Status of Microbial Modeling in Food Process Models

A meta-analysis of microbial thermal inactivation in low moisture foods

Microbial thermal inactivation in low moisture foods is challenging due to enhanced thermal resistance of microbes and low thermal conductivity of food matrices. In this study, we leveraged the body of previous work on this topic to model key experimental features that determine microbial thermal inactivation in low moisture foods. We identified 27 studies which contained 782 mean D-values and developed linear mixed-effect models to assess the effect of microorganism type, matrix structure and composition, water activity, temperature, and inoculation and recovery methods on cell death kinetics. Intraclass correlation statistics (I2) and conditional R2 values of the linear mixed effects models were: E. coli (R2-0.91, I2-83%), fungi (R2-0.88, I2-85%), L. monocytogenes (R2-0.84, I2-75%), Salmonella (R2-0.69, I2-46%). Finally, global response surface models (RSM) were developed to further study the non-linear effect of aw and temperature on inactivation. The fit of these models varied by organisms from R2 0.88 (E. coli) to 0.35 (fungi). Further dividing the Salmonella data into individual RSM models based on matrix structure improved model fit to R2 0.90 (paste-like products) and 0.48 (powder-like products). This indicates a negative relationship between data diversity and model performance.

Modeling the influence of propionic acid concentration and pH on the kinetics of Salmonella Typhimurium

Salmonella Typhimurium is a foodborne pathogen often found in the poultry production chain. Antibiotics have been used to reduce S. Typhimurium contamination in poultry aviaries and improve chicken growth. However, antibiotics were banned in several countries. Alternatively, organic acids, such as propionic acid (PA), can control pathogens. This study determined the PA minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and mathematically modeled S. Typhimurium growth/inactivation kinetics under the influence of PA at different pH values (4.5, 5.5, and 6.5) which are within the pH range of the chicken gastrointestinal tract. The PA MIC against S. Typhimurium was pH-dependent, resulting in 5.0, 3.5 and 9.0 mM undissociated PA at pH 4.5, 5.5, and 6.5, respectively. The Baranyi and Roberts and the Weibull model fit growth and inactivation data well, respectively. Secondary models were proposed. The validated model predicted 3-log reduction of S. Typhimurium in 3 h at 68.2 mM of undissociated PA and pH 4.5. The models presented a good capacity to describe the kinetics of S. Typhimurium subjected to PA, representing a useful tool to predict PA antibacterial action depending on the pH.

Inactivation kinetics of a surrogate yield conservative predictions of foodborne pathogen reductions from low water activity foods of varying size and composition during low-temperature steam processing

There is a growing interest in using models to predict foodborne pathogen inactivation as a way to validate or verify preventive controls. Unlike liquid foods, solid, low water activity foods (LWAF) are heterogenous in composition and structure and do not transfer heat uniformly. Using models constructed from one food to predict pathogen inactivation on another LWAF is complex and may not always be possible, even if the foods have similar composition. Using models constructed from inactivation kinetics of three foodborne pathogens and a surrogate from vacuum-steam-pasteurized (72 and 82 °C) whole macadamia nuts and dried apricot halves, 3-log reductions were predicted for the same pathogens and foods of reduced size. Model fits (First-order, Weibull, and Gompertz) were significantly impacted by the food type regardless of particle size. Despite the foods being identical in composition with particle size as the only altered characteristic, best-fit models accurately predicted the 3-log reductions only 50% of the time, but the surrogate inactivation models provided conservative predictions for pathogen reductions, highlighting that a surrogate's model may be a suitable tool for predicting pathogen reduction on LWAFs.

