Details

Autor(en) / Beteiligte

• Abe, Hiroki
• Kawasaki, Susumu

Titel

Modeling strain variability in Campylobacter jejuni thermal inactivation by quantifying the number of strains required

Ist Teil von

• International journal of food microbiology, 2024-04, Vol.414, p.110618-110618, Article 110618

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2024

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• 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. •Thermal death curves were estimated from 29 strains of Campylobacter jejuni at 55°C.•The most probable curve method reduced uncertainty in estimating concentrations.•The multivariate normal (MVN) distribution was applied to describe strain variability.•Trends in MVN predictions were investigated according to the number of reference strains.•Having more than 10 reference strains could stably predict strain variability.