Predictive Microbiology Models Research Articles

Machine Learning-Based Software for Predicting Pseudomonas spp. Growth Dynamics in Culture Media.

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of Pseudomonas spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors-temperature, water activity, and pH-served as predictor variables to model the growth of Pseudomonas spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R2adj) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R2adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, Bf: 0.998 to 1.047; accuracy factor, Af: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology.

Assessing refrigerated preservation performance using Listeria predictive microbiology models and temperature data: Refrigerator performance indicator and time-temperature equivalent.

Time-temperature data for queso fresco (QF) cheese varieties stored in a residential refrigerator operating at 5°C and a predictive microbiology secondary model for Listeria monocytogenes in QF were used to estimate a refrigerator performance indicator (RPI) of microbial preservation. RPI values were used to assess how compressor technology (single [SS] and variable speed [VS]), ambient temperature (21.1°C [LT] and 32.2°C [HT]), and refrigerator load (22.5kg regular load and 39kg higher load) affected preservation performance. All deterministic and probabilistic RPI estimations slightly exceeded the desirable 1.0 value, i.e., the variable temperatures for the QF kept in the refrigerator were worse than keeping it constantly at the temperature recommended by food safety agencies for QF. Furthermore, the mean comparison of estimates of the time-temperature equivalent indicator previously developed by French researchers showed similar behavior to those observed for RPI. Finally, statistical analysis showed that Tambient was the factor with the highest impact on refrigerator performance because of its impact on the sample temperature increase during door openings and when exposed to ambient temperature during product use. This highlights the need to reduce the time for product temperature recovery by improving the compressor operation logic. Also important are consumer behavior changes such as a reduction in product exposure to ambient temperature and in the door opening duration and frequency. PRACTICAL APPLICATION: This study demonstrated how a quantitative tool (RPI) can assess refrigerator preservation performance. Although the findings presented can be applied to any cold chain segment, the data used was collected for its weakest link, the domestic refrigerator. Surveys show that 77% of them operate above the recommended 4°C. The RPI methodology is ready for use by refrigerator designers to assess performance improvements possible by modifications of the compressor operation logic. Moreover, it can be integrated into smart-hubs monitoring the frequency and duration of refrigerator door openings to inform consumers when their habits are compromising the preservation performance of the refrigerator.

Methods for Determining and Predicting Shelf Life of Food Products

The length of shelf life can be determined using storage tests that evaluate the physical, chemical, enzymatic, and microbiological stability of the food. The design of the tests as well as the interpretation of the results require microbiological and technical knowledge and skills. Fresh foods can become microbiologically risky before they noticeably spoil. These products will require additional studies such as predictive microbiological models or challenge tests in addition to direct ones. Deterioration of shelf-stable or frozen foods at the end of their shelf life affects quality and consumer acceptance without impacting health and safety. In such a case, accelerated shelf-life testing is recommended, and the Arrhenius model is commonly used to determine the relationship between chemical reaction rate and temperature change.

Predictive modeling of Salmonella spp. growth behavior in cooked and raw chicken samples: Real-time PCR quantification approach and model assessment in different handling scenarios.

