食品科学 ›› 2005, Vol. 26 ›› Issue (12): 118-123.

• 基础研究 • 上一篇    下一篇

三种致病菌在鲜榨苹果汁中的生存/死亡概率预测模型

 李军, 汪政富, 葛毅强, 胡小松   

  1. 中国检验检疫科学研究院,中国农业大学食品科学与营养工程学院,河北科技师范学院食品工程系
  • 出版日期:2005-12-15 发布日期:2011-10-01

Surviving Probability Modeling on the Growthno-growth Interface of Escherichia coli, Salmonella enterica and Staphylococcus aureus Henrici in Fresh Apple Juices

 LI  Jun, WANG  Zheng-Fu, GE  Yi-Qiang, HU  Xiao-Song   

  1. 1.Chinese Academy of Inspection and Quarantine;2.Department of Food Engineering, Hebei Normal University of Science and Technology;3. College of Food Science and Nutritional Engineering, China Agricultural University
  • Online:2005-12-15 Published:2011-10-01

摘要: 采用三因素五水平中心组合试验设计(CCD),应用二值变量描述了大肠杆菌、沙门氏菌和金黄色葡萄球菌等致病菌在不同温度、pH和水分活度下的生存状况,通过Logistic回归过程建立了生存概率与生长抑制因子之间关系的数学预测模型,确定了其在鲜榨苹果汁中生存的限制条件,同时分析了温度、pH和水分活度在生长临界条件下的协同作用。经过实验验证,大肠杆菌、沙门氏菌、金黄色葡萄球菌的生存概率模型的预测结果与实际测定结果基本一致,在鲜榨苹果的水分活度下(Aw=0.98左右),生存概率p=0.1时的等值线可以作为细菌生存的临界线,从而确定鲜榨苹果汁中致病菌的生存临界条件。

关键词: 鲜榨苹果汁, 致病菌, 生存/死亡概率预测模型

Abstract: By means of three five-level variables for Central Composite Design (CCD), the growth and no growth situations of three strains, Escherichia coli, Salmonella enterica and Sarcina aurea Henrici under different conditions of temperature, pH and water activity (Aw) were studied with two-valued variables. Three mathematical models to predict the relations between the surviving probability and the growth control factors were established with Logistic Regression processing, and with the models the constraint conditions of bacteria survived in fruit juices were determined, and the coordinated effects of pH, Aw and temperature under critical conditions were analyzed. The predicted outcomes with the predicting models agreed with the experimental results for the above mentioned three bacteria. The surviving probability isoline under the conditions of 0.98 Aw with p=0.1 Could be used as a critical line of the bacteria surviving critical conditions to identify the pathagenic bacteria in the fresh apple juices.

Key words: fresh apple juice, pathogenic bacteria, growthno-growth probability predictive model