Software for predictive microbiology and risk assessment: A description and comparison of tools presented at the ICPMF8 Software Fair

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Chilled Pacu (Piaractus mesopotamicus) fillets: Modeling Pseudomonas spp. and psychrotrophic bacteria growth and monitoring spoilage indicators by 1H NMR and GC-MS during storage

This study aimed to assess the growth of Pseudomonas spp. and psychrotrophic bacteria in chilled Pacu (Piaractus mesopotamicus), a native South American fish, stored under chilling conditions (0 to 10 °C) through the use of predictive models under isothermal and non-isothermal conditions. Growth kinetic parameters, maximum growth rate (μmax, 1/h), lag time (tLag, h), and (Nmax, Log10 CFU/g) were estimated using the Baranyi and Roberts microbial growth model. Both kinetic parameters, growth rate and lag time, were significantly influenced by temperature (P < 0.05). The square root secondary model was used to describe the bacteria growth as a function of temperature. Secondary models, √μ = 0.016 (T + 10.13) and √μ =0.017 (T + 9.91) presented a linear correlation with R2 values >0.97 and were further validated under non-isothermal conditions. The model's performance was considered acceptable to predict the growth of Pseudomonas spp. and psychrotrophic bacteria in refrigerated Pacu fillets with bias and accuracy factors between 1.24 and 1.49 (fail-safe) and 1.45-1.49, respectively. Fish biomarkers and spoilage indicators were assessed during storage at 0, 4, and 10 °C. Volatile organic compounds, VOCs (1-hexanol, nonanal, octenol, and indicators 2-ethyl-1-hexanol) showed different behavior with storage time (P > 0.05). 1H NMR analysis confirmed increased enzymatic and microbial activity in Pacu fillets stored at 10 °C compared to 0 °C. The developed and validated models obtained in this study can be used as a tool for decision-making on the shelf-life and quality of refrigerated Pacu fillets stored under dynamic conditions from 0 to 10 °C.

The Influence of Storage Temperature and Packaging Technology on the Durability of Ready-to-Eat Preservative-Free Meat Bars with Dried Plasma

Food business operators must include the results of shelf life testing in their HACCP plan. Ready-to-eat preservative-free meat products enriched with blood plasma are an unfathomable area of research in food safety. We tested modified atmosphere (80% N2 and 20% CO2) and vacuum packaged RTE preservative-free baked and smoked pork bars with dried blood plasma for Aerobic Plate Count, yeast and mould, lactic acid bacteria, Staphylococcus aureus, Enterobacteriaceae, Escherichia coli, and Campylobacter spp., and the presence of Listeria monocytogenes and Salmonella spp. during storage (temperatures from 4 to 34 °C) up to 35 days after production. The obtained data on the count of individual groups of microorganisms were subjected to analysis of variance (ANOVA) and statistically tested (Student's t-test with the Bonferroni correction); for temperatures at which there were statistically significant differences and high numerical variability, the trend of changes in bacterial counts were visualised using mathematical modelling. The results show that the optimal storage conditions are refrigerated temperatures (up to 8 °C) for two weeks. At higher temperatures, food spoilage occurred due to the growth of aerobic bacteria, lactic acid bacteria, yeast, and mould. The MAP packaging method was more conducive to spoilage of the bars, especially in temperatures over 8 °C.

Microbiological Risk Assessment in Foods: Background and Tools, with a Focus on Risk Ranger

Risk assessment is an important phase of the food production path; it is strictly related to the processing chain as a necessary step for safe foods. This paper represents a contribution to understanding what is and how risk assessment could be conducted; it aims to provide some information on the structure of risk assessment, the tools for its identification and measurement and the importance of risk assessment for correct communication. In this context, after a focus on the background and on some commonly used tools (Risk Ranger, FDA-iRisk, decision tree, among others), the paper describes how to perform risk assessment through three case studies: lettuce (for Listeria monocytogenes), chicken salad (for Escherichia coli), and fresh egg pasta (for Staphylococcus aureus) in the first step, and then a comparison of risk for chicken salad contaminated by different pathogens (E. coli O157:H7, Campylobacter spp. and Salmonella sp.). As a final step, a critical evaluation of Risk Ranger was carried out, pointing out its pros and cons.

