Modeling the Growth of Six Listeria monocytogenes Strains in Smoked Salmon Pâté

Listeria monocytogenes in aquatic food products: Spotlight on epidemiological information, bio-based mitigation strategies and predictive approaches

Listeria monocytogenes is the foodborne pathogen responsible for listeriosis in humans. Its ability to grow at refrigeration temperatures, particularly in products that support its growth and have a long-refrigerated shelf-life, poses a significant health risk, especially for vulnerable consumer groups such as pregnant women and immunocompromised individuals. A comprehensive analysis of L. monocytogenes in aquatic food products (AFPs) was conducted, examining the prevalence of the bacterium, the associated outbreaks, and the resulting deaths. Data from 66 studies, comprising a total of 19,373 samples, were analysed from the scientific literature to determine prevalence of the pathogen. The mean pooled prevalence of L. monocytogenes was 11 % (95 % CI: 8-14 %) among different AFPs categories. An overview of worldwide listeriosis outbreaks associated with contaminated AFPs between 1980 and 2023 was provided, totalling 1824 cases, including 41 deaths. Furthermore, a compilation of bio-based mitigation strategies was presented, including the use of lactic acid bacteria (LAB) and bacteriophages as bio-protective cultures to inhibit L. monocytogenes in AFPs. A variety of predictive microbiology models, based on growth prediction and interaction for L. monocytogenes, were reviewed to assess the effectiveness of control strategies in different types of AFPs, offering insights into pathogen behaviour throughout the production chain. The reported growth models describe primarily the impact of storage temperature on pathogen growth parameters, while interaction models, which reflect the inhibitory effect of LAB against L. monocytogenes, were generally defined using the Jameson-effect approach and Lotka-Volterra models' family (i.e., predator-prey models). Both models can be used to describe the simultaneous growth of two bacterial populations and their interactions (i.e., amensalism and antagonisms). Several Quantitative Risk assessment studies have been conducted for AFP, identifying the food category as a relevant contributor to Listeriosis risk, and providing predictive insight critical influence of storage temperature, food microbiota, product shelf-life, and population aging on the risk posed by L. monocytogenes. More importantly, this quantitative approach can serve as a key tool to assess the effectiveness of specific mitigation and intervention strategies to control the pathogen, such as sampling schemes or bio-preservation techniques.

Electromagnetic fields effects on microbial growth in cocoa fermentation: A controlled experimental approach using established growth models

Understanding the effects of electromagnetic fields is crucial in the fermentation of cocoa beans, since through precise control of fermentation conditions the sensory and nutritional properties of cocoa beans could be improved. This study aimed to evaluate the effect of oscillating magnetic fields (OMF) on the kinetic growth of the core microbial communities of the Collections Castro Naranjal (CCN 51) cocoa bean. The data was obtained by three different models: Gompertz, Baranyi, and Logistic. The cocoa beans were subjected to different OMF strengths ranging from 0 mT to 80 mT for 1 h using the Helmholtz coil electromagnetic device. The viable microbial populations of lactic acid bacteria (LAB), acetic acid bacteria (AAB), and yeast (Y) were quantified using the colony-forming unit (CFU) counting method. The logistic model appropriately described the growth of LAB and Y under magnetic field exposure. Whereas the Baranyi model was suitable for describing AAB growth. The microbial populations in cocoa beans exposed to magnetic fields showed lower (maximum specific growth rate (μmax), values than untreated controls, with AAB exhibiting the highest average growth rate value at 5 mT and Y having the lowest average maximum growth rate value at 80 mT. The lower maximum specific growth rates and longer lag phases when exposed to magnetic fields compared to controls demonstrate the influence of magnetic fields on microbial growth kinetics.

Prediction of Pseudomonas spp. Population in Food Products and Culture Media Using Machine Learning-Based Regression Methods

Machine learning approaches are alternative modelling techniques to traditional modelling equations used in predictive food microbiology and utilise algorithms to analyse large datasets that contain information about microbial growth or survival in various food matrices. These approaches leverage the power of algorithms to extract insights from the data and make predictions regarding the behaviour of microorganisms in different food environments. The objective of this study was to apply various machine learning-based regression methods, including support vector regression (SVR), Gaussian process regression (GPR), decision tree regression (DTR), and random forest regression (RFR), to estimate bacterial populations. In order to achieve this, a total of 5618 data points for Pseudomonas spp. present in food products (beef, pork, and poultry) and culture media were gathered from the ComBase database. The machine learning algorithms were applied to predict the growth or survival behaviour of Pseudomonas spp. in food products and culture media by considering predictor variables such as temperature, salt concentration, water activity, and acidity. The suitability of the algorithms was assessed using statistical measures such as coefficient of determination (R²), root mean square error (RMSE), bias factor (Bf), and accuracy (A_f). Each of the regression algorithms showed appropriate estimation capabilities with R² ranging from 0.886 to 0.913, RMSE from 0.724 to 0.899, B_f from 1.012 to 1.020, and A_f from 1.086 to 1.101 for each food product and culture medium. Since the predictive capability of RFR was the best among the algorithms, externally collected data from the literature were used for RFR. The external validation process showed statistical indices of B_f ranging from 0.951 to 1.040 and A_f ranging from 1.091 to 1.130, indicating that RFR can be used for predicting the survival and growth of microorganisms in food products. Therefore, machine learning approaches can be considered as an alternative to conventional modelling methods in predictive microbiology. However, it is important to highlight that the prediction power of the machine learning regression method directly depends on the dataset size, and it requires a large dataset to be employed for modelling. Therefore, the modelling work of this study can only be used for the prediction of Pseudomonas spp. in specific food products (beef, pork, and poultry) and culture medium with certain conditions where a large dataset is available.

Listeria in Food: Prevalence and Control

Listeria monocytogenes is a foodborne pathogen characterized by its psychrotrophic and ubiquitous nature as well as its ability to survive and proliferate in a wide range of harsh environments and foods [...].