胁迫温度下两株高致病性副溶血弧菌的生长差异比较分析

为了比较胁迫温度下高致病性副溶血性弧菌的生长差异，作者选用致病性副溶血性弧菌ATCC 33847（tlh+/tdh+/trh-）和F 18（tlh+/tdh+/trh-），测定其在南美白对虾上4~30 ℃的生长情况，采用修正一级模型拟合其生长曲线，并基于最优模型拟合得到的最大比生长速率（μmax）、延滞期（LT）比较分析两株菌的生长特性。结果表明，20～30 ℃时，两株副溶血性弧菌并未呈现出明显生长差异，而4～15 ℃时的生长差异较大。修正Gompertz模型为ATCC 33847和F 18在15~30 ℃的生长曲线的最优模型（R2>0.99），Baranyi模型为ATCC 33847在4~7 ℃以及F 18在7 ℃下最优模型（R2>0.935），而F 18在4 ℃和10 ℃的生长模型有待于进一步研究。15~30 ℃，F 18的μmax大于ATCC 33847，差值约从0.2 lg（cfu/mL）/h增至0.8 lg（cfu/mL）/h，ATCC 33847的LT大于F 18，差值从2.5 h变化到7 h。比较其他同类文献的μmax二级模型发现高致病性菌株存在显著性生长差异（P<0.05），且差值随着温度的升高而变大。因此，胁迫温度下相同致病性副溶血性弧菌的生长存在较大的异质性，而结合微生物实际生活环境，建议考虑建立以混合菌株为研究对象的预测模型，为风险评估提供更准确的数据基础。
The objective of this investigation was to compare the growth characteristics of highly pathogenic Vibrio parahaemolyticus at stress temperature on ready-to-eat shrimp ATCC 33847（tlh+/tdh+/trh-） and F 18（tlh+/tdh+/trh-） were inoculated on shrimp and stored at different temperatures（4~30 ℃）. The growth models were chosen to fit the growth curves and were validated by the coefficient of determination（R2），Accuracy factor（Af） and bias factor（Bf）. The growth parameters of V. parahaemolyticus from different sources were decided by maximum growth rate（μmax） and lag phase（LT）. The result showed that big difference was existed between ATCC 33847 and F 18 from 4 ℃ to 15 ℃，while similar growth from 20 ℃ to 30 ℃. The modified Gompertz model was the optimal one to describe the growth of ATCC 33847 and F 18 at 15~30 ℃（R2 > 0.99） while Baranyi model was for ATCC 33847 at 4~7 ℃ and F 18 at 7 ℃. However，the optimal model for F 18 at 4 ℃ and 10 ℃ needed to be further studied. The difference value of μmax was 0.2 ~ 0.8 Log（cfu/mL）/h between ATCC 33847 and F 18 at 15~30 ℃，and the difference value of LT was 2.5~7 h. Compared the μmax with the ones of the published，significant difference（P<0.05） could be found among the highly pathogenic，and the difference value become larger as the temperature becoming higher. Thus，large variability of V. parahaemolyticus could be found at stress temperature. What is more，the predictive models based on cocktail strains should be studied for more accurate risk assessment