Food safety and food security through predictive microbiology tools: a short review

This article discusses the issues of food safety and food security as a matter of global health. Foodborne illness and deaths caused by pathogens in food continue to be a worldwide problem, with a reported 600 million cases per year, leading to around 420,000 deaths in 2010. Predictive microbiology can play a crucial role in ensuring safe food through mathematical modelling to estimate microbial growth and behaviour. Food security is described as the social and economical means of accessing safe and nutritious food that meets people's dietary preferences and requirements for an active and healthy life. The article also examines various factors that influence food security, including economic, environmental, technological, and geopolitical challenges globally. The concept of food safety is described as a science-based process or action that prevents food from containing substances that could harm human health. Food safety receives limited attention from policymakers and consumers in low- and middle-income countries, where food safety issues are most prevalent. The article also highlights the importance of detecting contaminants and pathogens in food to prevent foodborne illnesses and reduce food waste. Food and Agriculture Organization (FAO), an institution belonging to World Health Organization (WHO) presented calls to action to solve some of the emerging problems in food safety, as it should be a concern of all people to be involved in the pursue of safer food. The guarantee of safe food pertaining to microbiological contamination, as there are different types of active microorganisms in foods, could be obtained using predictive microbiology tools, which study and analyse different microorganisms' behaviour through mathematical models. Studies published by several authors show the application of primary, secondary, or tertiary models of predictive microbiology used for different food products.

Modeling of Growth and Organic Acid Kinetics and Evolution of the Protein Profile and Amino Acid Content during Lactiplantibacillus plantarum ITM21B Fermentation in Liquid Sourdough

The application of mathematical modeling to study and characterize lactic acid bacterial strains with pro-technological and functional features has gained attention in recent years to solve the problems relevant to the variabilities of the fermentation processes of sourdough. Since the key factors contributing to the sourdough quality are relevant to the starter strain growth and its metabolic activity, in this study, the cardinal growth parameters for pH, temperature (T), water activity (a_w), and undissociated lactic acid of the sourdough strain Lactiplantibacillus plantarum ITM21B, were determined. The strain growth, pH, organic acids (lactic, acetic, phenyllactic, and hydroxy-phenyllactic), total free amino acids, and proteins were monitored during fermentation of a liquid sourdough based on wheat flour and gluten (Bio21B) after changing the starting T, pH, and inoculum load. Results demonstrated that the different fermentation conditions affected the strain growth and metabolite pattern. The organic acid production and growth performance were modeled in Bio21B, and the resulting predictive model allowed us to simulate in silico the strain performances in liquid sourdough under different scenarios. This mathematical predictive approach can be useful to optimize the fermentation conditions needed to obtain the suitable nutritional and technological characteristics of the L. plantarum ITM21B liquid sourdough.

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.

Potential of multiparametric characterization of foodstuffs by nuclear magnetic resonance to better predict microbial behavior

Numerous predictive microbiology models have been proposed to describe bacterial population behaviors in foodstuffs. These models depict the growth kinetics of particular bacterial strains based on key physico-chemical parameters of food matrices and their storage temperature. In this context, there is a prominent issue to accurately characterize these parameters, notably pH, water activity (a_w ), and NaCl and organic acid concentrations. Usually, all these product features are determined using one destructive analysis per parameter at macroscale (>5 g). Such approach prevents an overall view of these characteristics on a single sample. Besides, it does not take into account the intra-product microlocal variability of these parameters within foods. Nuclear magnetic resonance (NMR) is a versatile non-invasive spectroscopic technique. Experiments can be recorded successively on a same collected sample without damaging it. In this work, we designed a dedicated NMR approach to characterize the microenvironment of foods using 10-mg samples. The multiparametric mesoscopic-scale approach was validated on four food matrices: a smear soft cheese, cooked peeled shrimps, cold-smoked salmon, and smoked ham. Its implementation in situ on salmon fillets enabled to observe the intra-product heterogeneity and to highlight the impact of process on the spatial distribution of pH, NaCl, and organic acids. This analytical development and its successful application can help address the shortcomings of monoparametric methods traditionally used for predictive microbiology purposes.

A Multi-Model Approach to Implement a Dynamic Shelf Life Criterion in Meat Supply Chains

The high perishability of fresh meat results in short sales and consumption periods, which can lead to high amounts of food waste, especially when a fixed best-before date is stated. Thus, the aim of this study was the development of a real-time dynamic shelf-life criterion (DSLC) for fresh pork filets based on a multi-model approach combining predictive microbiology and sensory modeling. Therefore, 647 samples of ma-packed pork loin were investigated in isothermal and non-isothermal storage trials. For the identification of the most suitable spoilage predictors, typical meat quality parameters (pH-value, color, texture, and sensory characteristics) as well as microbial contamination (total viable count, Pseudomonas spp., lactic acid bacteria, Brochothrix thermosphacta, Enterobacteriaceae) were analyzed at specific investigation points. Dynamic modeling was conducted using a combination of the modified Gompertz model (microbial data) or a linear approach (sensory data) and the Arrhenius model. Based on these models, a four-point scale grading system for the DSLC was developed to predict the product status and shelf-life as a function of temperature data in the supply chain. The applicability of the DSLC was validated in a pilot study under real chain conditions and showed an accurate real-time prediction of the product status.

