The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

Modelling and validation of the effects of amino group concentrations in food on the growth of Escherichia coli

Predictive models for bacterial growth developed on the basis of experimental data obtained from culture media often yield different results from observations in actual foods. Although this discrepancy may be due to differences in compositional characteristics, food structure, and other factors, the impacts on bacterial behaviour have not yet been quantified and modelled mathematically. This study first aimed to quantify the effects of amino group concentrations on the growth kinetics of Escherichia coli. A predictive model incorporating the effect of the amino group concentration was subsequently developed and its potential for improving prediction accuracy in foods was verified. The growth kinetics of E. coli ATCC 25922 were examined at 37 °C in protein mixture comprising albumin (0.001-30% (w/w)) and phosphate-buffered saline. The maximum specific growth rate (μmax) and maximum population density (Nmax) estimated by the Baranyi and Roberts models were successfully described as equations of the amino group concentration in the form of Monod's model (Monod, 1949)and logarithm, respectively. The developed μmax equation was further incorporated into the square-root type μmax model developed by Ross (2003) to improve the predictive robustness. The model performance was validated using the experimentally obtained changes in E. coli numbers over time in actual foods. The root mean squared error (RMSE) of the model incorporating amino group concentration was better (RMSE = 0.652) than that of the model without amino group concentration (RMSE = 0.681). Notably, for lettuce, the prediction accuracy was significantly improved with the model incorporating amino group concentration (RMSE = 0.661) compared to the model without it (RMSE = 1.015). The developed model incorporating the effect of the amino group concentration indicated the potential to reduce the discrepancy between observed bacterial growth in actual foods and model predictions depending on the food type.

Methods for studying microbial acid stress responses: from molecules to populations

The study of how micro-organisms detect and respond to different stresses has a long history of producing fundamental biological insights while being simultaneously of significance in many applied microbiological fields including infection, food and drink manufacture, and industrial and environmental biotechnology. This is well-illustrated by the large body of work on acid stress. Numerous different methods have been used to understand the impacts of low pH on growth and survival of micro-organisms, ranging from studies of single cells to large and heterogeneous populations, from the molecular or biophysical to the computational, and from well-understood model organisms to poorly defined and complex microbial consortia. Much is to be gained from an increased general awareness of these methods, and so the present review looks at examples of the different methods that have been used to study acid resistance, acid tolerance, and acid stress responses, and the insights they can lead to, as well as some of the problems involved in using them. We hope this will be of interest both within and well beyond the acid stress research community.

Dynamic Modelling and Simulation of Food Systems: Recent Trends and Applications

Several factors influence consumers' choices of food products [...].

Variability in Cold Tolerance of Food and Clinical Listeria monocytogenes Isolates

The aim of this study was to investigate the level of strain variability amongst food and clinical Listeria monocytogenes isolates growing at low temperatures (4 and 7 °C) in both laboratory media and real food matrices. Isolates (n = 150) grown in laboratory media demonstrated a large variation in growth profiles measured using optical density. Overall, it was noted that clinical isolates exhibited a significantly higher growth rate (p ≤ 0.05) at 7 °C than the other isolates. Analysis of variance (ANOVA) tests of isolates grouped using Multi Locus Sequence Typing (MLST) revealed that clonal complex 18 (CC18) isolates were significantly (p ≤ 0.05) faster growing at 4 °C than other CC-type isolates while CC101, CC18, CC8, CC37 and CC14 were faster growing than other CC types at 7 °C. Euclidean distance and Ward method-based hierarchical clustering of mean growth rates classified 33.33% of isolates as faster growing. Fast and slow growing representative isolates were selected from the cluster analysis and growth rates were determined using plate count data in laboratory media and model food matrices. In agreement with the optical density experiments, CC18 isolates were faster and CC121 isolates were slower than other CC types in laboratory media, UHT milk and fish pie. The same trend was observed in chocolate milk but the differences were not statistically significant. Moreover, pan-genome analysis (Scoary) of isolate genome sequences only identified six genes of unknown function associated with increased cold tolerance while failing to identify any known cold tolerance genes. Overall, an association that was consistent in laboratory media and real food matrices was demonstrated between isolate CC type and increased cold tolerance.

Genetic, physiological, and cellular heterogeneities of bacterial pathogens in food matrices: Consequences for food safety

In complex food systems, bacteria live in heterogeneous microstructures, and the population displays phenotypic heterogeneities at the single-cell level. This review provides an overview of spatiotemporal drivers of phenotypic heterogeneity of bacterial pathogens in food matrices at three levels. The first level is the genotypic heterogeneity due to the possibility for various strains of a given species to contaminate food, each of them having specific genetic features. Then, physiological heterogeneities are induced within the same strain, due to specific microenvironments and heterogeneous adaptative responses to the food microstructure. The third level of phenotypic heterogeneity is related to cellular heterogeneity of the same strain in a specific microenvironment. Finally, we consider how these phenotypic heterogeneities at the single-cell level could be implemented in mathematical models to predict bacterial behavior and help ensure microbiological food safety.

Contribution of omics to biopreservation: Toward food microbiome engineering

Biopreservation is a sustainable approach to improve food safety and maintain or extend food shelf life by using beneficial microorganisms or their metabolites. Over the past 20 years, omics techniques have revolutionised food microbiology including biopreservation. A range of methods including genomics, transcriptomics, proteomics, metabolomics and meta-omics derivatives have highlighted the potential of biopreservation to improve the microbial safety of various foods. This review shows how these approaches have contributed to the selection of biopreservation agents, to a better understanding of the mechanisms of action and of their efficiency and impact within the food ecosystem. It also presents the potential of combining omics with complementary approaches to take into account better the complexity of food microbiomes at multiple scales, from the cell to the community levels, and their spatial, physicochemical and microbiological heterogeneity. The latest advances in biopreservation through omics have emphasised the importance of considering food as a complex and dynamic microbiome that requires integrated engineering strategies to increase the rate of innovation production in order to meet the safety, environmental and economic challenges of the agri-food sector.