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Growth and growth boundary model with terms for melting salts to predict growth responses of Listeria monocytogenes in spreadable processed cheese

机译:具有熔盐项的生长和生长边界模型,用于预测可涂抹干酪中李斯特菌的生长反应

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The aim of this study was to develop and validate a growth and growth boundary model with terms for melting salts to predict growth of Listeria monocytogenes in spreadable processed cheese. Cardinal parameter terms for phosphate salts and citric acid were developed in broth studies and used to expand an available growth and growth boundary model. The expanded model includes the effect of nine environmental factors (temperature, pH, a(w), lactic acid, acetic acid, citric acid, orthophosphate, di-phosphate and tri-phosphate). To generate growth data for model evaluation challenge tests with inoculated commercial (n = 10) and customized (n = 10) spreadable processed cheeses were performed. Evaluation of the new model by comparison of observed and predicted mu(max)-values resulted in a bias factor of 1.12 and an accuracy factor of 1.33 (n = 42). Prediction of growth and no-growth responses in processed cheese (n = 60) were 89% correct with 11% fail-safe and 0% fail-dangerous predictions. The developed model can be used to support product development, reformulation or risk assessment for spreadable processed cheese.
机译:这项研究的目的是开发和验证一种生长和生长边界模型,该模型具有用于熔盐的术语,以预测可涂抹干酪中李斯特菌的生长。在肉汤研究中开发了磷酸盐和柠檬酸的基本参数项,并用于扩展可用的生长和生长边界模型。扩展模型包括九种环境因素的影响(温度,pH,a(w),乳酸,乙酸,柠檬酸,正磷酸盐,二磷酸盐和三磷酸盐)。为了生成用于模型评估的生长数据,进行了接种商业化奶酪(n = 10)和定制化奶酪(n = 10)的挑战性测试。通过比较观察到的和预测的mu(max)值对新模型进行评估,得出偏差系数为1.12,准确度系数为1.33(n = 42)。加工奶酪(n = 60)中生长和无生长反应的预测正确率为89%,其中11%为故障安全预测和0%为故障危险预测。开发的模型可用于支持可涂抹干酪的产品开发,重新配方或风险评估。

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