Data Science in the Food Industry

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

Microrisk Lab: An Online Freeware for Predictive Microbiology

Microrisk Lab is an R-based online modeling freeware designed to realize parameter estimation and model simulation in predictive microbiology. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/inactivation under static and nonisothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/inactivation under static and nonisothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate data set and/or parameter setting. In this study, six case studies were reproduced in Microrisk Lab and compared in parallel with DMFit, GInaFiT, IPMP 2013/GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling systems. Microrisk Lab allows for a friendly user experience when modeling microbial behaviors owing to its interactive interfaces, high integration, and interconnectivity. Users can freely access this application at https://microrisklab.shinyapps.io/english/ or https://microrisklab.shinyapps.io/chinese/.

From Cheese-Making to Consumption: Exploring the Microbial Safety of Cheeses through Predictive Microbiology Models

Cheeses are traditional products widely consumed throughout the world that have been frequently implicated in foodborne outbreaks. Predictive microbiology models are relevant tools to estimate microbial behavior in these products. The objective of this study was to conduct a review on the available modeling approaches developed in cheeses, and to identify the main microbial targets of concern and the factors affecting microbial behavior in these products. Listeria monocytogenes has been identified as the main hazard evaluated in modelling studies. The pH, a_w, lactic acid concentration and temperature have been the main factors contemplated as independent variables in models. Other aspects such as the use of raw or pasteurized milk, starter cultures, and factors inherent to the contaminating pathogen have also been evaluated. In general, depending on the production process, storage conditions, and physicochemical characteristics, microorganisms can grow or die-off in cheeses. The classical two-step modeling has been the most common approach performed to develop predictive models. Other modeling approaches, including microbial interaction, growth boundary, response surface methodology, and neural networks, have also been performed. Validated models have been integrated into user-friendly software tools to be used to obtain estimates of microbial behavior in a quick and easy manner. Future studies should investigate the fate of other target bacterial pathogens, such as spore-forming bacteria, and the dynamic character of the production process of cheeses, among other aspects. The information compiled in this study helps to deepen the knowledge on the predictive microbiology field in the context of cheese production and storage.

Guidance on date marking and related food information: part 1 (date marking)

A risk-based approach was developed to be followed by food business operators (FBO) when deciding on the type of date marking (i.e. 'best before' date or 'use by' date), setting of shelf-life (i.e. time) and the related information on the label to ensure food safety. The decision on the type of date marking needs to be taken on a product-by-product basis, considering the relevant hazards, product characteristics, processing and storage conditions. The hazard identification is food product-specific and should consider pathogenic microorganisms capable of growing in prepacked temperature-controlled foods under reasonably foreseeable conditions. The intrinsic (e.g. pH and aw), extrinsic (e.g. temperature and gas atmosphere) and implicit (e.g. interactions with competing background microbiota) factors of the food determine which pathogenic and spoilage microorganisms can grow in the food during storage until consumption. A decision tree was developed to assist FBOs in deciding the type of date marking for a certain food product. When setting the shelf-life, the FBO needs to consider reasonably foreseeable conditions of distribution, storage and use of the food. Key steps of a case-by-case procedure to determine and validate the shelf-life period are: (i) identification of the relevant pathogenic/spoilage microorganism and its initial level, (ii) characterisation of the factors of the food affecting the growth behaviour and (iii) assessment of the growth behaviour of the pathogenic/spoilage microorganism in the food product during storage until consumption. Due to the variability between food products and consumer habits, it was not appropriate to present indicative time limits for food donated or marketed past the 'best before' date. Recommendations were provided relating to training activities and support, using 'reasonably foreseeable conditions', collecting time-temperature data during distribution, retail and domestic storage of foods and developing Appropriate Levels of Protection and/or Food Safety Objectives for food-pathogen combinations.

