Validation of Lag Time and Growth Rate Models forSalmonellaTyphimurium: Acceptable Prediction Zone Method

Recognition of environmental contaminant and pathogenic bacteria by means of redox potential methodology

The time-consuming nature of culturing methods has urged the exploration of rapid modern technologies. One promising alternative utilizes redox potential, which describes the oxidative changes within complex media, indicating oxygen and nutrient consumption, as well as the production of reduced substances in the investigated biological system. Redox potential measurement can detect microbial activity within 16 h, what is significantly faster than the minimum 24 h incubation time of the reference plate counting technique. The redox potential based method can be specific with selective media, but bacterial strains have unique kinetic pattern as well. The proposed method suggests evaluation of the curve shape for the differentiation of environmental contaminant and pathogenic microbial strains. Six bacterial species were used in validation (Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Listeria innocua, Listeria monocytogenes, and Listeria ivanovii). Descriptive parameters reached 98.2 % accuracy and Gompertz model achieved 91.6 % accuracy in classification of the selected 6 bacteria species.•Mathematical model (Gompertz function) and first order descriptive parameters are suggested to describe the specific shape of redox potential curves, while Support Vector Machine (SVM) is recommended for classification.•Due to the concentration dependent time to detection (TTD), pre-processing applies standardization according to the inflection point time.

Machine Learning Prediction of Leuconostoc spp. Growth Inducing Spoilage in Cooked Deli Foods Considering the Effect of Glycine and Sodium Acetate

To control spoilage by lactic acid bacteria (Leuconostoc spp.) in cooked deli food, various combinations of environmental and/or intrinsic factors have been employed based on hurdle technology. Since many factors and their combinations greatly influence Leuconostoc spp. growth, this study aimed to develop a machine learning model based on the experimentally obtained growth kinetic data using extreme gradient boosting tree algorithm to quantitatively and flexibly predict Leuconostoc spp. growth. In particular, the effects of sodium acetate (0-1.5%) and glycine (0-1.5%), which are frequently used food additives in the Japanese food industry, on the growth of Leuconostoc spp. in cooked deli foods were examined with a combination of temperature (5-25 °C) and pH (5.0-6.0) conditions. The developed machine learning model to predict the number of Leuconostoc spp. over time successfully demonstrates comparable accuracy in culture media to the conventional Baranyi model-based prediction. Furthermore, while the accuracy of the prediction by the machine learning model for cooked deli foods such as potato salad, Japanese simmered hijiki, and unohana evaluated by the proportion of relative error within the acceptable prediction range was 98%, the accuracy of the conventional Baranyi model-based prediction was 89%. The developed machine learning model successfully and flexibly predicted the growth of Leuconostoc spp. in various cooked deli foods incorporating the effect of food additives, with an accuracy comparable to or better than that of the conventional kinetic-based model.

Kinetic study of the thermal inactivation of Weizmannia coagulans during food thermal processing

Weizmannia coagulans has attracted attention due to its remarkable health benefits for human, but the dynamic changes of its viable bacteria during thermal processing have been less reported. In this study, a predictive model for the survival of Weissmanella coagulans during thermal processing of food was developed and validated during the processing of coffee, tea, instant noodles, calcium milk biscuits, muffin cake and steamed buns. The kinetics of heat inactivation activities of Weizmannia coagulans VHProbi C08 and Weizmannia coagulans GBI-30, 6086 at 85, 95, 105, 110 and 115 °C were investigated, and their coefficients of determination were greater than 0.91 and 0.87, and the root-mean-square errors were less than 0.64 and 0.43, respectively. The z-values of VHProbi C08 and GBI-30, 6086 were obtained by Bigelow model fitting as 36.1 °C and 36.9 °C, respectively. The developed prediction model was applied to the thermal processing of six food products and the measured values were all within ±0.5 Log10 (CFU/mL) of the predicted values, indicating high prediction accuracy. The model predicts the survival of Weissmanella coagulans simply by obtaining the initial number of viable bacteria and the change in temperature. These suggested that the model can be used as an effective tool to evaluate the stability of Weizmannia coagulans in food thermal processing.

