巴氏消毒及冷藏温度作用下副溶血弧菌菌株失活异质性的比较

Inactivation variability of Vibrio parahaemolyticus under sterilization and storage temperature

温度是影响细菌生长与失活的关键因素,在食品生产中常用来控制致病微生物潜在的风险。但由于不同菌株间个体的差异,细菌在相同温度作用下呈现出不同的失活趋势,这种行为方式称为菌株的失活异质性,容易导致微生物风险控制的不确定性和变异性。比较了19株副溶血性弧菌(16株临床菌株和3株环境菌株)在巴氏消毒温度(65℃)及冷链温度(10℃)作用下的失活情况,并结合Weibull模型,拟合相应的失活参数(t_R值),探究了不同菌株间的失活异质性。在65℃处理条件下,19株副溶血性弧菌的t_R值介于22.62~67.23 s,VPC-1为耐热性最强菌株,而VPC-10为耐热性最弱菌株,热失活参数t_R值最适的概率分布为Normal (44.82, 12.27)。在10℃条件下,t_R值介于113.96~371.38 h,VPC-3为耐冷性最强菌株,VPC-2为耐冷性最弱菌株,冷失活参数t_R值最适的概率分布为Loglogistic (51.45,148.88,4.67)。结果表明,副溶血性弧菌的热失活和冷失活间没有显著的相关性,菌株的失活异质性广泛存在于副溶血性弧菌之中,仅基于单一菌株进行失活模型的拟合,很难描述其整体的失活趋势。同时,初步构建了菌株失活异质性的随机模型,并使用概率分布代替了传统的失活参数。

Temperature is a key factor affecting bacterial growth and inactivation, which often used to control the potential risk of pathogenic microorganisms. However, due to individual differences among bacterial strains, bacteria show different inactivation trends under the same temperature, which is called bacterial inactivation variability. The inactivation variability of 19 Vibrio parahaemolyticus was compared at pasteurization temperature(65 ℃) and cold chain temperature(10 ℃), data with the Weibull model were fitted, the inactivation parameters(t_R) were received, and the inactivation variability was explored among the different strains. Under the condition of 65 ℃, the t_R values of 19 Vibrio parahaemolyticus between 22.62-67.23 s, VPC-1 was the most heat resistant strains, VPC-10 was the least heat resistant strains, the optimum probability distribution of heat inactivation t_R values was Normal(44.82, 12.27). Under the cold inactivation condition of 10 ℃, the t_R values between 113.95-371.38 h, VPC-3 was the cold resistant strains, VPC-2 was the least cold resistant strains, the optimum probability distribution of cold inactivation t_R values was Loglogistic(51.45, 148.88, 4.67). The results showed that there was no significant correlation between thermal inactivation and cold inactivation of Vibrio parahaemolyticus, the inactivation variability of the bacterial strain was widely existed in Vibrio parahaemolyticus, and the present inactivation model was only based on a single strain, so it was difficult to describe the overall inactivation trend. At the same time, a random model of bacterial inactivation variability was preliminarily constructed, and the traditional inactivation parameters were replaced by the probability distribution.