The increasing prevalence of Salmonella contamination in poultry meat emphasizes the importance of suitable predictive microbiological models for estimating Salmonella growth behavior. This study was conducted to evaluate the potential of chicken juice as a model system to predict the behavior of Salmonella spp. in cooked and raw chicken products and to assess its ability to predict cross-contamination scenarios. A cocktail of four Salmonella serovars was inoculated into chicken juice, sliced chicken, ground chicken, and chicken patties, with subsequent incubation at 10, 15, 20, and 25°C for 39h. The number of Salmonella spp. in each sample was determined using real-time polymerase chain reaction. Growth curves were fitted into the primary Baranyi and Roberts model to obtain growth parameters. Interactions between temperature and growth parameters were described using the secondary Ratkowsky's square root model. The predictive results generated by the chicken juice model were compared with those obtained from other chicken meat models. Furthermore, the parameters of the chicken juice model were used to predict Salmonella spp. numbers in six worst-case cross-contamination scenarios. Performance of the chicken juice model was evaluated using the acceptable prediction zone from -1.0 (fail-safe) to 0.5 (fail-dangerous) log. Chicken juice model accurately predicted all observed data points within the acceptable range, with the distribution of residuals being wider near the fail-safe zone (75%) than near the fail-dangerous zone (25%). This study offers valuable insights into a novel approach for modeling Salmonella growth in chicken meat products, with implications for food safety through the development of strategic interventions. PRACTICAL APPLICATION: The findings of this study have important implications in the food industry, as chicken juice could be a useful tool for predicting Salmonella behavior in different chicken products and thus reducing the risk of foodborne illnesses through the development of strategic interventions. However, it is important to recognize that some modifications to the chicken juice model will be necessary to accurately mimic all real-life conditions, as multiple factors particularly those related to food processing can vary between different products.

A new methodology to robustify an experimental design: Application to the Baranyi model

A robust experimental design is a desired object for practitioners when there is uncertainty about any of the assumptions necessary to compute the optimal design. For instance, when they use non-linear models, which requires having nominal values of the parameters. Several alternatives have been developed in the literature to obtain robust experimental designs such as adaptive or Bayesian designs, among others. Here a new methodology is proposed to robustify the optimal experimental design. Based on the maximin idea, the method adds support points to the optimal design, to obtain designs that are robust. It is applied to the Baranyi model, one of the most used mathematical models in predictive microbiology to describe the behaviour of microorganisms in food products, an essential issue for human health. Previously, D-optimal designs are provided for the model, considering 4 and 6 parameters to be estimated. A sensitivity analysis is carried out regarding the deviations in the nominal values of the parameters, which shows a greater loss of efficiency for two of them. Given these results, the new methodology is applied to the D-optimal design, checking the robustness of the augmented designs through the efficiency achieved. Finally, c-, A- and I-optimal designs are calculated to provide accurate estimation of the model parameters and the predictions.

A stochastic approach for modelling the in-vitro effect of osmotic stress on growth dynamics and persistent cell formation in Listeria monocytogenes

Persistent bacteria (or persisters) can be defined as a microbial subpopulation that, exposed to bactericidal treatment, is killed more slowly than the rest of the population they are part of. They stochastically originate in response to environmental stressors or spontaneously without external signals. When transferred into a fresh medium, persisters can resume active replication although they spend more time adapting to the new conditions remaining in the lag phase longer. They were studied for decades for their ability to survive antibiotic treatments while studies on their formation in food and potential impact on their safety are lacking. The most common food preservation techniques may act as stressors that trigger the formation of persistent bacteria able to survive bactericidal treatments and grow later in foods during storage. This study aimed to investigate a possible relationship between exposure to different salt concentrations (osmotic stress) and the amount of persisters triggered in a strain of Listeria monocytogenes. Furthermore, we described this phenomenon from a mathematical perspective through predictive microbiology models commonly used in the food field. The lag time distribution of a L. monocytogenes ATCC 7644 strain grown in broth with additional 2 %, 4 % and 6 % NaCl was evaluated using the software ScanLag. It uses office scanners to automatically record the colony growth on agar plates and evaluate the frequency distribution of their appearance times (lag phase) by automated image analysis. The same broth cultures were diluted to equalize salt concentration and transferred into a fresh broth to evaluate how the previous salt exposure impacted their growth kinetics. The observed growth curves were reproduced using predictive models in which the mean duration of the lag phase of the whole population took into account the occurrence of persisters with a longer lag phase. The models were solved first using a deterministic approach and then a stochastic one introducing a stochastic term that mimics the variability of lag phase duration due to the persisters occurrence. Results showed that the growth of L. monocytogenes in broth with additional NaCl might trigger the formation of persistent cells whose number increased consistently with salt concentrations. The proposed predictive approach reproduced the observed real curves in strong agreement, especially through the stochastic resolution of the models. Persistence is currently a neglected bacterial defence strategy in the food sector but the persisters' formation during production cannot be excluded; therefore, further insights on the topic are certainly desirable.

