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Mathematical modeling and validation of growth of Salmonella Enteritidis and background microorganisms in potato salad - One-step kinetic analysis and model development

机译:马铃薯沙拉中肠炎沙门氏菌和背景微生物生长的数学建模和验证-一步动力学分析和模型开发

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This study was conducted to examine the growth of Salmonella Enteritidis (SE) in potato salad caused by cross-contamination and temperature abuse, and develop mathematical models to predict its growth. The growth of SE was investigated under constant temperature conditions (8,10,15, 20, 25, 30, and 37 degrees C) to evaluate the effect of temperature on growth rates and lag times. Duplicated experiments were conducted. The data set from one replicate was used to develop kinetic models and determine kinetic parameters. The data from the other replicate served as an independent data set for model validation. The growth of background microorganism (BK) was also examined. One-step kinetic analysis method was used to directly construct both primary (Huang) and secondary (Ratkowsky square-root) models. Nonlinear regression was used to minimize the global residual sum of squares (RSS) for SE and BK. The results showed that both primary and secondary models can be used to analyze the growth curves, with the kinetic parameters closely matching the characteristics of SE and BK. The validation results showed that the root-mean-square error (RMSE) was only 0.40 Log CFU/g for SE and 0.66 Log CFU/g for BK, with the residual errors of predictions following Laplace and logistic distributions, respectively. This study showed that one-step kinetic analysis is a useful and efficient method for analyzing the entire data set to directly construct primary and secondary growth models and determine kinetic parameters. Since the models are validated, they can be used to predict the growth of SE and conduct risk assessment, and to predict the microbiological shelf-life of potato salad. Published by Elsevier Ltd.
机译:这项研究旨在检查由于交叉污染和温度滥用导致马铃薯沙拉中肠炎沙门氏菌(SE)的生长,并开发数学模型来预测其生长。在恒温条件(8、10、15、20、25、30和37摄氏度)下研究了SE的生长,以评估温度对生长速率和滞后时间的影响。进行重复实验。来自一个重复的数据集用于建立动力学模型并确定动力学参数。来自另一个副本的数据用作模型验证的独立数据集。还检查了背景微生物(BK)的生长。使用一步动力学分析方法直接构建初级(黄)模型和次级(Ratkowsky平方根)模型。非线性回归用于最小化SE和BK的全局残差平方和(RSS)。结果表明,主要模型和次要模型均可用于分析生长曲线,其动力学参数与SE和BK的特征紧密匹配。验证结果表明,SE的均方根误差(RMSE)仅为0.40 Log CFU / g,BK的均方根误差(RMSE)仅为0.66 Log CFU / g,预测的剩余误差分别遵循Laplace和logistic分布。这项研究表明,一步动力学分析是分析整个数据集以直接构建一级和二级生长模型并确定动力学参数的有用且有效的方法。由于模型已经过验证,因此可用于预测SE的增长并进行风险评估,并预测马铃薯沙拉的微生物货架期。由Elsevier Ltd.发布

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