Artificial Neural Network Prediction of Antiadhesion and Antibiofilm-Forming Effects of Antimicrobial Active Mushroom Extracts on Food-Borne Pathogens

The problem of microbial biofilms has come to the fore alongside food, pharmaceutical, and healthcare industrialization. The development of new antibiofilm products has become urgent, but it includes bioprospecting and is time and money-consuming. Contemporary efforts are directed at the pursuit of effective compounds of natural origin, also known as "green" agents. Mushrooms appear to be a possible new source of antibiofilm compounds, as has been demonstrated recently. The existing modeling methods are directed toward predicting bacterial biofilm formation, not in the presence of antibiofilm materials. Moreover, the modeling is almost exclusively targeted at biofilms in healthcare, while modeling related to the food industry remains under-researched. The present study applied an Artificial Neural Network (ANN) model to analyze the anti-adhesion and anti-biofilm-forming effects of 40 extracts from 20 mushroom species against two very important food-borne bacterial species for food and food-related industries-Listeria monocytogenes and Salmonella enteritidis. The models developed in this study exhibited high prediction quality, as indicated by high r² values during the training cycle. The best fit between the modeled and measured values was observed for the inhibition of adhesion. This study provides a valuable contribution to the field, supporting industrial settings during the initial stage of biofilm formation, when these communities are the most vulnerable, and promoting innovative and improved safety management.

Salmonella Behavior in Meat during Cool Storage: A Systematic Review and Meta-Analysis

The aim of the present study was to investigate Salmonella behavior in meat stored in cool conditions (between 0 °C and 7.5 °C), by employing a systematic review and meta-analysis. The data were obtained from research articles published in SciELO, PubMed, the Web of Science, and Scopus databases. The results of the retrieved studies were obtained from meat (beef, chicken, pork, poultry, and turkey), fish, shellfish, and broth media samples The data were extracted as sample size (n), initial concentration (Xi), final concentration (Xf), standard deviation (SD), standard error (SE), and microbial behavior effects (reduction or growth). A meta-analysis was carried out using the metaphor package from R software. A total of 654 articles were initially retrieved. After applying the exclusion criteria, 83 articles were selected for the systematic review, and 61 of these were used for the meta-analysis. Most studies were conducted at 0 °C to 4.4 °C storage temperatures under normal atmosphere package conditions. Salmonella Typhimurium, S. Enteritidis, and a cocktail (strain mixture) were inoculated at 5.0 and 6.0 log CFU mL−1. Articles both with and without the addition of antimicrobial compounds were found. Salmonella concentration decreases were observed in most studies, estimated for all study combinations as −0.8429 ± 0.0931 log CFU g−1 (95% CI; −1.0254, −0.6604) (p < 0.001), varying for each subgroup analysis. According to this survey, Salmonella concentration decreases are frequent during cool storage, although concentration increases and no bacterial inactivation were observed in some studies.

Engineering modeling frameworks for microbial food safety at various scales

The landscape of mathematical model-based understanding of microbial food safety is wide and deep, covering interdisciplinary fields of food science, microbiology, physics, and engineering. With rapidly growing interest in such model-based approaches that increasingly include more fundamental mechanisms of microbial processes, there is a need to build a general framework that steers this evolutionary process by synthesizing literature spread over many disciplines. The framework proposed here shows four interconnected, complementary levels of microbial food processes covering sub-cellular scale, microbial population scale, food scale, and human population scale (risk). A continuum of completely mechanistic to completely empirical models, widely-used and emerging, are integrated into the framework; well-known predictive microbiology modeling being a part of this spectrum. The framework emphasizes fundamentals-based approaches that should get enriched over time, such as the basic building blocks of microbial population scale processes (attachment, migration, growth, death/inactivation and communication) and of food processes (e.g., heat and moisture transfer). A spectrum of models are included, for example, microbial population modeling covers traditional predictive microbiology models to individual-based models and cellular automata. The models are shown in sufficient quantitative detail to make obvious their coupling, or their integration over various levels. Guidelines to combine sub-processes over various spatial and time scales into a complete interdisciplinary and multiphysics model (i.e., a system) are provided, covering microbial growth/inactivation/transport and physical processes such as fluid flow and heat transfer. As food safety becomes increasingly predictive at various scales, this synthesis should provide its roadmap. This big picture and framework should be futuristic in driving novel research and educational approaches.