Understanding How Microorganisms Respond to Acid pH Is Central to Their Control and Successful Exploitation

Microbes from the three domains of life, Bacteria, Archaea, and Eukarya, share the need to sense and respond to changes in the external and internal concentrations of protons. When the proton concentration is high, acidic conditions prevail and cells must respond appropriately to ensure that macromolecules and metabolic processes are sufficiently protected to sustain life. While, we have learned much in recent decades about the mechanisms that microbes use to cope with acid, including the unique challenges presented by organic acids, there is still much to be gained from developing a deeper understanding of the effects and responses to acid in microbes. In this perspective article, we survey the key molecular mechanisms known to be important for microbial survival during acid stress and discuss how this knowledge might be relevant to microbe-based applications and processes that are consequential for humans. We discuss the research approaches that have been taken to investigate the problem and highlight promising new avenues. We discuss the influence of acid on pathogens during the course of infections and highlight the potential of using organic acids in treatments for some types of infection. We explore the influence of acid stress on photosynthetic microbes, and on biotechnological and industrial processes, including those needed to produce organic acids. We highlight the importance of understanding acid stress in controlling spoilage and pathogenic microbes in the food chain. Finally, we invite colleagues with an interest in microbial responses to low pH to participate in the EU-funded COST Action network called EuroMicropH and contribute to a comprehensive database of literature on this topic that we are making publicly available.

Predicting growth of Listeria monocytogenes at dynamic conditions during manufacturing, ripening and storage of cheeses - Evaluation and application of models

Mathematical models were evaluated to predict growth of L. monocytogenes in mould/smear-ripened cheeses with measured dynamic changes in product characteristics and storage conditions. To generate data for model evaluation three challenge tests were performed with mould-ripened cheeses produced by using milk inoculated with L. monocytogenes. Growth of L. monocytogenes and lactic acid bacteria (LAB) in the rind and in the core of cheeses were quantified together with changes in product characteristics over time (temperature, pH, NaCl/a_w, lactic- and acetic acid concentrations). The performance of nine available L. monocytogenes growth models was evaluated using growth responses from the present study and from literature together with the determined or reported dynamic product characteristics and storage conditions (46 kinetics). The acceptable simulation zone (ASZ) method was used to assess model performance. A reduced version of the Martinez-Rios et al. (2019) model (https://doi.org/10.3389/fmicb.2019.01510) and the model of Østergaard et al. (2014) (https://doi.org/10.1016/j.ijfoodmicro.2014.07.012) had acceptable performance with a ASZ-score of 71-70% for L. monocytogenes growth in mould/smear-ripened cheeses. Models from Coroller et al. (2012) (https://doi.org/10.1016/j.ijfoodmicro.2011.09.023) had close to acceptable performance with ASZ-scores of 67-69%. The validated models (Martinez-Rios et al., 2019; Østergaard et al., 2014) can be used to facilitate the evaluation of time to critical L. monocytogenes growth for mould/smear-ripened cheeses including modification of recipes with for example reduced salt/sodium or to support exposure assessment studies for these cheeses.

A Systematic Review of Beef Meat Quantitative Microbial Risk Assessment Models

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.

Predictive Modeling of Microbial Behavior in Food

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.

Application of data science in risk assessment and early warning

The food supply chain has been recognised by the EU as a critical infrastructure, and its complexity is the main cause of vulnerability. Depending on the food matrix, natural and/or deliberate contamination, food-borne diseases or even food fraud incidents may occur worldwide. Consequently, robust predictive models and/or software tools are needed to support decision-making and mitigating risks in an efficient and timely manner. In this frame, the fellow participated in data collection and analysis tasks, so as to provide additional predictive models. The working programme, covered a wide range of aspects related to risk assessment including identification of emerging risks (quantitative), microbiological risk assessment, authenticity assessment, spatio-temporal epidemiological modelling and database formation for hosting predictive microbial models. The training and close integration, in the open-source, in-house (German Federal Institute for Risk Assessment (BfR)) developed software tools under the framework of FoodRisk-Labs (https://foodrisklabs.bfr.bund.de.) for data analysis, predictive microbiology, quantitative microbiological risk assessment and automatic data retrieval purposes allowed for the independent use. Moreover, the fellow actively contributed to the update of the upcoming Yersinia enterocolitica risk assessment, and also in authenticity assessment of edible oils. Over the course of the year, the fellow was closely involved in international and national research projects with experts in the above-mentioned disciplines. Lastly, he consolidated his acquired knowledge by presenting his scientific work to conferences, and BfR-internal meetings.

Bacillus cereus cshA Is Expressed during the Lag Phase of Growth and Serves as a Potential Marker of Early Adaptation to Low Temperature and pH

The spore-forming bacterium B. cereus is a major cause of foodborne outbreaks in Europe. Some B. cereus strains can grow at low temperatures and low pH in many processed foods. Modeling of the bacterial lag time is hampered by a lack of knowledge of the timing of events occurring during this phase. In this context, the identification of lag phase markers, not currently available, could be a real advance for the better prediction of lag time duration. Currently, no molecular markers of this phase are available. By determining that cshA was always expressed early during the lag phase, we provide a molecular marker of the early adaptation process of B. cereus cells when exposed to low temperature and pH.