Unveiling Fresh-Cut Lettuce Processing in Argentine Industries: Evaluating Salmonella Levels Using Predictive Microbiology Models

A survey was performed to gather information on the processing steps, conditions, and practices employed by industries processing ready-to-eat (RTE) leafy vegetables in Argentina. A total of seven industries participated in the survey. A cluster analysis of the data obtained was performed to identify homogeneous groups among the participating industries. The data collected were used as inputs of two predictive microbiology models to estimate Salmonella concentrations after chlorine washing, during storage and distribution of final products, and to rank the different practices according to the final estimated Salmonella levels. Six different clusters were identified by evaluating the parameters, methods, and controls applied in each processing step, evidencing a great variability among industries. The disinfectant agent applied by all participating industries was sodium hypochlorite, though concentrations and application times differed among industries from 50 to 200 ppm for 30 to 110 s. Simulations using predictive models indicated that the reductions in Salmonella in RTE leafy vegetables would vary in the range of 1.70–2.95 log CFU/g during chlorine-washing depending on chlorine concentrations applied, washing times, and vegetable cutting size, which varied from 9 to 16 cm2 among industries. Moreover, Salmonella would be able to grow in RTE leafy vegetables during storage and distribution, achieving levels of up to 2 log CFU/g, considering the storage and transportation temperatures and times reported by the industries, which vary from 4 to 14 °C and from 18 to 30 h. These results could be used to prioritize risk-based sampling programs by Food Official Control or determine more adequate process parameters to mitigate Salmonella in RTE leafy vegetables. Additionally, the information gathered in this study is useful for microbiological risk assessments.

Modeling naturally-occurring Vibrio parahaemolyticus in post-harvest raw shrimps

There is little known about the growth and survival of naturally-occurring Vibrio parahaemolyticus in harvested raw shrimps. In this study, the fate of naturally-occurring V. parahaemolyticus in post-harvest raw shrimps was investigated from 4℃ to 30℃ using real-time PCR combined with propidium monoazide (PMA-qPCR). The Baranyi-model was used to fit the growth and survival data. A square root model and non-linear Arrhenius model was then used to quantify the parameters derived from the Baranyi-model. The results showed that naturally-occurring V. parahaemolyticus were slowly inactivated at 4℃ and 7℃ with deactivation rates of 0.019 Log CFU/g/h and 0.025 Log CFU/g/h. Conversely, at 15, 20, 25, and 30 °C, the average maximum growth rates (μmax) of naturally-occurring V. parahaemolyticus were determined to be 0.044, 0.105, 0.179 and 0.336 Log CFU/g/h, accompanied by the average lag phases (λ) of 15.5 h, 7.3 h, 4.4 h and 3.7 h. The validation metrics, Af and Bf, for both the square root model and non-linear, indicating that the model had a good ability to predict the growth behavior of naturally-occurring V. parahaemolyticus in post-harvest raw shrimps. Furthermore, a comparative exploration between the growth of artificially contaminated V. parahaemolyticus in cooked shrimps and naturally-occurring V. parahaemolyticus in post-harvest raw shrimps revealed intriguing insights. While no substantial distinction in deactivation rates emerged at 4 °C and 7 °C (P > 0.05), a discernible disparity in growth rates was observable at 15 °C, 20 °C, 25 °C, and 30 °C, with the former surpassing the latter. Which indicated the risk of V. parahaemolyticus using models derived from cooked shrimps may be biased. Our study also unveiled a discernible seasonal effect. The μmax and λ of V. parahaemolyticus in shrimps harvested in summer were similar to those harvested in autumn, while the initial and maximum bacterial concentration harvested in summer were higher than those harvested in autumn. This predictive microbiology model of naturally-occurring V. parahaemolyticus in raw shrimps provides relevance to modelling growth in situ.