Machine learning to predict the relationship between Vibrio spp. concentrations in seawater and oysters and prevalent environmental conditions

Vibrio parahaemolyticus and Vibrio vulnificus are bacteria with a significant public health impact. Identifying factors impacting their presence and concentrations in food sources could enable the identification of significant risk factors and prevent incidences of foodborne illness. In recent years, machine learning has shown promise in modeling microbial presence based on prevalent external and internal variables, such as environmental variables and gene presence/absence, respectively, particularly with the generation and availability of large amounts and diverse sources of data. Such analyses can prove useful in predicting microbial behavior in food systems, particularly under the influence of the constant changes in environmental variables. In this study, we tested the efficacy of six machine learning regression models (random forest, support vector machine, elastic net, neural network, k-nearest neighbors, and extreme gradient boosting) in predicting the relationship between environmental variables and total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater and oysters. In general, environmental variables were found to be reliable predictors of total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater, and pathogenic V. parahaemolyticus in oysters (Acceptable Prediction Zone >70 %) when analyzed using our machine learning models. SHapley Additive exPlanations, which was used to identify variables influencing Vibrio concentrations, identified chlorophyll a content, seawater salinity, seawater temperature, and turbidity as influential variables. It is important to note that different strains were differentially impacted by the same environmental variable, indicating the need for further research to study the causes and potential mechanisms of these variations. In conclusion, environmental variables could be important predictors of Vibrio growth and behavior in seafood. Moreover, the models developed in this study could prove invaluable in assessing and managing the risks associated with V. parahaemolyticus and V. vulnificus, particularly in the face of a changing environment.

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 39 h. 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.

Poultry Food Assess Risk Model for Salmonella and Chicken Gizzards: III. Dose Consumed Step

The Dose Consumed step of the Poultry Food Assess Risk Model (PFARM) for Salmonella and chicken gizzards was presented and compared to the Exposure Assessment step of Quantitative Microbial Risk Assessment (QMRA). The specific objectives were 1) to demonstrate the dose consumed step of PFARM for Salmonella and chicken gizzards; 2) to compare Salmonella dose consumed from cooked chicken gizzards to that from cross-contaminated and temperature-abused lettuce; 3) to determine if Salmonella dose consumed changed over time in a production chain; and 4) to compare PFARM and QMRA predictions of Salmonella dose consumed. The PFARM and QMRA were developed in an Excel notebook and simulated with @Risk. Salmonella prevalence and number data (P = 100) for chicken gizzards (56 g) and scenario analysis were used to address objectives 1, 2, and 4, whereas running windows of 60 consecutive chicken gizzard samples and scenario analysis were used to address objective 3. A lot size of 1,000 kg of chicken gizzards was simulated. Mean portion size was 168 g resulting in the simulation of 5,952 meals per lot. Of these, 3.69 ± 0.32% and 0.49 ± 0.07% (mean ± SD) resulted in Salmonella dose consumed of ≥1 per meal from cooked chicken gizzards and lettuce, respectively. However, the total Salmonella dose consumed per lot from cooked chicken gizzards (272 ± 27) was less (P ≤ 0.05) than from lettuce (6,050 ± 4,929) because of a few highly contaminated (>310 Salmonella) lettuce portions at consumption. Over time in the production chain, Salmonella prevalence and total dose consumed per lot changed (P ≤ 0.05) but the patterns differed. The QMRA predicted higher (P ≤ 0.05) Salmonella dose consumed per meal than PFARM. In part, this was because QMRA only simulated contaminated grams, whereas PFARM simulated contaminated and non-contaminated meals. However, other factors, which are discussed, also contributed to the overestimation of Salmonella dose consumed by QMRA.

Quality changes, potential spoilage organisms, and shelf-life prediction of brackish river prawn (Macrobrachium macrobrachion) at different storage temperatures