Bacterial adaptation is characterized by a lag phase during which cells do not multiply or modify their physiology to cope with the constraints of their environment. Our aim was to determine a sequence of events during the lag phase of growth at low temperature and pH for three Bacillus cereus strains. The onsets of expression of two genes, one of which is essential for stress adaptation (cshA, coding for a RNA helicase) and one of which is involved in the transition between lag phase and exponential phase (abrB, coding for a transition regulator), were determined using fluorescent transcriptional reporter systems. Regardless of the stressing conditions and the tested strains, the cshA promoter was active very early, while the biomass increased and always did so before the first cell division. At 12°C and pH 7.0, the onset of cshA promoter activity occurred at between 3 h and 7 h, while the bacterial counts started to increase at between 12 h and 13 h. At pH 5.0 and at 20°C or 30°C, the onset of cshA promoter activity occurred before 1 h and earlier than at pH 7.0. In contrast, the onset of abrB promoter activity depended on the strain and the stressing conditions. In the ATCC 14579 strain, the onset of abrB promoter activity always started at between 30 min and 3 h, before biomass increased and cell division occurred. For the other strains, it took place along with the first cell division at 12°C but did so much later during growth under the other tested conditions.

IMPORTANCE The spore-forming bacterium B. cereus is a major cause of foodborne outbreaks in Europe. Some B. cereus strains can grow at low temperatures and low pH in many processed foods. Modeling of the bacterial lag time is hampered by a lack of knowledge of the timing of events occurring during this phase. In this context, the identification of lag phase markers, not currently available, could be a real advance for the better prediction of lag time duration. Currently, no molecular markers of this phase are available. By determining that cshA was always expressed early during the lag phase, we provide a molecular marker of the early adaptation process of B. cereus cells when exposed to low temperature and pH.

Physiological traits and relative abundance of species as explanatory variables of co-occurrence pattern of cultivable bacteria associated with chia seeds

Deciphering the rules defining microbial community assemblage is envisioned as a promising strategy to improve predictions of pathogens colonization and proliferation in food. Despite the increasing number of studies reporting microbial co-occurrence patterns, only a few attempts have been made to challenge them in experimental or theoretical frameworks. Here, we tested the hypothesis that observed variations in co-occurrence patterns can be explained by taxonomy, relative abundance, and physiological traits of microbial species. We used PCR amplicon sequencing of taxonomic markers to assess distribution and co-occurrence patterns of bacterial and fungal species found in 25 chia (Salvia hispanica L.) samples originating from eight different sources. The use of nutrient-rich and oligotrophic media enabled isolation of 71 strains encompassing 16 bacterial species, of which five corresponded to phylotypes represented in the molecular survey. Tolerance to different growth inhibitors and antibiotics was tested to assess the physiological traits of these isolates. Divergence of physiological traits and relative abundance of each pair of species explained 69% of the co-occurrence profile displayed by cultivable bacterial phylotypes in chia. Validation of this ecological network conceptualization approach to more food products is required to integrate microbial species co-occurrence patterns in predictive microbiology.

Listeria monocytogenes in Milk: Occurrence and Recent Advances in Methods for Inactivation

Milk is one of the most important food items consumed by humans worldwide. In addition to its nutritional importance, milk is an excellent culture medium for microorganisms, which may include pathogens such as Listeria monocytogenes (L. monocytogenes). Traditional processing of milk for direct consumption is based on thermal treatments that efficiently eliminate pathogens, including pasteurization or sterilization. However, the occurrence of L. monocytogenes in milk as a consequence of failures in the pasteurization process or postpasteurization contamination is still a matter of concern. In recent years, consumer demand for minimally processed milk has increased due to the perception of better sensory and nutritional qualities of the products. This review deals with the occurrence of L. monocytogenes in milk in the last 10 years, including regulatory aspects, and recent advances in technologies for the inactivation of this pathogen in milk. The results from studies on nonthermal technologies, such as high hydrostatic pressure, pulsed electric fields, ultrasounds, and ultraviolet irradiation, are discussed, considering their potential application in milk processing plants.