Ambient temperature, refrigerator food load, and door openings effect on a preservation performance indicator based on chicken temperature data and predictive microbiology.

Home preservation depends on the food matrix, refrigerator design/technology, consumer actions, and ambient temperature. Storing different food matrices in product-relevant refrigerator locations generating different temperature histories can be used to develop an indicator of how refrigerator technology, consumer habits, and environment conditions impact the refrigerator food preservation performance. In this study, poultry, particularly prone to spoilage reflecting its pH, nutrient availability, and high aw, was used to evaluate refrigerator preservation performance as affected by compressor technology (single [SS] and variable speed [VS]), ambient temperature (21.1°C [LT] and 32.2°C [HT]), and refrigerator load (22.5kg [RL] and 39kg [HL]). Time-temperature values collected for chicken breast stored in a drawer independently controlled at 0°C in a refrigerator set 5°C, and a Pseudomonas predictive microbiology model, were used to estimate a normalized refrigerator performance indicator (RPI). Values <1, ∼1, and >1 described excellent, good, or poor performance, respectively. A first analysis revealed that up to 54% of chicken breast temperatures were above its recommended refrigerated storage value. When ignoring variability sources, SS technology yielded RPI values ranging 0.61-0.70, whereas the more energy efficient VS compressor yielded values ranging 0.86-1.14. The higher and wider VS RPI range reflects a compressor control logic optimized for energy efficiency compliance while disregarding effects on food preservation. When considering the variability of model parameters and temperature measurements through one-sided 95% confidence intervals yielded RPI reaching 1.16. Although the independently controlled drawer preservation performance was near optimal, it can improve by considering energy use and preservation impact when optimizing the compressor speed control protocol. PRACTICAL APPLICATION: Worldwide poultry meat consumption has reached 15kg per person. Refrigeration is widely used for its safety and quality preservation. Efficiency regulations decreased the energy use of residential refrigerators by nearly tenfold even though their size increased by 50% in the last half century. In this study, we provide quantitative evidence that their preservation performance must be improved. This is particularly true for upper end units typically equipped with quieter and more energy-efficient variable speed compressors. The same methodology can be used to evaluate the preservation performance of the storage units, trucks, and display cases used for refrigerated products.

The importance of what we cannot observe: Experimental limitations as a source of bias for meta-regression models in predictive microbiology

Meta-regression models have gained in popularity during the last years as a way to create more generic models for Microbial Risk Assessments that also include variability. However, as with most meta-analyses and empirical models, systematic biases in the data can result in inaccurate models. In this article, we define experimental bias as a type of selection bias due to the practical limitations of microbial inactivation experiments. Conditions with extremely high D-values (i.e. slow inactivation) need very long experimental runs to cause significant reductions. On the other hand, when the D-value is extremely low, not enough data points can be gathered before the microbial population is below the detection limit. Consequently, experimental designs favour conditions within a practical experimental range, introducing a selection bias in the D-values.We demonstrate the impact of experimental bias in meta-regression models using numerical simulations. Models fitted to data with experimental bias overestimated the z-value and underestimated variability. We propose a rapid heuristic method to identify experimental bias in datasets, and we propose truncated regression to mitigate its impact in meta-regression models. Both methods were validated using simulated data. Thereafter the procedures were tested by building a meta-regression model for actual data for the inactivation of Bacillus cereus spores. We concluded that the dataset included experimental bias, and that it would cause an overestimation of the microbial resistance at high temperatures (>120 °C) for classical meta-regression models. This effect was mitigated when the model was built using truncated regression. In conclusion, we demonstrate that experimental bias could potentially result in inaccurate models for predictive microbiology. Therefore, checking for experimental bias should be a routine step in meta-regression modelling, and be included in guidelines on data analysis for meta-regression.

Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork.

To better guide microbial risk management and control, growth kinetic models of Salmonella with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Salmonella Derby (S. Derby), Pseudomonas aeruginosa (P. aeruginosa), and Escherichia coli (E. coli), and incubated at various temperatures (4-37 °C). The predictive growth models were developed based on the observed growth data. By comparing R2 of primary models, Baranyi models were preferred to fit the growth curves of S. Derby and P. aeruginosa, while the Huang model was preferred for E. coli (all R2 ≥ 0.997). The secondary Ratkowsky square root model can well describe the relationship between temperature and μmax (all R2 ≥ 0.97) or Lag (all R2 ≥ 0.98). Growth models were validated by the actual test values, with Bf and Af close to 1, and MSE around 0.001. The time for S. Derby to reach a pathogenic dose (105 CFU/g) at each temperature in pork was predicted accordingly and found to be earlier than the time when the pork began to be judged nearly fresh according to the sensory indicators. Therefore, the predictive microbiology model can be applied to more accurately predict the shelf life of pork to secure its quality and safety.

Quantitative Risk Assessment of Susceptible and Ciprofloxacin-Resistant Salmonella from Retail Pork in Chiang Mai Province in Northern Thailand.

The adverse human health effects as a result of antimicrobial resistance have been recognized worldwide. Salmonella is a leading cause of foodborne illnesses while antimicrobial resistant (AMR) Salmonella has been isolated from foods of animal origin. The quantitative risk assessment (RA) as part of the guidelines for the risk analysis of foodborne antimicrobial resistance was issued by the Codex Alimentarius Commission more than a decade ago. However, only two risk assessments reported the human health effects of AMR Salmonella in dry-cured pork sausage and pork mince. Therefore, the objective of this study was to quantitatively evaluate the adverse health effects attributable to consuming retail pork contaminated with Salmonella using risk assessment models. The sampling frame covered pork at the fresh market (n = 100) and modern trade where pork is refrigerated (n = 50) in Chiang Mai province in northern Thailand. The predictive microbiology models were used in the steps where data were lacking. Susceptible and quinolone-resistant (QR) Salmonella were determined by antimicrobial susceptibility testing and the presence of AMR genes. The probability of mortality conditional to foodborne illness by susceptible Salmonella was modeled as the hazard characterization of susceptible and QR Salmonella. For QR Salmonella, the probabilistic prevalences from the fresh market and modern trade were 28.4 and 1.9%, respectively; the mean concentrations from the fresh market and modern trade were 346 and 0.02 colony forming units/g, respectively. The probability of illness (PI) and probability of mortality given illness (PMI) from QR Salmonella-contaminated pork at retails in Chiang Mai province were in the range of 2.2 × 10−8–3.1 × 10−4 and 3.9 × 10−10–5.4 × 10−6, respectively, while those from susceptible Salmonella contaminated-pork at retails were in the range 1.8 × 10−4–3.2 × 10−4 and 2.3 × 10−7–4.2 × 10−7, respectively. After 1000 iterations of Monte Carlo simulations of the risk assessment models, the annual mortality rates for QR salmonellosis simulated by the risk assessment models were in the range of 0–32, which is in line with the AMR adverse health effects previously reported. Therefore, the risk assessment models used in both exposure assessment and hazard characterization were applicable to evaluate the adverse health effects of AMR Salmonella spp. in Thailand.

