摘要
为研究市售猪肉中沙门氏菌的生长规律,从3种血清型的沙门氏菌分离株中各随机挑选1株,制成混合菌液(终浓度为103~104CFU/mL),并对新鲜市售猪肉进行人工污染,建立不同恒温条件下沙门氏菌的生长动力模型。通过内、外部实验对模型进行验证,结果显示,所建立的生长预测模型可靠性高,可以归纳出猪肉中沙门氏菌的生长规律。此外,根据消费者从超市购买新鲜猪肉到回家贮藏的过程中温度变化的实际情况,以"等效生长时间"理论建立了波动温度下猪肉中沙门氏菌的生长预测模型。实验验证结果显示,该模型可准确地反映出波动温度下猪肉中沙门氏菌的生长规律。结果提示,消费者在购买猪肉后应尽快食用或低温保存,以降低因沙门氏菌增殖而增加的食品安全风险。
Abstract： In order to figure out the growth of Salmonella in fresh pork, the mentioned Salmonella strainswere used to make a mixture （103- 104CFU/ mL） and artificially contaminate the fresh pork samples.Firstly, the growth dynamics models of Salmonella at different constant temperatures were established.After the internal and external validation, the established growth prediction model was proved highly reli-able which can be used to predict the growth of Salmonella in pork. In addition, the change of storagetemperature during the fresh pork bought by customers was simulated. According to the equivalent growthtime theory, the growth prediction model of Salmonella in pork under the fluctuating temperature wasestablished. After validation of the reliability, the growth of Salmonella under fluctuating temperaturesexactly drew from the model. The results demonstrated that the pork should be consumed or preserved atthe low temperature as soon as possible to reduce the high risk of food safety owing to the growth ofSalmonella.
作者
殷玉洁
倪培恩
刘丹蕾
张菊梅
吴清平
王大鹏
YIN Yujie;NI Pein;LIU Danlei;ZHANG Jumei;WU Qingping;WANG Dapeng(Department of Food Science and Technology, School of Agriculture and Biology,Shanghai Jiao Tong University, Shanghai 200240, China;Guangdong Institute of Microbiology / State Key Laboratory of Applied Microbiology Southern China /Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application /Guangdong Open Laboratory of Applied Microbiology, Guangzhou 510070, China)
出处
《食品科学技术学报》
CAS
北大核心
2018年第3期33-39,共7页
Journal of Food Science and Technology
基金
国家重点研发计划项目(2017YFF0210200)
上海市科技兴农重点攻关项目(沪农科攻字(2015)第4-5号).
关键词
猪肉
沙门氏菌
预测微生物学
生长动力模型
波动温度
pork
Salmonella
predictive microbiology
growth prediction model
fluctuating temperature