Modeling Growth of Listeria and Lactic Acid Bacteria in Food Environments

Predictive food microbiology models can facilitate the assessment and management of microbial food safety. Importantly, the combined effect of storage conditions and product characteristics can be predicted by successfully validated models. This makes it easier and faster to develop or reformulation safe food recipes and predictions can be used to documents safety of available foods. The effect of various product characteristics and storage conditions must be taken into account and extensive mathematical models including the effect of these environmental factors are needed. Here the development, evaluation and application of an extensive growth and growth boundary model for Listeria monocytogenes including the effect of 12 environmental factors as well as the growth dampening effect of lactic acid bacteria is described. The Food Spoilage and Safety Predictor software is used to illustrate how predictions can be applied.

Intrinsic Parameters and Bacterial Growth Prediction in a Brazilian Minimally Ripened Cheese (Coalho) during Refrigerated Storage

This study evaluated the microbiological and physicochemical characteristics in different commercial brands of a Brazilian minimally ripened (coalho) cheese during 60 days of storage under refrigeration. Combinations of maximum and minimum values of water activity and pH determined in cheese samples at refrigeration temperature (7°C) were used in a bacterial growth prediction analysis. Maximum growth rate (Grmax) was estimated for different pathogenic and/or spoilage bacteria using the ComBase Predictor. Results of microbiological characterization analyses showed persistent high counts for all monitored microbial groups ( Lactobacillus spp., Lactococcus spp., Enterococcus spp., Staphylococcus spp., Enterobacteriaceae, proteolytic and lipolytic microorganisms, and fungi) in cheese samples; no dominant microbial group was observed over time. Values of pH (6.03 ± 0.16 to 7.28 ± 0.55), acidity (0.15% ± 0.09% to 0.66% ± 0.26%), sodium chloride (1.05% ± 0.19% to 1.97% ± 0.75%), and water activity (0.948 ± 0.020 to 0.974 ± 0.012) did not vary in cheese samples during storage. Estimated Grmax values for the tested bacteria were in the range of 0.004 to 0.044 log CFU/h. Highest Grmax values (0.005 to 0.044 log CFU/h) were predicted for the psychrotrophic Aeromonas hydrophila, Listeria monocytogenes, Pseudomonas spp., and Yersinia enterocolitica. Grmax values predicted for Escherichia coli, Salmonella spp., and Staphylococcus aureus were in the range of 0.004 to 0.016 log CFU/h. These results indicate unsatisfactory microbiological characteristics of commercially available coalho cheese. Physicochemical characteristics of commercial coalho cheese stored under refrigeration allow bacterial growth to occur, indicating higher risk for fast growth of contaminant bacteria in this product.

Postharvest Supply Chain with Microbial Travelers: a Farm-to-Retail Microbial Simulation and Visualization Framework

Fresh produce supply chains present variable and diverse conditions that are relevant to food quality and safety because they may favor microbial growth and survival following contamination. This study presents the development of a simulation and visualization framework to model microbial dynamics on fresh produce moving through postharvest supply chain processes. The postharvest supply chain with microbial travelers (PSCMT) tool provides a modular process modeling approach and graphical user interface to visualize microbial populations and evaluate practices specific to any fresh produce supply chain. The resulting modeling tool was validated with empirical data from an observed tomato supply chain from Mexico to the United States, including the packinghouse, distribution center, and supermarket locations, as an illustrative case study. Due to data limitations, a model-fitting exercise was conducted to demonstrate the calibration of model parameter ranges for microbial indicator populations, i.e., mesophilic aerobic microorganisms (quantified by aerobic plate count and here termed APC) and total coliforms (TC). Exploration and analysis of the parameter space refined appropriate parameter ranges and revealed influential parameters for supermarket indicator microorganism levels on tomatoes. Partial rank correlation coefficient analysis determined that APC levels in supermarkets were most influenced by removal due to spray water washing and microbial growth on the tomato surface at postharvest locations, while TC levels were most influenced by growth on the tomato surface at postharvest locations. Overall, this detailed mechanistic dynamic model of microbial behavior is a unique modeling tool that complements empirical data and visualizes how postharvest supply chain practices influence the fate of microbial contamination on fresh produce.IMPORTANCE Preventing the contamination of fresh produce with foodborne pathogens present in the environment during production and postharvest handling is an important food safety goal. Since studying foodborne pathogens in the environment is a complex and costly endeavor, computer simulation models can help to understand and visualize microorganism behavior resulting from supply chain activities. The postharvest supply chain with microbial travelers (PSCMT) model, presented here, provides a unique tool for postharvest supply chain simulations to evaluate microbial contamination. The tool was validated through modeling an observed tomato supply chain. Visualization of dynamic contamination levels from harvest to the supermarket and analysis of the model parameters highlighted critical points where intervention may prevent microbial levels sufficient to cause foodborne illness. The PSCMT model framework and simulation results support ongoing postharvest research and interventions to improve understanding and control of fresh produce contamination.