Absolute preservation indicator for the assessment of refrigeration performance based on dynamic temperature measurements and predictive microbiology

National and international norms have been established to assess the energy use of refrigerated food storage including compliance with the maximum allowed for residential refrigerators. However, norms on how to assess refrigerated food storage from a preservation point of view have not been developed. A published predictive microbiology model and ∼3 million time-temperature values collected for pasteurized egg white stored in the top door shelf of a customized refrigerator were used to estimate an absolute performance indicator (API) of refrigerated storage. Deterministic (only using mean values) and probabilistic (using 1000 normally distributed values) estimates of 48h-temperature-dependent cumulative exponential growth for Salmonella spp. were defined as API (log10 CFU/mL). APIs were used to assess the effects of low and high ambient temperature (21.1 °C [LT], 32.2 °C [HT]), regular and high refrigerator load (22.5 [RL], 39.0 kg [HL]), and single or variable speed (SS or VS) compressor technology. Also included were samples temporarily exposed to room temperature [M] simulating consumer use, or always kept refrigerated [F]. The severe variable speed compressor, high ambient temperature, and high load condition (VS/HT/HL) yielded the highest deterministic API values, i.e., API = 0.138 ± 0.033 and 0.211 ± 0.091 for F and M samples, respectively. The probabilistic analysis yielded 5/95% percentiles of 0.08/0.22 and 0.02/0.13 for F and M samples, respectively. The lowest API values were observed for VS/LT/HL reaching 5/95% percentiles of 0.00/0.05 and 0.01/0.05 for F and M samples, respectively. Compressor speed control modifications lowering product temperature and shortening temperature recovery time after door openings and product exposure to room temperature are necessary, particularly for refrigerators operating under high ambient temperature. Units offering low API values while meeting energy use guidelines would be preferred by consumers.

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 (aw ), and NaCl and organic acid concentrations. Usually, all these product features are determined using one destructive analysis per parameter at macroscale (>5g). 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.

Machine learning approach for predicting single cell lag time of Salmonella Enteritidis after heat and chlorine treatment

The importance of single-cell variability is increasingly prominent with the developments in foodborne pathogens modeling. Traditional predictive microbiology model cannot accurately describe the growth behavior of small numbers of cells due to individual cell heterogeneity. The objective of the present study was to develop predictive models for single cell lag times of Salmonella Enteritidis after heat and chlorine treatment. A time-lapse microscopy method was employed to evaluate the single cell lag time by monitoring cell divisions. Four supervised machine learning algorithms including gradient boosting regression tree (GBRT), artificial neural network (ANN), random forest (RF), and support vector regression (SVR) were applied and compared. Results show that all four machine learning models have good predictive capabilities without an overfitting of the data. The ANN approach demonstrated superior prediction performance over other machine learning models (RMSE: 0.209, MAE: 0.135 and R2: 0.989). Furthermore, the SHapley Additive exPlanation (SHAP) measures were used to capture the influence of each feature on the model output, and results revealed that population lag times and sublethal injury rate have dominant impacts on the single cell lag time. Consequently, the findings generated from this study may be useful in managing the potential food safety risk caused by single cells of foodborne pathogens.

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 educationalapproaches.

Methodology for the food preservation assessment of residential refrigerators: Compressor and consumer practices effects on absolute and relative preservation indicators

Predictive microbiology models and 2.8E6 ham temperature measurements quantified absolute (API) and relative preservation indicators (RPI) assessing the performance of a residential refrigerator operating at 5 °C, the conventional setting for energy use assessments. Factors considered were compressor (single/variable speed, SS/VS), ambient temperature (21.1/32.2 °C), food load (22.5/39 kg), doors (closed/opening cycles), and fixed/movable samples (simulating breakfast use). APIT(t) based on 48 h temperature-dependent Listeria monocytogenes exponential growth ranged 0.23–0.36 log CFU/g at 32.2 °C/VS for all sample types and loads. The API value at the recommended 5 °C for ham (API5 °C, 0.155 log CFU/g) defined RPI = APIT(t)/API5 °C with values <1, ~1, >1 indicating a better, similar or worse preservation than constant 5 °C storage. The highest RPI value (2.32) was observed for VS/32.2 °C/high load/movable samples; however, it reached 1.10 when the entire ham temperature profile was lowered by 3.7 °C, the maximum possible without risking product freezing. A probabilistic analysis considering the variability of predictive microbiology parameters and temperature measurements, yielded high 95% percentile RPI values for all conditions. Although the SS compressor showed better RPIs, operating strategies for all compressor types should be modified to ensure lower-temperatures and shorter temperature recovery times while still meeting energy use regulations. The higher energy efficiency and multiple adjustments possible of VS compressors suggest that they should deliver better food preservation than SS compressors. This study showed an effective use of product temperature to assess the preservation performance of all cold chain components.

