Abstract：
There is a limited understanding of the survival responses of Campylobacter jejuni during thermal processing, which must be investigated for appropriate risk assessment and processing. Therefore, we aimed to elucidate the survival response of C. jejuni and develop a predictive model considering strain variability and uncertainty, which are important for quantitative microbial risk assessment (QMRA) or risk-based processing control measures. We employed the most probable curve (MPC) method to consider the uncertainty in cell concentrations. Further, the multivariate normal (MVN) distribution served as a model for strain variability in bacterial survival behavior. The prediction curves from the MVN successfully captured the parameter variability of the most probable curves of each strain. More than ten reference strains effectively described the strain variability in parameters using the MVN distribution. The findings indicated that, with sufficient strain data, the MVN could estimate the strain variability, including unknown strains. The multi-level model for strain variability can potentially become a specialized tool for QMRA and risk-based processing controls. The combined approach of MPC and MVN provides valuable insights into strain variability, emphasizing the importance of accounting for variability and uncertainty in predictive models for QMRA and risk-based processing control measures.
摘要：
对空肠弯曲菌在热加工过程中的生存反应了解有限，必须对其进行调查以进行适当的风险评估和处理。因此,我们旨在阐明空肠弯曲杆菌的生存反应，并建立一个考虑应变变异性和不确定性的预测模型，这对于定量微生物风险评估（QMRA）或基于风险的处理控制措施很重要。我们采用最可能曲线（MPC）方法来考虑细胞浓度的不确定性。Further,多元正态（MVN）分布可作为细菌生存行为中菌株变异性的模型。来自MVN的预测曲线成功地捕获了每个应变的最可能曲线的参数变异性。十多个参考应变使用MVN分布有效地描述了参数中的应变变异性。调查结果表明,有足够的应变数据，MVN可以估计应变变异性，包括未知菌株。应变变异性的多级模型可能成为QMRA和基于风险的处理控制的专用工具。MPC和MVN的组合方法为菌株变异性提供了有价值的见解，强调在QMRA和基于风险的处理控制措施的预测模型中考虑可变性和不确定性的重要性。