The brackish river prawn (Macrobrachium macrobrachion) is a species of commercial importance in West Africa. However, like other fishery products, it is prone to deterioration due mainly to microbial activities. The present study aimed at evaluating the spoilage characteristics of M. macrobrachion and predicting the growth of the main spoilage bacteria as well as the shelf-life of the product as a function of storage temperature. Freshly caught brackish river prawn samples from Lake Aheme were aerobically stored at 0, 7, 15, and 28 °C and, at pre-determined times during storage, they were taken for microbiological, chemical, and sensory analysis. At sensory rejection times, the spoilage potential of 185 isolates from specific groups of organisms enumerated was assessed in prawn of which the endogenous microbiota was heat inactivated. Isolates capable of producing strong off-odor were identified using 16S rRNA sequencing. Models predicting the maximum growth rate of Pseudomonas spp. and H2S-producing bacteria in the brackish river prawn as well as the shelf-life of the product were developed. These models were validated using an independent experiment during which prawn was stored at 0, 4, 10, and 25 °C. Results showed that Pseudomonas spp. at 0 °C, Pseudomonas spp. and H2S-producing bacteria at 7 °C, and H2S-producing bacteria at 15 °C and 28 °C were the dominant groups of microorganisms during storage. As expected, total volatile basic nitrogen, trimethylamine, and pH with initial values of 21.2 ± 3.0 mg-N/100 g, 4.1 ± 0.8 mg-N/100 g, and 7.46 ± 0.15 increased during storage reaching approximately 35 mg-N/100 g, 10 mg/ 100 g and 8, respectively at sensory rejection times which were 7 h at 28 °C, 1.2 d at 15 °C, 4.6 d at 7 °C, and 11.7 d at 0 °C. The main spoilage organisms were Citrobacter braakii at 28 °C, Citrobacter braakii, Pseudomonas kurunegalensis, and Shewanella bicestrii at 15 °C, Shewanella putrefaciens, Shewanella baltica, and Pseudomonas bubulae at 7 °C, and Pseudomonas versuta at 0 °C. The validation of the developed models showed an adequate agreement between the predicted and observed values. This study highlights the specific spoilage characteristics of the brackish river prawn and reveals that Gram-negative rod bacteria are the main spoilage organisms even at high storage temperatures, contrary to many earlier reports on the spoilage of tropical fishery products.

Modeling the combined resistance to microwave treatments and salt conditions of Escherichia coli and Staphylococcus aureus

In the present study, the efficiency of the combined effect of microwave irradiation treatments together with salt concentration was assessed against Escherichia coli and Staphylococcus aureus. Microbial survival has been modeled through a one-step Weibull equation considering the non-isothermal profiles during the heating treatments. Three sodium chloride concentrations 0.5%, 3.5%, and 8.5% (w/v) treated under three microwave power levels (450, 600, and 800 W) were studied. Predictive models were validated using the determination coefficient (R2), root mean squared error and the acceptable prediction zone with external data obtained from ultra high temperature milk. The results obtained suggested that increasing microwave power levels and decreasing salt concentrations led to a higher microbial inactivation, being the δ values (time for achieving a first decimal reduction) for E coli of 19.57 s at 800 W and 0.5% NaCl. In contrast, experimental data of S aureus showed a higher variability since it presented more resistance to the microwave treatments. The results obtained and generated models can be used as decision-making tools to set specific guidelines on microwave treatments for assuring food safety.

Modeling the Temperature Effect on the Growth of Uropathogenic Escherichia coli in Sous-Vide Chicken Breast

Uropathogenic Escherichia coli (UPEC) is known to cause 65-75% of human urinary tract infection (UTI) cases. Poultry meat is a reservoir of UPEC, which is suspected to cause foodborne UTIs. In the present study, we aimed to determine the growth potential of UPEC in ready-to-eat chicken breasts prepared by sous-vide processing. Four reference strains isolated from the urine of UTI patients (Bioresource Collection and Research Center [BCRC] 10,675, 15,480, 15,483, and 17,383) were tested by polymerase chain reaction assay for related genes to identify their phylogenetic type and UPEC specificity. A cocktail of these UPEC strains was inoculated into sous-vide cooked chicken breast at 103-4 colony-forming unit (CFU)/g and stored at 4°C, 10°C, 15°C, 20°C, 30°C, and 40°C. Changes in the populations of UPEC during storage were analyzed by a one-step kinetic analysis method using the U.S. Department of Agriculture [USDA] Integrated Pathogen Modeling Program-Global Fit [IPMP-Global Fit]. The results showed that the combination of the no lag phase primary model and the Huang square-root secondary model fitted well with the growth curves to obtain the appropriate kinetic parameters. This combination for predicting UPEC growth kinetics was further validated using it to study additional growth curves at 25°C and 37°C, which showed that the root mean square error, bias factor, and accuracy factor were 0.49-0.59 (log CFU/g), 0.941-0.984, and 1.056-1.063, respectively. In conclusion, the models developed in this study are acceptable and can be used to predict the growth of UPEC in sous-vide chicken breast.

