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The use of predictive modelling tools, which mainly describe the response of microorganisms to a particular set of environmental conditions, may contribute to a better understanding of microbial behaviour in foods. In this paper, a tertiary model, in the form of a readily available and userfriendly web-based application Praedicere Possumus (PP) is presented with research examples from our laboratories. Through the PP application, users have access to different modules, which apply a set of published models considered reliable for determining the compliance of a food product with EU safety criteria and for optimising processing throughout the identification of critical control points. The application pivots around a growth/no-growth boundary model, coupled with a growth model, and includes thermal and non-thermal inactivation models. Integrated functionalities, such as the fractional contribution of each inhibitory factor to growth probability (f) and the time evolution of the growth probability (Pt), have also been included. The PP application is expected to assist food industry and food safety authorities in their common commitment towards the improvement of food safety.

Salmonellosis is globally recognized as one of the leading causes of acute human bacterial gastroenteritis resulting from the consumption of animal-derived products, particularly those derived from the poultry and pig industry. Salmonella spp. is generally associated with self-limiting gastrointestinal symptoms, lasting between 2 and 7 days, which can vary from mild to severe. The bacteria can also spread in the bloodstream, causing sepsis and requiring effective antimicrobial therapy; however, sepsis rarely occurs. Salmonellosis control strategies are based on two fundamental aspects: (a) the reduction of prevalence levels in animals by means of health, biosecurity, or food strategies and (b) protection against infection in humans. At the food chain level, the prevention of salmonellosis requires a comprehensive approach at farm, manufacturing, distribution, and consumer levels. Proper handling of food, avoiding cross-contamination, and thorough cooking can reduce the risk and ensure the safety of food. Efforts to reduce transmission of Salmonella by food and other routes must be implemented using a One Health approach. Therefore, in this review we provide an update on Salmonella, one of the main zoonotic pathogens, emphasizing its relationship with animal and public health. We carry out a review on different topics about Salmonella and salmonellosis, with a special emphasis on epidemiology and public health, microbial behavior along the food chain, predictive microbiology principles, antimicrobial resistance, and control strategies.

Microorganisms can contaminate food, thus causing food spoilage and health risks when the food is consumed. Foods are not sterile; they have a natural flora and a transient flora reflecting their environment. To ensure food is safe, we must destroy these microorganisms or prevent their growth. Recurring hazards due to lapses in the handling, processing, and distribution of foods cannot be solved by obsolete methods and inadequate proposals. They require positive approach and resolution through the pooling of accumulated knowledge. As the industrial domain evolves rapidly and we are faced with pressures to continually improve both products and processes, a considerable competitive advantage can be gained by the introduction of predictive modeling in the food industry. Research and development capital concerns of the industry have been preserved by investigating the plethora of factors able to react on the final product. The presence of microorganisms in foods is critical for the quality of the food. However, microbial behavior is closely related to the properties of food itself such as water activity, pH, storage conditions, temperature, and relative humidity. The effect of these factors together contributing to permitting growth of microorganisms in foods can be predicted by mathematical modeling issued from quantitative studies on microbial populations. The use of predictive models permits us to evaluate shifts in microbial numbers in foods from harvesting to production, thus having a permanent and objective evaluation of the involving parameters. In this vein, predictive microbiology is the study of the microbial behavior in relation to certain environmental conditions, which assure food quality and safety. Microbial responses are evaluated through developed mathematical models, which must be validated for the specific case. As a result, predictive microbiology modeling is a useful tool to be applied for quantitative risk assessment. Herein, we review the predictive models that have been adapted for improvement of the food industry chain through a built virtual prototype of the final product or a process reflecting real-world conditions. It is then expected that predictive models are, nowadays, a useful and valuable tool in research as well as in industrial food conservation processes.

Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival, and behaviour of microorganisms in food and other environments. This approach allows researchers, food producers, and regulatory bodies to assess the potential risks associated with microbial contamination and spoilage, enabling informed decisions to be made regarding food safety, quality, and shelf life. Two-step and one-step modelling approaches are modelling techniques with primary and secondary models being used, while the machine learning approach does not require using primary and secondary models for describing the quantitative behaviour of microorganisms, leading to the spoilage of food products. This comprehensive review delves into the various modelling techniques that have found applications in predictive food microbiology for estimating the shelf life of food products. By examining the strengths, limitations, and implications of the different approaches, this review provides an invaluable resource for researchers and practitioners seeking to enhance the accuracy and reliability of microbial shelf life predictions. Ultimately, a deeper understanding of these techniques promises to advance the domain of predictive food microbiology, fostering improved food safety practices, reduced waste, and heightened consumer confidence.

