This study examined the inactivation of spores of Bacillus licheniformis and Bacillus subtilis in four pea-based milk alternatives, semi-skimmed bovine milk and Brain Heart Infusion (BHI) broth to assess the matrix impact on the thermal inactivation of bacterial spores. Heat inactivation was performed with the method of capillary tubes in temperature range 97-110 °C. A four-parameter non-linear model, including initial level, shoulder duration, inactivation rate and tailing, was fitted to the data obtained. D-values were estimated and secondary ZT-value models were developed for both species. A secondary model for the shoulder length of B. licheniformis in a plant-based milk alternative formulation was built too. Models were validated at a higher temperature, 113.5 °C. D-values in the different matrices ranged between 2.3 and 8.2 min at 97 °C and 0.1-0.3 min at 110 °C for B. licheniformis. D-values for B. subtilis ranged between 3.9 and 6.3 min at 97 °C and 0.2-0.3 min at 110 °C. ZT-values in the different matrices ranged between 7.3 and 8.9 °C and 8.9-10.0 °C for B. licheniformis and B. subtilis, respectively. Significant differences in inactivation parameters were found within the pea-based formulations as well as when compared to bovine milk. Heat resistance was higher in pea-based matrices. Shoulders observed were temperature- and matrix-dependent, while no such trend was found for the tailings. These results provide insights, useful on designing safe thermal processing, limiting spoilage in plant-based milk alternatives and thus, reducing global food waste.

Monascus ruber is an important fungus that causes spoilage in table olives, resulting in the darkening of the brine, the softening of the fruit, increased pH, and apparent mycelial growth. This study aimed to evaluate this resistance, providing a model to determine the optimal processing conditions for mitigating fungal contamination and prolonging shelf life without antifungal agents while optimizing pasteurization to reduce energy consumption. The resistance in brine (3.5% NaCl; pH 3.5) from Arauco cultivar green olives imported from Argentina was assessed. Four predictive models (log linear, log linear + shoulder, log linear + tail, log linear + shoulder + tail) estimated kinetic parameters for each survival curve. Log linear + shoulder + tail provided the best fit for 70 °C and 75 °C, with low RMSE (0.171 and 0.112) and high R2 values (0.98 and 0.99), respectively, while the log linear model was used for 80 °C. Decimal reduction times at 70, 75, and 80 °C were 24.8, 5.4, and 1.6 min, respectively, with a z-value of 8.2 °C. The current regulatory processes are insufficient to eliminate M. ruber at requisite levels, considering reduced antifungal agents.

Despite their acidic pH, carbonated beverages can be contaminated by spoilage microorganisms. Thermal treatments, before and/or after carbonation, are usually applied to prevent the growth of these microorganisms. However, the impact of CO2 on the heat resistance of spoilage microorganisms has never been studied. A better understanding of the combined impact of CO2 and pH on the heat resistance of spoilage microorganisms commonly found in carbonated beverages might allow to optimize thermal treatment. Five microorganisms were selected for this study: Alicyclobacillus acidoterrestris (spores), Aspergillus niger (spores), Byssochlamys fulva (spores), Saccharomyces cerevisiae (vegetative cells), and Zygosaccharomyces parabailii (vegetative cells). A method was developed to assess the impact of heat treatments in carbonated media on microbial resistance. The heat resistances of the five studied species are coherent with the literature, when data were available. However, neither the dissolved CO2 concentration (from 0 to 7 g/L), nor the pH (from 2.8 to 4.1) have an impact on the heat resistance of the selected microorganisms, except for As. niger, for which the presence of dissolved CO2 reduced the heat resistance. This study improved our knowledge about the heat resistance of some spoilage microorganisms in presence of CO2.

Challenge tests are commonly employed to evaluate the growth behavior of L. monocytogenes in food matrices; they are known for being expensive and time-consuming. An alternative could be the use of predictive models to forecast microbial behavior under different conditions. In this study, the growth behavior of L. monocytogenes in different fresh produce was evaluated using a predictive model based on the Gamma concept considering pH, water activity (aw), and temperature as input factors. An extensive literature search resulted in a total of 105 research articles selected to collect growth/no growth behavior data of L. monocytogenes. Up to 808 L. monocytogenes behavior values and physicochemical characteristics were extracted for different fruits and vegetables. The predictive performance of the model as a tool for identifying the produce commodities supporting the growth of L. monocytogenes was proved by comparing with the experimental data collected from the literature. The model provided satisfactory predictions on the behavior of L. monocytogenes in vegetables (>80% agreement with experimental observations). For leafy greens, a 90% agreement was achieved. In contrast, the performance of the Gamma model was less satisfactory for fruits, as it tends to overestimate the potential of acid commodities to inhibit the growth of L. monocytogenes.

Variability in microbial growth is a keystone of modern Quantitative Microbiological Risk Assessment (QMRA). However, there are still significant knowledge gaps on how to model variability, with the most common assumption being that variability is constant. This is implemented by an error term (with constant variance) added on top of the secondary growth model (for the square root of the growth rate). However, this may go against microbial ecology principles, where differences in growth fitness among bacterial strains would be more prominent in the vicinity of the growth limits than at optimal growth conditions. This study coins the term \"secondary models for variability\", evaluating whether they should be considered in QMRA instead of the constant strain variability hypothesis. For this, 21 strains of Listeria innocua were used as case study, estimating their growth rate by the two-fold dilution method at pH between 5 and 10. Estimates of between-strain variability and experimental uncertainty were obtained for each pH using mixed-effects models, showing the lowest variability at optimal growth conditions, increasing towards the growth limits. Nonetheless, the experimental uncertainty also increased towards the extremes, evidencing the need to analyze both sources of variance independently. A secondary model was thus proposed, relating strain variability and pH conditions. Although the modelling approach certainly has some limitations that would need further experimental validation, it is an important step towards improving the description of variability in QMRA, being the first model of this type in the field.