Poultry Food Assess Risk Model for Salmonella and Chicken Gizzards: II. Illness Dose Step

The Illness Dose (ID) step of a Poultry Food Assess Risk Model (PFARM) for Salmonella and chicken gizzards (CGs) was shown in the present study. The illness dose is the minimum dose of Salmonella consumed that causes an illness. It depends on zoonotic potential (ZP) of Salmonella, food consumption behavior (FCB), and consumer health and immunity (CHI) or the disease triangle (DT). Zoonotic potential is the ability of Salmonella to survive, grow, and spread in the production chain or food and then cause illness in humans. Illness dose is predicted in PFARM using a DT, dose-response model (DRM) that was developed with human feeding trial (HFT) data and was validated with human outbreak investigation (HOI) data for Salmonella. The ability of the DT, DRM to predict DR data from HOI and HFT for Salmonella was quantified using the Acceptable Prediction Zones (APZ) method where acceptable performance occurred when the proportion of residuals in the APZ (pAPZ) was ≥ 0.7. United States, Centers for Disease Control and Prevention (CDC) data for human salmonellosis from 2007 to 2016 were used to simulate ZP and only minor changes in ZP of 11 Salmonella serotypes were observed during this time. The performance of the DT, DRM for predicting Salmonella DR data from HFT and HOI was acceptable with pAPZ that ranged from 0.87 to 1 for individual serotypes of Salmonella. Simulation results from the DT, DRM in PFARM indicated that ID decreased (P ≤ 0.05) and ZP increased (P ≤ 0.05) over time in the simulated production chain because the main serotype of Salmonella changed from Kentucky (low ZP) to Infantis (high ZP) while FCB and CHI were held constant. These results indicated that the DT, DRM in PFARM can be used with confidence to predict ID as a function of ZP, FCB, and CHI. In other words, the DT, DRM in PFARM can be used with confidence to predict dose-response for Salmonella and CGs.

Thermal inactivation kinetics of uropathogenic Escherichia coli in sous-vide processed chicken breast

Chicken is a suspected reservoir of uropathogenic Escherichia coli (UPEC), resulting in foodborne urinary tract infections (UTIs). Sous-vide ready-to-eat (RTE) food products may be associated with microbial hazards due to the low-temperature long-time (LTLT) process. However, little is known regarding the survival of UPEC during sous-vide cooking. The aim of this study was to evaluate the heat resistance of UPEC in chicken breast during sous-vide processing and establish predictive inactivation models. Chicken breast samples were inoculated with a four-strain cocktail of UPEC, including reference strains from UTI patients and chicken isolates. The inoculated samples, with or without 3% NaCl solution for marination, were vacuum sealed in bags, immersed in a temperature-controlled water bath, and cooked at 50 °C, 55 °C, 60 °C, and 63 °C. The change in survival of populations of UPEC was fitted with the linear and Weibull inactivation models to obtain the survival curves at different temperatures; the D- and z-values were also calculated. The goodness-of-fit was evaluated using the root mean square error (RMSE), sum of squared errors (SSE), adjusted R², and Akaike information criterion (AIC). The results showed that the linear model with tail was better than the Weibull model in terms of fitting performance. With the addition of salt marinade, D-values at 50 °C, 55 °C, 60 °C, and 63 °C determined by the linear model with tail decreased from 299.78 to 166.93 min, 16,60 to 13.87 min, 4.06 to 3.05 min, and 1.05 to 0.87 min, respectively, compared with the controls. The z-values of control and salt-marinated samples were 6.14 °C and 5.89 °C, respectively. The model developed for predicting UPEC survival under sous-vide cooking was validated using an additional survival curve at 58 °C. The validation results showed that the RMSE was 0.122 and 0.133 log CFU/g, and the proportion of relative error was 0.875 and 0.750 in the acceptable prediction zones for the control and salt-marinated samples, respectively. In conclusion, the heat resistance of an emerging foodborne pathogen, UPEC, in sous-vide processed chicken breast was revealed for the first time. Our results showed that salt marinade (3% NaCl) increases the heat sensitivity of UPEC during the sous-vide processing. The developed survival functions based on the linear model with tail can be applied to control the thermal lethality of UPEC.