Salmonella continues to be a problem associated with low-moisture foods, particularly given enhanced thermal resistance at lower water activity (a ). However, there is a scarcity of thermal inactivation models accounting for the effect of a . The objective of this study was to test multiple secondary models for the effect of product (wheat flour) a on Salmonella enterica Enteritidis phage type 30 thermal resistance. A full-factorial experimental design included three temperatures (75, 80, and 85°C) and four a values (~0.30, 0.45, 0.60, and 0.70). Prior to isothermal treatment, sample a was achieved by equilibrating samples in a humidity-controlled conditioning chamber. Two primary models (log linear and Weibull type) and three secondary models (second-order response surface, modified Bigelow type, and combined effects) were evaluated using the corrected Akaike information criterion and root mean squared errors. Statistical analyses of the primary models favored the log-linear model. Incorporating the three secondary models into the log-linear primary model yielded root mean squared errors of 2.1, 0.78, and 0.96 log CFU/g and corrected Akaike information criterion values of 460, -145, and -19 for the response surface, modified Bigelow, and combined-effects models, respectively. The modified Bigelow-type model, which exponentially scaled both temperature and a effects on thermal inactivation rates, predicted Salmonella lethality significantly better (P < 0.05) than did the other secondary models examined. Overall, a is a critical factor affecting thermal inactivation of Salmonella in low-moisture products and should be appropriately included in thermal inactivation models for these types of systems.

Each year in Europe, meat is associated with 2.3 million foodborne illnesses, with a high contribution from beef meat. Many of these illnesses are attributed to pathogenic bacterial contamination and inadequate operations leading to growth and/or insufficient inactivation occurring along the whole farm-to-fork chain. To ensure consumer health, decision-making processes in food safety rely on Quantitative Microbiological Risk Assessment (QMRA) with many applications in recent decades. The present study aims to conduct a critical analysis of beef QMRAs and to identify future challenges. A systematic approach, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was used to collate beef QMRA models, identify steps of the farm-to-fork chain considered, and analyze inputs and outputs included as well as modelling methods. A total of 2343 articles were collected and 67 were selected. These studies focused mainly on western countries and considered Escherichia coli (EHEC) and Salmonella spp. pathogens. Future challenges were identified and included the need of whole-chain assessments, centralization of data collection processes, and improvement of model interoperability through harmonization. The present analysis can serve as a source of data and information to inform QMRA framework for beef meat and will help the scientific community and food safety authorities to identify specific monitoring and research needs.

Staphylococcus aureus is a gram-positive bacterium, commonly found in the nostrils, on the skin and on the hair of warm-blooded animals, including humans. It can produce a wide variety of virulence factors, including staphylococcal enterotoxins (SEs). In literature, 24 different SEs and many variants have been identified; among these, only five (the so-called classic enterotoxins) have been well-defined. Due to their emetic activity, SEs are frequently responsible for staphylococcal food poisoning, when consumers ingest contaminated food. SEs are proteins with a high tolerance of denaturing and can maintain their activity, even when the vegetative form of the bacteria is inactivated during food processing. The enterotoxin encoding genes are found in a variety of different genetic elements and, as a result, enterotoxin production varies widely between different populations of S. aureus. SEs production is modulated by multiple, and often overlapping, regulatory pathways, which are influenced by environmental factors. Furthermore, complex food matrices possess many characteristics (storage temperature, pH, sugar or salt concentration, presence of competitive microorganisms, etc.) that have a high impact on S. aureus behaviour. The multiple factors influencing S. aureus growth in food matrices and the production of SE complicates risk assessment procedures. In this review, we focus on enterotoxin production by S. aureus in food of animal origin, its regulation and detection and on the most recent developments in predictive microbiology and risk assessment models. Highlights Staphylococcus aureus produces several virulence factors that contribute to the pathogenesis of several serious human diseases: among these staphylococcal enterotoxins (SEs) have emetic activity, and are responsible for staphylococcal food poisoning (SFP). SEs are proteins that maintain their activity even though the vegetative form of the bacteria is inactivated during food processing. Enterotoxin encoding genes are found in a variety of genetic elements and enterotoxin production varies and is regulated by multiple regulatory pathways, which are influenced by environmental factors.

