金枪鱼生鱼片中优势菌群生长预测模型构建与验证

Predictive model development and validation of growth of dominant background microflora on raw tuna

中文关键词:  金枪鱼生鱼片  优势菌群  生长  预测模型  一步法

英文关键词:raw tuna  dominant microflora  growth  predictive model  one-step kinetic analysis

中文摘要:

      目的 考察不同温度条件下优势菌群的接种浓度对其生长的影响, 构建并验证相关数学模型。方法将分离的6株优势生长菌群分别按低浓度2.5~3.0 logCFU/g和高浓度4.5~5.0 logCFU/g混合接种至无菌金枪鱼样品中, 并于恒定温度8~30 ℃培养, 测定其生长曲线。采用一步法对高、低浓度接种的菌群生长数据进行拟合分析, 同步构建初级模型(Baranyi模型)和二级模型(Huang Square-Root模型), 并通过另设的3组波动温度条件下的优势菌群生长实验对模型进行验证。结果 优势菌群的接种浓度对其生长速率无显著影响; 通过一步法对2种接种状态下优势菌群生长数据的合并分析, 估计得出金枪鱼中优势菌群的最大生长浓度为 9.67 logCFU/g, 模型的均方根误差(root mean square error, RMSE)0.53 logCFU/g; 3组波动温度验证试验的RMSE值介于0.22~0.46 logCFU/g。结论 本研究构建的预测模型可用于金枪鱼生鱼片等产品中优势腐败菌的生长预测及货架期评估。

英文摘要:

      Objective To investigate the influence of the inoculum concentration of the dominant microflora on its growth under different temperature conditions, and to construct and verify the relevant mathematical models. Methods The 6 isolated dominant growth bacterial groups were mixed and inoculated into sterile tuna samples at low concentration 2.5?3.0 logCFU/g and high concentration 4.5?5.0 logCFU/g, cultured at a constant temperature 8?30 ℃, then its growth curve was determined. Then the growth data of the bacterial population inoculated with high and low concentrations were fit and analyzed by one-step method, and the primary model (Baranyi model) and secondary model (Huang Square-Root model) were constructed simultaneously. The model was validated by the growth experiment of dominant microflora under 3 groups of fluctuating temperatures. Results The inoculation concentration of dominant microflora had no significant effect on its growth rate. And one-step approach was used to analyze the combined growth data of 2 different inoculation concentrations of dominant microflora in raw tuna, the maximum cell density of dominant microflora in raw tuna was estimated to be 9.67 logCFU/g, while the root-mean-square errors (RMSE) of the model was 0.53 logCFU/g. The RMSE values of the 3 fluctuating temperature verification tests were 0.22?0.46 logCFU/g. Conclusion This models developed in this study can be used to predict the growth of dominant spoilage bacteria in raw tuna and shelf life evaluation.