Distinct Survival, Growth Lag, and rRNA Degradation Kinetics during Long-Term Starvation for Carbon or Phosphate

The stationary phase is the general term for the state a bacterial culture reaches when no further increase in cell mass occurs due to exhaustion of nutrients in the growth medium. Depending on the type of nutrient that is first depleted, the metabolic state of the stationary phase cells may vary greatly, and the subsistence strategies that best support cell survival may differ. As ribosomes play a central role in bacterial growth and energy expenditure, ribosome preservation is a key element of such strategies. To investigate the degree of ribosome preservation during long-term starvation, we compared the dynamics of rRNA levels of carbon-starved and phosphorus-starved Escherichia coli cultures for up to 28 days. The starved cultures' contents of full-length 16S and 23S rRNA decreased as the starvation proceeded in both cases, and phosphorus starvation resulted in much more rapid rRNA degradation than carbon starvation. Bacterial survival and regrowth kinetics were also quantified. Upon replenishment of the nutrient in question, carbon-starved cells resumed growth faster than cells starved for phosphate for the equivalent amount of time, and for both conditions, the lag time increased with the starvation time. While these results are in accordance with the hypothesis that cells with a larger ribosome pool recover more readily upon replenishment of nutrients, we also observed that the lag time kept increasing with increasing starvation time, also when the amount of rRNA per viable cell remained constant, highlighting that lag time is not a simple function of ribosome content under long-term starvation conditions. IMPORTANCE The exponential growth of bacterial populations is punctuated by long or short periods of starvation lasting from the point of nutrient exhaustion until nutrients are replenished. To understand the consequences of long-term starvation for Escherichia coli cells, we performed month-long carbon and phosphorus starvation experiments and measured three key phenotypes of the cultures, namely, the survival of the cells, the time needed for them to resume growth after nutrient replenishment, and the levels of intact rRNA preserved in the cultures. The starved cultures' concentration of rRNA dropped with starvation time, as did cell survival, while the lag time needed for regrowth increased. While all three phenotypes were more severely affected during starvation for phosphorus than for carbon, our results demonstrate that neither survival nor lag time is correlated with ribosome content in a straightforward manner.

Predictive model for growth of Clostridium botulinum from spores during cooling of cooked ground chicken

Cooking temperature of poultry meat is typically inadequate to inactivate the heat resistant spores of Clostridium botulinum. The purpose of this study is to develop a predictive model for C. botulinum during cooling of cooked ground chicken. Cooked chicken was inoculated with a cocktail of five strains of proteolytic C. botulinum type A and five strains of proteolytic C. botulinum type B to yield a final spore concentration of approximately 2 log CFU/g. The growth of C. botulinum was determined at constant temperatures from 10 to 46 °C. Dynamic temperature experiments were performed with continued cooling from 54.4 to 4.4 °C or 7.2 °C in mono- or bi-phasic cooling profiles, respectively. The Baranyi primary model was used to fit growth data and the modified Ratkowsky secondary model was used to fit growth rates with respect to temperature. The primary models fitted the growth data well (R² values ranging from 0.811 to 0.988). The R² and root mean square error (RMSE) of the modified Ratkowsky secondary model were 0.95 and 0.06, respectively. Out of 11 prediction error values calculated in this study, ten were within the limit of acceptable prediction zone (-1.0 to 0.5), indicating a good fit of the model. The predictive model will assist institutional food service operations in determining the safety of cooked ground chicken subjected to different cooling periods.

Predictive model for growth of Clostridium perfringens during cooling of cooked pork supplemented with sodium chloride and sodium pyrophosphate

A dynamic model was developed to predict growth of Clostridium perfringens in cooked ground pork supplemented with salt (0-3% wt/wt) and sodium pyrophosphate (0-0.3% wt/wt) under varying temperatures. C. perfringens (NCTC 8238, NCTC 8239, and NCTC 10240) spores were heat shocked, cooled, and inoculated into ground pork. Isothermal bacterial growth was quantified with variable salt and phosphate concentrations at temperatures ranging from 15 to 51 °C. The primary Baranyi model was fitted to all C. perfringens growth profiles and gave a satisfactory fit (R² ≥ 0.85). A quadratic polynomial secondary model was developed (P < 0.0001) to predict the maximum specific growth rate as a function of temperature, salt, and phosphate concentrations (R² = 0.93). A dynamic model was developed and validated using growth data retrieved from 7 published studies. Thirty three out of 44 predictions were within the acceptable prediction zone (-0.5 ≤ prediction error ≤ 1.0). The developed predictive model can be used to minimize the risk of C. perfringens in pork products supplemented with additives during cooling.