Predictive Modeling and Validation on Growth, Production of Asexual Spores and Ochratoxin A of Aspergillus Ochraceus Group under Abiotic Climatic Variables

This study aimed to generate predictive models for growth, sporulation, and ochratoxin A (OTA) production under abiotic climatic variables, including temperatures (15-35 °C) and water activity levels (0.99-0.90 aw) by Aspergillus ochraceus group. The data were divided into three sets: one for training, one for testing, and the third one for model validation. Optimum growth occurred at 0.95 aw and 25 °C and 0.95 aw and 30 °C for A. westerdijkiae and A. steynii, respectively. Significantly improved A. westerdijkiae and A. steynii spore production occurred at 0.95 aw and 20 °C and 0.90 aw and 35 °C, respectively. A. steynii and A. westerdijkiae produced the majority of OTA at 35 °C and 0.95 aw and 25-30 °C at 0.95-0.99 aw, respectively. The accuracy of the third-order polynomial regression model reached 96% in growth cases, 94.7% in sporulation cases, and 90.9% in OTA production cases; the regression coefficients (R2) ranged from 0.8819 to 0.9978 for the Aspergillus ochraceus group. A reliable agreement was reached between the predicted and observed growth, sporulation, and OTA production. The effects of abiotic climatic variables on growth, sporulation, and OTA production of A. ochraceus group have been effectively defined, and the models generated were responsible for adequately predicted and validated models against data from other strains within A. ochraceus group that had been published in the literature under the current treatments. These models could be successfully implemented to predict fungal growth and OTA contamination on food matrices for these strains under these conditions.

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.

How to Decide on Modeling Details: Risk and Benefit Assessment

Mathematical models based on thermodynamic, kinetic, heat, and mass transfer analysis are central to this chapter. Microbial growth, death, enzyme inactivation models, and the modeling of material properties, including those pertinent to conduction and convection heating, mass transfer, such as diffusion and convective mass transfer, and thermodynamic properties, such as specific heat, enthalpy, and Gibbs free energy of formation and specific chemical exergy are also needed in this task. The origins, simplifying assumptions, and uses of model equations are discussed in this chapter, together with their benefits. The simplified forms of these models are sometimes referred to as "laws," such as "the first law of thermodynamics" or "Fick's second law." Starting to modeling a study with such "laws" without considering the conditions under which they are valid runs the risk of ending up with erronous conclusions. On the other hand, models started with fundamental concepts and simplified with appropriate considerations may offer explanations for the phenomena which may not be obtained just with measurements or unprocessed experimental data. The discussion presented here is strengthened with case studies and references to the literature.

Implications of salt and sodium reduction on microbial food safety

Excess sodium consumption has been cited as a primary cause of hypertension and cardiovascular diseases. Salt (sodium chloride) is considered the main source of sodium in the human diet, and it is estimated that processed foods and restaurant foods contribute 80% of the daily intake of sodium in most of the Western world. However, ample research demonstrates the efficacy of sodium chloride against pathogenic and spoilage microorganisms in a variety of food systems. Notable examples of the utility and necessity of sodium chloride include the inhibition of growth and toxin production by Clostridium botulinum in processed meats and cheeses. Other sodium salts contributing to the overall sodium consumption are also very important in the prevention of spoilage and/or growth of microorganisms in foods. For example, sodium lactate and sodium diacetate are widely used in conjunction with sodium chloride to prevent the growth of Listeria monocytogenes and lactic acid bacteria in ready-to-eat meats. These and other examples underscore the necessity of sodium salts, particularly sodium chloride, for the production of safe, wholesome foods. Key literature on the antimicrobial properties of sodium chloride in foods is reviewed here to address the impact of salt and sodium reduction or replacement on microbiological food safety and quality.