Bioinactivation FE: A free web application for modelling isothermal and dynamic microbial inactivation

Mathematical models developed in predictive microbiology are nowadays an essential tool for food scientists and researchers. However, advanced knowledge of scientific programming and mathematical modelling are often required in order to use them, especially in cases of modelling of dynamic and/or non-linear processes. This may be an obstacle for food scientists without such skills. Scientific software can help making these tools more accessible for scientists lacking a deep mathematical or computing background. Recently, the R package bioinactivation was published, including functions (model fitting and predictions) for modelling microbial inactivation under isothermal or dynamic conditions. It was uploaded to the Comprehensive R Archive Network (CRAN), but users need basic R programming knowledge in order to use it. Therefore, it was accompanied by Bioinactivation SE, a user-friendly web application including selected functions in the software for users without a programming background. In this work, a new web application, Bioinactivation FE, is presented. It is an extension of Bioinactivation SE which includes an interface to every function in the bioinactivation package: model fitting of isothermal and non-isothermal experiments, and generation of survivor curves and prediction intervals. Moreover, it includes several improvements in the user interface based on the users' feedback. The capabilities of the software are demonstrated through two case studies using data published in the scientific literature. In the first case study, the response of Escherichia coli to isothermal and non-isothermal treatments is compared, illustrating the presence of an induced thermal resistance. In the second, the effect of nanoemulsified d-limonene on the thermal resistance of Salmonella Senftenberg is quantified.

Case studies of packaging and processing solutions to improve meat quality and safety

A significant amount of the meat is wasted due to spoilage or safety risks. Active packaging systems have a great potential to reduce waste through chemical and microbial control of the product and/or the storage environment. Although commercial products are already available, active packaging is far from being fully developed. In contrast, passive packaging, such as modified atmosphere packaging (MAP) and vacuum packaging, have been fully implemented. Research conducted at the Danish Meat Research Institute (DMRI), demonstrates that it is possible to create new opportunities for the meat industry by modifying MAP or combining microwave treatment with vacuum packaging. Predictive shelf life models can be used to estimate the shelf life in MAP or vacuum under dynamic temperature conditions. Using the tri-gas guidelines, the industry can benefit from the increased eating quality, and the in-package decontamination process using vacuum packaging in combination with 5.8 GHz microwaves eliminates C. botulinum spores, resulting in increased food safety and an extended shelf life.

Shelf Life of Food Products: From Open Labeling to Real-Time Measurements

The labels currently used on food and beverage products only provide consumers with a rough guide to their expected shelf lives because they assume that a product only experiences a limited range of predefined handling and storage conditions. These static labels do not take into consideration conditions that might shorten a product's shelf life (such as temperature abuse), which can lead to problems associated with food safety and waste. Advances in shelf-life estimation have the potential to improve the safety, reliability, and sustainability of the food supply. Selection of appropriate kinetic models and data-analysis techniques is essential to predict shelf life, to account for variability in environmental conditions, and to allow real-time monitoring. Novel analytical tools to determine safety and quality attributes in situ coupled with modern tracking technologies and appropriate predictive tools have the potential to provide accurate estimations of the remaining shelf life of a food product in real time. This review summarizes the necessary steps to attain a transition from open labeling to real-time shelf-life measurements.

The STARTEC Decision Support Tool for Better Tradeoffs between Food Safety, Quality, Nutrition, and Costs in Production of Advanced Ready-to-Eat Foods

A prototype decision support IT-tool for the food industry was developed in the STARTEC project. Typical processes and decision steps were mapped using real life production scenarios of participating food companies manufacturing complex ready-to-eat foods. Companies looked for a more integrated approach when making food safety decisions that would align with existing HACCP systems. The tool was designed with shelf life assessments and data on safety, quality, and costs, using a pasta salad meal as a case product. The process flow chart was used as starting point, with simulation options at each process step. Key parameters like pH, water activity, costs of ingredients and salaries, and default models for calculations of Listeria monocytogenes, quality scores, and vitamin C, were placed in an interactive database. Customization of the models and settings was possible on the user-interface. The simulation module outputs were provided as detailed curves or categorized as "good"; "sufficient"; or "corrective action needed" based on threshold limit values set by the user. Possible corrective actions were suggested by the system. The tool was tested and approved by end-users based on selected ready-to-eat food products. Compared to other decision support tools, the STARTEC-tool is product-specific and multidisciplinary and includes interpretation and targeted recommendations for end-users.