Modeling Salmonella spp. inactivation in chicken meat subjected to isothermal and non-isothermal temperature profiles

Salmonella genus has foodborne pathogen species commonly involved in many outbreaks related to the consumption of chicken meat. Many studies have aimed to model bacterial inactivation as a function of the temperature. Due to the large heterogeneity of the results, a unified description of Salmonella spp. inactivation behavior is hard to establish. In the current study, by evaluating the root mean square errors, mean absolute deviation, and Akaike and Bayesian information criteria, the double Weibull model was considered the most accurate primary model to fit 61 datasets of Salmonella inactivation in chicken meat. Results can be interpreted as if the bacterial population is divided into two subpopulations consisting of one more resistant (2.3% of the total population) and one more sensitive to thermal stress (97.7% of the total population). The thermal sensitivity of the bacteria depends on the fat content of the chicken meat. From an adapted version of the Bigelow secondary model including both temperature and fat content, 90% of the Salmonella population can be inactivated after heating at 60 °C of chicken breast, thigh muscles, wings, and skin during approximately 2.5, 5.0, 9.5, and 57.4 min, respectively. The resulting model was applied to four different non-isothermal temperature profiles regarding Salmonella growth in chicken meat. Model performance for the non-isothermal profiles was evaluated by the acceptable prediction zone concept. Results showed that >80% of the predictions fell in the acceptable prediction zone when the temperature changes smoothly at temperature rates lower than 20 °C/min. Results obtained can be used in risk assessment models regarding contamination with Salmonella spp. in chicken parts with different fat contents.

Modelling viability of Listeria monocytogenes in paneer

Paneer is a fresh, soft ready-to-eat cheese that is susceptible to Listeria monocytogenes contamination, exemplified by product recalls in Australia, Canada, and the USA. Previous research demonstrates that L. monocytogenes grows in paneer, however there are no paneer-specific predictive models that quantify the effect of environmental conditions on L. monocytogenes viability. This study measured the viability of a five-strain cocktail of L. monocytogenes in freshly prepared paneer incubated at 4-40 °C. Growth rates were fitted with the extended Ratkowsky square root model, with growth rates ranging from 0.014 to 0.352 log₁₀ CFU/h. In comparison with published models, only the ComBase L. monocytogenes broth model acceptably predicted growth (B_f = 1.01, A_f = 1.12) versus the developed model. The influence of paneer pH (5.0-6.0) and storage temperature (41-45 °C) on L. monocytogenes growth at the upper temperature growth boundary was described using a logistic model. These models provide quantitative tools to improve the safety of paneer processing conditions, shelf-life estimation, food safety management plans, and risk assessment.

Quality and Shelf-Life Modeling of Frozen Fish at Constant and Variable Temperature Conditions

The objective of this study was the investigation of the effect of variable conditions on quality parameters and the shelf life of fish during frozen storage. Three different fish products were tested, i.e., gilthead sea bream (Sparus aurata) fillets, sea bass (Dicentrarchus labrax) fillets, and yellowfin tuna (Thunnus albacares) slices stored in the range of −5 to −15 °C. The kinetic modeling of different shelf-life indices was conducted. Sensory scoring of frozen fish showed high correlation with color (L-value) and total volatile basic nitrogen (TVBN). The temperature dependence of the rates of quality degradation was expressed via the activation energy values, calculated via the Arrhenius equation, and ranged, for the tested quality indices, between 49 and 84 kJ/mol. The estimated kinetic parameters were validated at dynamic conditions and their applicability in real conditions was established, allowing for their practical application as tools for cold chain management.

Modelling the biphasic growth of non-starter lactic acid bacteria on starter-lysate as a substrate

Since cheese is poor in energy for bacterial growth, it is believed that non-starter lactic acid bacteria growth and flavour development are supported by the nutrients from lysis of the starter culture. This study was performed to investigate the dynamics of interaction between starter and non-starter strains from cheese. A starter culture lysate was prepared by enzymatic digestion and tested as a growth substrate for Lactobacillus sp. strains. The two starter culture strains of Lactococcus lactis were also tested on the starter-lysate. All seventeen strains were individually inoculated at the level of 5.0 log₁₀ cfu mL^-1 in M17 broth, with or without 10% starter-lysate, and incubated at 30 °C for 140 h. The optical density_{600 nm} was modelled with the primary log-transformed Logistic model with delay and lag phase duration, maximum specific growth rate as well as maximum population density obtained. Biphasic growth was mainly observed when the strains were able to utilize the starter-lysate as an energy source. To deal with the lack-of-fit related to the biphasic growth, the observed data points of the curve were divided after graphic evaluation and according to deviation of the residuals from the range ±0.05. Modelling was then performed in two phases by applying the same primary Logistic model in each of the two parts of the growth curve. Values of root-mean-square error and graphic evaluation indicated the good fitting of the data with the suggested approach. The growth of the two Lactococcus lactis strains was not affected by the starter-lysate. However, thirteen of the non-starter strains had their growth rates increased. The increase was greatest for Lactobacillus rhamnosus KU-LbR1, which reached maximum optical densities of 0.23 and 0.58 in the absence and the presence of starter-lysate, respectively. No effect of the starter-lysate was shown for the growth of Lactobacillus curvatus strains. The extend of the growth of non-starter strains on the starter-lysate was shown to be species and strain dependent.

