% of the SPC. The agreement of the repeated measurements was low. This study indicates that staphylococci in goat bulk milk are related to SCC and make a significant contribution to SPC. Because of the high variation in bacterial counts, repeated sampling is necessary to draw valid conclusions from bulk milk culturing.     

The effect of season on somatic cell count and the incidence of clinical mastitis

Bulk milk somatic cell count (BMSCC), individual cow somatic cell count (ICSCC), and incidence rate of clinical mastitis (IRCM) are all udder health parameters. So far, no studies have been reported on the effect of season on BMSCC, IRCM, and ICSCC in the same herds and period over multiple years. The objectives of this study were to determine the seasonal pattern over a 4-yr period of 1) BMSCC, 2) elevated ICSCC, 3) IRCM, and 4) pathogen-specific IRCM. Bulk milk somatic cell count, ICSCC, and pathogen-specific clinical mastitis data were recorded in 300 Dutch dairy farms. For the analyses of BMSCC, ICSCC, and IRCM, a mixed, a transitional, and a discrete time survival analysis model were used, respectively. Sine and cosine were included in the models to investigate seasonal patterns in the data. For all parameters, a seasonal effect was present. Bulk milk somatic cell count peaked in August to September in all 4 years. The probability of cows getting or maintaining a high ICSCC was highest in August and May, respectively. Older and late-lactation cows were more likely to develop or maintain a high ICSCC. Incidence rate of clinical mastitis was highest in December to January, except for Streptococcus uberis IRCM, which was highest in August. Totally confined herds had a higher Escherichia coli IRCM in summer than in winter. Compared with the major mastitis pathogens, the seasonal differences in IRCM were smaller for the minor pathogens. Distinguishing between Strep. uberis, Streptococcus dysgalactiae, Streptococcus agalactiae, and other streptococci is essential when identifying Streptococcus spp. because each of them has a unique epidemiology. Streptococcus uberis IRCM seemed to be associated with being on pasture, whereas E. coli IRCM was more housing-related.     

Chemical composition,adulteration, total microbial load,and heavy metal in raw milk samples collected from dairy farms and urban areas in Lahore District,Pakistan

The present study evaluated raw milk samples from dairy farms and urban areas in Lahore District, Pakistan, for the measurement of chemical composition, presence of adulterants, total microbial, and heavy metals loads. Measurements of chemical composition and physicochemical properties of the raw milk samples collected from urban areas showed the following mean values of 2.76 ± 0.29, 2.62 ± 0.18, 1.27 ± 0.08, 7.13 ± 0.30, 6.46 ± 0.26, and 0.47 ± 0.02 for lactose, fat, protein, total solids, pH, and ash, respectively, while milk samples collected from dairy farms achieved the mean values of 4.82 ± 0.32, 5.02 ± 0.39, 3.36 ± 0.18, 13.19 ± 0.66, 6.67 ± 0.27, and 0.75 ± 0.05 for lactose, fat, protein, total solids, pH, and ash, respectively. Mean values of total plate count were 5.21 ± 0.28 CFU/ml for urban areas raw milk samples and 4.67 ± 0.27 CFU/ml for the dairy farm milk samples. Heavy metal mean concentrations for Pb, Cd, Cu, Zn, and Ni in urban areas milk samples from milk shop were 0.024 ± 0.005, 0.021 ± 0.006, 1.22 ± 0.25, 2.42 ± 0.57, and 0.044 ± 0.006 ppm, respectively. Conclusively, strategies should be adopted in order to prevent the heavy metal contamination in milk to further minimizes the health risks associated with heavy metal consumption.     

Influences of somatic cell count on milk composition and dairy farm profitability

The objective of this paper was to investigate seasonal variations in bulk somatic cell totals and milk composition, evaluate the influence of somatic cell count (SCC) on milk fat and protein content and determine the effects of SCC on dairy farm profitability. A total of 1440 samples were analysed. Data were obtained by randomly collecting five samples of bulk tank milk from each of 24 dairy farms every month from April 2008 to March 2009. Milk was analysed for titratable protein, fat content and SCC (direct microscopic cell count). The highest total bulk SCCs were observed during autumn and winter. Conversely, higher levels of milk fat and protein were generated during spring and summer. A significant negative correlation was noted between SCC and milk composition, daily milk yield and milk returns. By logarithmic function, a significant negative relationship was observed between SCC and milk composition or milk returns. In conclusion, this study demonstrates that the SCC is a useful tool for judging dairy farm profit and milk quality.     

Short communication: Microbiological quality of raw cow milk and its association with herd management practices in Northern China

Contamination of raw milk with bacterial pathogens is potentially hazardous to human health. The aim of this study was to evaluate the total bacteria count (TBC) and presence of pathogens in raw milk in Northern China along with the associated herd management practices. A total of 160 raw milk samples were collected from 80 dairy herds in Northern China. All raw milk samples were analyzed for TBC and pathogens by culturing. The results showed that the number of raw milk samples with TBC <2 × 10⁶ cfu/mL and <1 × 10⁵ cfu/mL was 146 (91.25%) and 70 (43.75%), respectively. A total of 84 (52.50%) raw milk samples were Staphylococcus aureus positive, 72 (45.00%) were Escherichia coli positive, 2 (1.25%) were Salmonella positive, 2 (1.25%) were Listeria monocytogenes positive, and 3 (1.88%) were Campylobacter positive. The prevalence of S. aureus was influenced by season, herd size, milking frequency, disinfection frequency, and use of a Dairy Herd Improvement program. The TBC was influenced by season and milk frequency. The correlation between TBC and prevalence of S. aureus or E. coli is significant. The effect size statistical analysis showed that season and herd (but not Dairy Herd Improvement, herd size, milking frequency, disinfection frequency, and area) were the most important factors affecting TBC in raw milk. In conclusion, the presence of bacteria in raw milk was associated with season and herd management practices, and further comprehensive study will be powerful for effectively characterizing various factors affecting milk microbial quality in bulk tanks in China.