Quantitative microbiological risk assessment in food industry: Theory and practical application

The objective of this article is to bring scientific background as well as practical hints and tips to guide risk assessors and modelers who want to develop a quantitative Microbiological Risk Assessment (MRA) in an industrial context. MRA aims at determining the public health risk associated with biological hazards in a food. Its implementation in industry enables to compare the efficiency of different risk reduction measures, and more precisely different operational settings, by predicting their effect on the final model output. The first stage in MRA is to clearly define the purpose and scope with stakeholders, risk assessors and modelers. Then, a probabilistic model is developed; this includes schematically three important phases. Firstly, the model structure has to be defined, i.e. the connections between different operational processing steps. An important step in food industry is the thermal processing leading to microbial inactivation. Growth of heat-treated surviving microorganisms and/or post-process contamination during storage phase is also important to take into account. Secondly, mathematical equations are determined to estimate the change of microbial load after each processing step. This phase includes the construction of model inputs by collecting data or eliciting experts. Finally, the model outputs are obtained by simulation procedures, they have to be interpreted and communicated to targeted stakeholders. In this latter phase, tools such as what-if scenarios provide an essential added value. These different MRA phases are illustrated through two examples covering important issues in industry. The first one covers process optimization in a food safety context, the second one covers shelf-life determination in a food quality context. Although both contexts required the same methodology, they do not have the same endpoint: up to the human health in the foie gras case-study illustrating here a safety application, up to the food portion in the brioche case-study illustrating here a quality application.

Modeling carbon dioxide effect in a controlled atmosphere and its interactions with temperature and pH on the growth of L. monocytogenes and P. fluorescens

The effect of carbon dioxide, temperature, and pH on growth of Listeria monocytogenes and Pseudomonas fluorescens was studied, following a protocol to monitor microbial growth under a constant gas composition. In this way, the CO₂ dissolution didn't modify the partial pressures in the gas phase. Growth curves were acquired at different temperatures (8, 12, 22 and 37 °C), pH (5.5 and 7) and CO₂ concentration in the gas phase (0, 20, 40, 60, 80, 100% of the atmospheric pressure, and over 1 bar). These three factors greatly influenced the growth rate of L. monocytogenes and P. fluorescens, and significant interactions have been observed between the carbon dioxide and the temperature effects. Results showed no significant effect of the CO₂ concentration at 37 °C, which may be attributed to low CO2 solubility at high temperature. An inhibitory effect of CO₂ appeared at lower temperatures (8 and 12 °C). Regardless of the temperature, the gaseous CO₂ is sparingly soluble at acid pH. However, the CO₂ inhibition was not significantly different between pH 5.5 and pH 7. Considering the pKa of the carbonic acid, these results showed the dissolved carbon under HCO₃^- form didn't affect the bacterial inhibition. Finally, a global model was proposed to estimate the growth rate vs. CO₂ concentration in the aqueous phase. This dissolved concentration is calculated according to the physical equations related to the CO₂ equilibriums, involving temperature and pH interactions. This developed model is a new tool available to manage the food safety of MAP.

Predicting the kinetics of Listeria monocytogenes and Yersinia enterocolitica under dynamic growth/death-inducing conditions, in Italian style fresh sausage

Traditional Italian pork products can be consumed after variable drying periods, where the temporal decrease of water activity spans from optimal to inactivating values. This makes it necessary to A) consider the bias factor when applying culture-medium-based predictive models to sausage; B) apply the dynamic version (described by differential equations) of those models; C) combine growth and death models in a continuous way, including the highly uncertain growth/no growth range separating the two regions. This paper tests the applicability of published predictive models on the responses of Listeria monocytogenes and Yersinia enterocolitica to dynamic conditions in traditional Italian pork sausage, where the environment changes from growth-supporting to inhibitory conditions, so the growth and death models need to be combined. The effect of indigenous lactic acid bacteria was also taken into account in the predictions. Challenge tests were carried out using such sausages, inoculated separately with L. monocytogenes and Y. enterocolitica, stored for 480h at 8, 12, 18 and 20°C. The pH was fairly constant, while the water activity changed dynamically. The effects of the environment on the specific growth and death rate of the studied organisms were predicted using previously published predictive models and parameters. Microbial kinetics in many products with a long shelf-life and dynamic internal environment, could result in both growth and inactivation, making it difficult to estimate the bacterial concentration at the time of consumption by means of commonly available predictive software tools. Our prediction of the effect of the storage environment, where the water activity gradually decreases during a drying period, is designed to overcome these difficulties. The methodology can be used generally to predict and visualise bacterial kinetics under temporal variation of environments, which is vital when assessing the safety of many similar products.