Quantifying the bioprotective effect of Lactobacillus sakei CTC494 against Listeria monocytogenes on vacuum packaged hot-smoked sea bream

In this study, the bioprotective potential of Lactobacillus sakei CTC494 against Listeria monocytogenes CTC1034 was evaluated on vacuum packaged hot-smoked sea bream at 5 °C and dynamic temperatures ranging from 3 to 12 °C. The capacity of three microbial competition interaction models to describe the inhibitory effect of L. sakei CTC494 on L. monocytogenes was assessed based on the Jameson effect and Lotka-Volterra approaches. A sensory analysis was performed to evaluate the spoiling capacity of L. sakei CTC494 on the smoked fish product at 5 °C. Based on the sensory results, the bioprotection strategy against the pathogen was established by inoculating the product at a 1:2 ratio (pathogen:bioprotector, log CFU/g). The kinetic growth parameters of both microorganisms were estimated in mono-culture at constant storage (5 °C). In addition, the inhibition function parameters of the tested interaction models were estimated in co-culture at constant and dynamic temperature storage using as input the mono-culture kinetic parameters. The growth potential (δ log) of L. monocytogenes, in mono-culture, was 3.5 log on smoked sea bream during the experimental period (20 days). In co-culture, L. sakei CTC494 significantly reduced the capability of L. monocytogenes to grow, although its effectiveness was temperature dependent. The LAB strain limited the growth of the pathogen under storage at 5 °C (<1 log increase) and at dynamic profile 2 (<2 log increase). Besides, under storage at dynamic profile 1, the growth of L. monocytogenes was inhibited (<0.5 log increase). These results confirmed the efficacy of L. sakei CTC494 for controlling the pathogen growth on the studied fish product. The Lotka-Volterra competition model showed slightly better fit to the observed L. monocytogenes growth response than the Jameson-based models according to the statistical performance. The proposed modelling approach could support the assessment and establishment of bioprotective culture-based strategies aimed at reducing the risk of listeriosis linked to the consumption of RTE hot-smoked sea bream.

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 predictive growth model for Clostridium botulinum during cooling of cooked uncured ground beef

A dynamic model to predict the germination and outgrowth of Clostridium botulinum spores in cooked ground beef was presented. Raw ground beef was inoculated with a ten-strain C. botulinum spore cocktail to achieve approximately 2 log spores/g. The inoculated ground beef was vacuum packaged, cooked to 71 °C to heat shock the spores, cooled to below 10 °C, and incubated isothermally at temperatures from 10 to 46 °C. C. botulinum growth was quantified and fitted into the primary Baranyi Model. Secondary models were fitted to maximum specific growth rate and lag phase duration using Modified Ratkowsky equation (R² 0.96) and hyperbolic function (R² 0.94), respectively. Similar experiments were also performed under non-isothermal (cooling) conditions. Acceptable zone prediction (APZ) analysis was conducted on growth data collected over 3 linear cooling regimes from the current study. The model performance (prediction errors) for all 22 validation data points collected in the current work were within the APZ limits (-1.0 to +0.5 log CFU/g). Additionally, two other growth data sets of C. botulinum reported in the literature were also subjected to the APZ analysis. In these validations, 20/22 and 10/14 predictions fell within the APZ limits. The model presented in this work can be employed to predict C. botulinum spore germination and growth in cooked uncured beef under non-isothermal conditions. The beef industry processors and food service organizations can utilize this predictive microbial model for cooling deviations and temperature abused situations and in developing customized process schedules for cooked, uncured beef products.