The first step in quantitative microbial risk assessment (QMRA) is to determine the distribution of pathogen contamination among servings of the food in question at some point in the farm-to-table chain. In the present study, the distribution of Salmonella contamination among servings of chicken liver for use in the QMRA was determined at meal preparation. Salmonella prevalence (P), most probable number (MPN, N), and serotype for different serving sizes were determined by use of a combination of five methods: (i) whole sample enrichment; (ii) quantitative PCR; (iii) culture isolation; (iv) serotyping; and (v) Monte Carlo simulation. Epidemiological data also were used to convert serotype data to virulence (V) values for use in the QMRA. A Monte Carlo simulation model based in Excel and simulated with @Risk predicted Salmonella P, N, serotype, and V as a function of a serving size of one (58 g) to eight (464 g) chicken livers. Salmonella P of chicken livers was 72.5% (58 of 80) per 58 g. Four Salmonella serotypes were isolated from chicken livers: (i) Infantis (P = 28%, V = 4.5); (ii) Enteritidis (P = 15%, V = 5); (iii) Typhimurium (P = 15%, V = 4.8); and (iv) Kentucky (P = 15%, V = 0.8). Salmonella N was 1.76 log MPN/58 g (median) with a range of 0 to 4.67 log MPN/58 g, and the median Salmonella N was not affected (P > 0.05) by serotype. The model predicted a nonlinear increase (P ≤ 0.05) of Salmonella P from 72.5%/58 g to 100%/464 g, a minimum N of 0 log MPN/58 g to 1.28 log MPN/464 g, and a median N from 1.76 log MPN/58 g to 3.22 log MPN/464 g. Regardless of serving size, predicted maximum N was 4.74 log MPN per serving, mean V was 3.9 per serving, and total N was 6.65 log MPN per lot (10,000 chicken livers). The data acquired and modeled in this study address an important data gap in the QMRA for Salmonella and whole chicken liver.

The growth of the lactic acid bacteria (LAB), Streptococcus thermophilus and Lactobacillus bulgaricus , widely used for yogurt production, results in acid production and the reduction of the milk pH . Industrial processes can show temperature ( T ) changes due to the large scale of the equipment. As T and pH affect the LAB growth, this study aimed to model the dependence of S. thermophilus and L . bulgaricus as a function of temperature and pH and to estimate and internally validate their growth parameters and confidence intervals with different modeling approaches. Twenty-four datasets regarding the growth kinetics of S. thermophilus and L . bulgaricus were used for estimating the kinetic parameters for each pure culture. The classical Baranyi and Roberts (sigmoidal) primary and Rosso and coworkers (cardinal parameter) secondary models successfully described the experimental data. The one-step modeling approach showed better statistical results than the two-step approach. The values of eight growth parameters ( μ opt , T min , T opt , T max , pH min , pH opt , pH max , and y max ) for each culture estimated from the fitting with the one-step approach and the Monte-Carlo-based approach were similar. Low averaged root-mean-squared errors ( RMSE ) (0.125 and 0.090 log CFU/mL) and percent discrepancy factor % D f ( 1.5 % and 1.2 % ) values for S. thermophilus and L. bulgaricus were obtained in the internal model validation, reinforcing the predictive ability of the model.

Unravelling the mechanisms of action of disinfectants is essential to optimise dosing regimes and minimise the emergence of antimicrobial resistance. In this work, we examined the mechanisms of action of a commonly used disinfectant—benzalkonium chloride (BAC)—over a significant pathogen—L. monocytogenes—in the food industry. For that purpose, we used modelling at multiple scales, from the cell membrane to cell population inactivation. Molecular modelling revealed that the integration of the BAC into the membrane requires three phases: (1) the approaching of BAC to the cellular membrane, (2) the absorption of BAC to its surface, and (3) the integration of the compound into the lipid bilayer, where it remains at least for several nanoseconds, probably destabilising the membrane. We hypothesised that the equilibrium of adsorption, although fast, was limiting for sufficiently large BAC concentrations, and a kinetic model was derived to describe time–kill curves of a large population of cells. The model was tested and validated with time series data of free BAC decay and time–kill curves of L. monocytogenes at different inocula and BAC dose concentrations. The knowledge gained from the molecular simulation plus the proposed kinetic model offers the means to design novel disinfection processes rationally.