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, aw, 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.

Residential Refrigerator Performance Based on Microbial Indicators of Ground Beef Preservation Assessed Using Predictive Microbiology Tools

The aim of this study was to develop a refrigerator performance assessment procedure based on the temperature (2 × 106 values) of ground beef stored in its bottom drawer set to Fresh Meat (0 °C). Effects analyzed were ambient temperature (LT, 21.1 °C/HT, 32.2 °C), food load (LL, 22.5 kg/HL, 39.0 kg), door openings, and refrigerator compressor mode (SS, single speed/VS, variable speed). Published predictive microbiology models for exponential growth and ground beef temperature (48-h tests) were used to define absolute (API) and relative preservation indicators (RPI). For VS mode, API (log CFU/g) ranged from 1.7 (LT/LL) to 3.1 (HT/HL) for Listeria monocytogenes and 1.6 to 2.5 for Pseudomonas putida. Ground beef temperature reductions with minimum freezing risk yielded significantly lower API values indicating the need for refrigerator settings below 5 °C. Probabilistic analysis considering the model and temperature measurement variability confirmed this need. At 2 °C as recommended for ground beef storage, API2°C would be 1.1 (L. monocytogenes) and 1.4 logCFU/g (P. putida). RPI defined as the ratio of experimental API over API2°C yielded values > 1 confirming that a refrigerator control logic must consider preservation in addition to energy use compliance. In this study, SS outperformed energy-efficient VS compressors; however, compressor optimization considering energy use and food preservation would favor the latter. In conclusion, API and RPI values were effective tools to assess the microbial food preservation performance of a refrigerator and its use could be extended to analyze any segment of the refrigerated food distribution chain. Transforming time-temperature data into microbial performance indicators is practical and cost-effective.

Modelling of the Behaviour of Salmonella enterica serovar Reading on Commercial Fresh-Cut Iceberg Lettuce Stored at Different Temperatures.

The aim of this study was to model the growth and survival behaviour of Salmonella Reading and endogenous lactic acid bacteria on fresh pre-cut iceberg lettuce stored under modified atmosphere packaging for 10 days at different temperatures (4, 8 and 15 °C). The Baranyi and Weibull models were satisfactorily fitted to describe microbial growth and survival behaviour, respectively. Results indicated that lactic acid bacteria (LAB) could grow at all storage temperatures, while S. Reading grew only at 15 °C. Specific growth rate values (μmax) for LAB ranged between 0.080 and 0.168 h−1 corresponding to the temperatures 4 and 15 °C while for S. Reading at 15 °C, μmax = 0.056 h−1. This result was compared with published predictive microbiology models for other Salmonella serovars in leafy greens, revealing that predictions from specific models could be valid for such a temperature, provided they were developed specifically in lettuce regardless of the type of serovars inoculated. The parameter delta obtained from the Weibull model for the pathogen was found to be 16.03 and 18.81 for 4 and 8 °C, respectively, indicating that the pathogen underwent larger reduction levels at lower temperatures (2.8 log10 decrease at 4 °C). These data suggest that this Salmonella serovar is especially sensitive to low temperatures, under the assayed conditions, while showcasing that a correct refrigeration could be an effective measure to control microbial risk in commercial packaged lettuce. Finally, the microbiological data and models from this study will be useful to consider more specifically the behaviour of S. Reading during transport and storage of fresh-cut lettuce, elucidating the contribution of this serovar to the risk by Salmonella in leafy green products.