Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling
Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling
复制标题
DOI：
10.3389/fmicb.2019.02239
复制
发表时间：
2019-09-25
影响因子：
5.2
通讯作者：
Koutsoumanis, Konstantinos
中科院分区：
文献类型：
--
作者：
Koyama, Kento;Aspridou, Zafiro;Koutsoumanis, Konstantinos
不确定性分析是识别科学知识的局限性并评估其对科学结论的影响的过程。在微生物风险评估的背景下，预测的微生物行为的不确定性可能是总体不确定性的重要组成部分。提供病原体水平点估计的传统确定性建模方法无法量化预测的不确定性。本研究的目的是使用贝叶斯统计模型来描述预测的鼠伤寒沙门氏菌 DT104 微生物热灭活的不确定性。使用从 ComBase 数据库 (www.combase.cc) 获得的一组在 9 个不同温度条件下将水活度调整为 0.75 的肉汤热灭活数据。对数线性微生物灭活被用作主要模型，而对于二次建模，假设灭活率的对数与温度之间存在线性关系。为了进行比较，数据采用两步回归和全局贝叶斯回归进行拟合。模型参数的后验分布用于预测沙门氏菌热灭活。模型参数的联合后验分布的组合允许将细胞密度随时间、总还原时间和失活率预测为不同时间和温度条件下的概率分布。例如，对于在 65 摄氏度下消除约 10(7) CFU/ml 的沙门氏菌种群所需的时间，模型预测的时间分布中位数为 0.40 分钟，第 5 个和第 95 个百分位数分别为 0.24 和 0.60 分钟。模型的验证表明，它可以成功描述预测的热失活的不确定性，大多数观测数据都在模型的 95% 预测区间内。与两步回归相比，全局回归方法的预测不确定性较小。开发的模型可用于量化基于风险的加工设计以及风险评估研究中热失活的不确定性。
Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. In the context of microbial risk assessment, the uncertainty in the predicted microbial behavior can be an important component of the overall uncertainty. Conventional deterministic modeling approaches which provide point estimates of the pathogen's levels cannot quantify the uncertainty around the predictions. The objective of this study was to use Bayesian statistical modeling for describing uncertainty in predicted microbial thermal inactivation of Salmonella enterica Typhimurium DT104. A set of thermal inactivation data in broth with water activity adjusted to 0.75 at 9 different temperature conditions obtained from the ComBase database (www.combase.cc) was used. A log-linear microbial inactivation was used as a primary model while for secondary modeling, a linear relation between the logarithm of inactivation rate and temperature was assumed. For comparison, data were fitted with a two-step and a global Bayesian regression. Posterior distributions of model's parameters were used to predict Salmonella thermal inactivation. The combination of the joint posterior distributions of model's parameters allowed the prediction of cell density over time, total reduction time and inactivation rate as probability distributions at different time and temperature conditions. For example, for the time required to eliminate a Salmonella population of about 10(7) CFU/ml at 65 degrees C, the model predicted a time distribution with a median of 0.40 min and 5th and 95th percentiles of 0.24 and 0.60 min, respectively. The validation of the model showed that it can describe successfully uncertainty in predicted thermal inactivation with most observed data being within the 95% prediction intervals of the model. The global regression approach resulted in less uncertain predictions compared to the two-step regression. The developed model could be used to quantify uncertainty in thermal inactivation in risk-based processing design as well as in risk assessment studies.