Analysis and probabilistic simulation of Listeria monocytogenes inactivation in cooked beef during unsteady heating

Improvement of quantitative microbiological risk assessment (QMRA) methodology through integration with gaenetic data

Quantitative microbiological risk assessment (QMRA) methodology aims to estimate and describe the transmission of pathogenic microorganisms from animals and food to humans. In microbiological literature, the availability of whole genome sequencing (WGS) data is rapidly increasing, and incorporating this data into QMRA has the potential to enhance the reliability of risk estimates. This study provides insight into which are the key pathogen properties for incorporating WGS data to enhance risk estimation, through examination of example risk assessments for important foodborne pathogens: Listeria monocytogenes (Lm), Salmonella, Campylobacter and Shiga toxin-producing Escherichia coli. By investigating the relationship between phenotypic pathogen properties and genetic traits, a better understanding was gained regarding their impact on risk assessment. Virulence of Lm was identified as a promising property for associating different symptoms observed in humans with specific genotypes. Data from a genome-wide association study were used to correlate lineages, serotypes, sequence types, clonal complexes and the presence or absence of virulence genes of each strain with patient's symptoms. We also investigated the effect of incorporating WGS data into a QMRA model including relevant genomic traits of Lm, focusing on the dose-response phase of the risk assessment model, as described with the case/exposure ratio. The results highlighted that WGS studies which include phenotypic information must be encouraged, so as to enhance the accuracy of QMRA models. This study also underscores the importance of executing more risk assessments that consider the ongoing advancements in OMICS technologies, thus allowing for a closer investigation of different bacterial subtypes relevant to human health.

Analysis and mathematical modeling of the survival kinetics of Staphylococcus aureus in raw pork under dynamic and static temperature conditions

The incidence of frequent foodborne disease outbreaks due to Staphylococcus aureus contamination necessitates the urgent searching for effective methods to monitor S. aureus. This research aims to construct model with a dynamic survival curve and some static growth curves to predict the behavior of S. aureus in raw pork. Lack of research about S. aureus kinetics in pork under fluctuating temperature conditions across freezing and thawing necessitates this study. One-step analysis was used to determine the model parameters, which was more efficient than conventional model analysis with two steps. The results of kinetic analysis showed that Tmin (minimum growth temperature) was 6.85°C, which is close to the estimated values in previous reports. Subsequently, validation results indicated the integrated model can accurately predict the behavior of S. aureus regardless of isothermal or nonisothermal conditions with the root-mean-square errors (RMSE < 0.44 log CFU/g, 73.9% of the errors of prediction falls within ±0.5 log CFU/g), accuracy factors Af and bias factors Bf were both close to 1. This work may offer an effective method for the assessment of microbial security related to S. aureus in pork.