This study assessed the fate of a Salmonella enterica cocktail (S. Typhimurium, S. Enteritidis, S. Newport, S. Agona and S. Anatum; initial counts 3.5 log CFU/g) in minimally processed sliced chard, broccoli and red cabbage at 16 conditions of different temperature (7, 14, 21 and 37 °C) and relative humidity (RH; 15, 35, 65 and 95%) over six days (144 h). Linear regression was used to estimate the rate change of Salmonella in cut vegetables as a function of temperature and relative humidity (RH). R2 value of 0.85, 0.87, and 0.78 were observed for the rates of change in chard, broccoli, and red cabbage, respectively. The interaction between temperature and RH was significant in all sliced vegetables. Higher temperatures and RH values favored Salmonella growth. As temperature or RH decreased, the rate of S. enterica change varied by vegetable. The models developed here can improve risk management of Salmonella in fresh cut vegetables.

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.

Salmonella is the most frequently reported cause of foodborne outbreaks with known origin in Europe, with eggs and egg products standing out as the most frequent food source (when it was known). The growth and survival of Salmonella in eggs and egg products have been extensively studied and, recently, it has been reported that factors such as the initial concentration and thermal history of the egg product can also influence its growth capability. Therefore, the objective of this study was to define the boundary zones of the growth/no growth domain of Salmonella Enteritidis (4 strains) as a function of temperature (low temperature boundary) and the initial concentration in different egg products. A series of polynomial logistic regression equations were successfully adjusted, allowing the study of these factors and their interaction on the probability of growth of S. Enteritidis in these products. Results obtained indicate that the minimum growth temperatures of Salmonella Enteritidis are higher in egg white (9.5-18.3 °C) than in egg yolk (7.1-7.8 °C) or liquid whole egg (7.2-7.9 °C). Results also demonstrate that in raw liquid whole egg and raw and pasteurized egg white, the minimum growth temperature of Salmonella Enteritidis does depend on the initial concentration. Similarly, the previous thermal history of the egg product only influenced the minimum growth temperature in some of them. On the other hand, large differences in the minimum growth temperatures among strains were observed in some products (up to approx. 6 °C in egg white). Finally, it should be noted that none of the strains grew at 5 °C under any of the conditions assayed. Therefore, storage of egg products (particularly whole liquid egg and egg yolk) below this temperature might be regarded/proposed as a good management approach. Our experimental approach has allowed us to provide a more accurate prediction of S. Enteritidis minimum growth temperatures in egg products by taking into account additional factors (initial concentration and thermal history) while also providing a quantification of the intra-specie variability. This would be of high relevance for improving the safety of egg products.

Secondary growth models from predictive microbiology can describe how the growth rate of microbial populations varies with environmental conditions. Because these models are built based on time and resource consuming experiments, model-based Optimal Experimental Design (OED) can be of interest to reduce the experimental load. In this study, we identify optimal experimental designs for two common models (full Ratkowsky and Cardinal Parameters Model (CPM)) for a different number of experiments (10-30). Calculations are also done fixing one or more model parameters, observing that this decision strongly affects the layout of the OED. Using in silico experiments, we conclude that OEDs are more informative than conventional (equidistant) designs with the same number of experiments. However, OEDs cluster the experiments near the growth limits (Xmin and Xmax) resulting in impractical designs with aggregated experimental runs ~10 times longer than conventional designs. To mitigate this, we propose a novel optimality criterion (i.e., the objective function) that accounts for the aggregated time. The novel criterion provides a reduction in parameter uncertainty with respect to the conventional design, without an increase in the experimental load. These results underline that an OED is only based on information theory (Fisher information), so the results can be impractical when actual experimental limitations are considered. The study also emphasizes that most OED schemes identify where to measure, but do not give an indication on how many experiments should be made. In this sense, numerical simulations can estimate the parameter uncertainty that would be obtained for a particular experimental design (OED or not). These results and methodologies (available in Open Code) can guide the design of future experiments for the development of secondary growth models.

Quantitative microbial risk assessment (QMRA) has witnessed rapid development within the context of food safety in recent years. As a means of contributing to these advancements, a QMRA for Salmonella spp. in fresh chicken patties for the general European Union (EU) population was developed. A two-dimensional (Second Order) Monte-Carlo simulation method was used for separating variability and uncertainty of model\'s parameters. The stages of industrial processing, retail storage, domestic storage, and cooking in the domestic environment were considered in the exposure assessment. For hazard characterization, a dose-response model was developed by combining 8 published dose-response models using a Pert distribution for describing uncertainty. The QMRA model predicted a mean probability of illness of 1.19*10-4 (5.28*10-5 - 3.57*10-4 95 % C.I.), and a mean annual number of illnesses per 100,000 people of 2.13 (0.96 - 6.59 95 % C.I.). Moreover, sensitivity analysis was performed, and variability in cooking preferences was found to be the most influential model parameter (r = -0.39), followed by dose-response related variability (r = 0.22), and variability in the concentration of Salmonella spp. at the time of introduction at the processing facility (r = 0.11). Various mitigation strategy scenarios were tested, from which, \"increasing the internal temperature of cooking\" and \"decreasing shelf life\" were estimated to be the most effective in reducing the predicted risk of illness. Salmonella-related illnesses exhibit particularly high severity, making them some of the most prominent zoonotic diseases in the EU. Regular monitoring of this hazard in order to further highlight its related parameters and causes is a necessary procedure. This study not only provides an updated assessment of Salmonella spp. risk associated with chicken patties, but also facilitates the identification of crucial targets for scientific investigation and implementation of real-world intervention strategies.