A Predictive Model for Survival of Escherichia coli O157:H7 and Generic E. coli in Soil Amended with Untreated Animal Manure

This study aimed at developing a predictive model that captures the influences of a variety of agricultural and environmental variables and is able to predict the concentrations of enteric bacteria in soil amended with untreated Biological Soil Amendments of Animal Origin (BSAAO) under dynamic conditions. We developed and validated a Random Forest model using data from a longitudinal field study conducted in mid-Atlantic United States investigating the survival of Escherichia coli O157:H7 and generic E. coli in soils amended with untreated dairy manure, horse manure, or poultry litter. Amendment type, days of rain since the previous sampling day, and soil moisture content were identified as the most influential agricultural and environmental variables impacting concentrations of viable E. coli O157:H7 and generic E. coli recovered from amended soils. Our model results also indicated that E. coli O157:H7 and generic E. coli declined at similar rates in amended soils under dynamic field conditions.The Random Forest model accurately predicted changes in viable E. coli concentrations over time under different agricultural and environmental conditions. Our model also accurately characterized the variability of E. coli concentration in amended soil over time by providing upper and lower prediction bound estimates. Cross-validation results indicated that our model can be potentially generalized to other geographic regions and incorporated into a risk assessment for evaluating the risks associated with application of untreated BSAAO. Our model can be validated for other regions and predictive performance also can be enhanced when data sets from additional geographic regions become available.

Microbial interaction between Salmonella enterica and main postharvest fungal pathogens on strawberry fruit

The microbial interaction between Salmonella enterica and the main postharvest fungal pathogens of strawberries was evaluated. Inoculation of fungal suspension was done 2 (D2) and 1 (D1) day(s) before and at the same time (D0) as S. enterica. Fruits were stored at 20 °C and 4 °C. At both temperatures, Botrytis cinerea and Rhizopus stolonifer caused a decrease in S. enterica population. Treatments where the mould was inoculated (D2, D1 and D0) achieved a significant logarithmic reduction (P < 0.05) of S. enterica populations after 48 h (20 °C) and 14 days (4 °C) compared to fungal-uninoculated fruits (CK). Regarding temperature, average reductions were significantly higher at 4 °C (3.38 log₁₀ CFU/wound) than at 20 °C (1.16 log₁₀ CFU/wound) (P < 0.05). Average reductions comprising all treatments were 1.91 and 0.41 log₁₀ CFU/wound for B. cinerea and R. stolonifer at 20 °C, and 3.39 and 3.37 log₁₀ CFU/wound for B. cinerea and R. stolonifer at 4 °C. A linear log₁₀ model was fitted in order to predict the inactivation rate (k_max, log₁₀ CFU/h) of S. enterica. Inactivation rates were higher at 20 °C for D2 treatments than at 4 °C throughout the running time. The main inactivation rate was obtained for B. cinerea at 20 °C (0.160 ± 0.027/h), which was found to have stronger inhibitory activity against S. enterica than R. stolonifer. Univariate analysis ANOVA was carried out to evaluate the effect of different external variables on the inhibition of S. enterica. Results found that single effects were significant (P < 0.05) except for the pH. The inhibitory effect caused by the action of moulds in conjunction with some environmental factors could indicate the potential interactions between strawberry fungal pathogens and S. enterica.

Modification of a Predictive Model To Include the Influence of Fat Content on Salmonella Inactivation in Low-Water-Activity Foods

ABSTRACT

Low-water-activity (aw) foods (including those containing fat) are often implicated in outbreaks of Salmonella spp. The influence of fat content on survival in foods such as peanut butter remains unclear. Certain Salmonella serovars can survive for long periods in harsh temperatures and low moisture conditions. The objective of this study was to determine the influence of fat content on the survival of Salmonella in low-aw foods and expand an existing secondary inactivation model previously validated for lower-fat foods. Whey protein powder supplemented with peanut oil was equilibrated to five target aw values (aw < 0.60), inoculated with a dried four-strain cocktail of Salmonella, vacuum sealed, and stored at 22, 37, 50, 60, 70, and 80°C for 48 h, 28 days, or 168 days. Survival data were fitted to Weibull, Biphasic-linear, Double Weibull, and Geeraerd-tail models. The Weibull model was chosen for secondary modeling due to its ability to satisfactorily describe the data over most of the conditions under study. The influence of temperature, fat content, and aw on the Weibull model parameters was evaluated using nonlinear least squares regression, and a revised secondary model was developed based on parameter significance. Peanut butter, chia seed powder, toasted oat cereal, and animal crackers within the aw range of the model were used to validate the modified model within its temperature range. Fat content influenced survival in samples held at temperatures ≥50°C, whereas aw influenced survival at 37 and 70°C. The model predictions demonstrated improved % bias and % discrepancy compared with the previous model. Weibull model predictions were accurate and fail-safe in 38 and 58%, respectively, of the food and environmental conditions under study. Predictions were less reliable for peanut butter held at 80°C. This study provides data and a model that can aid in the development of risk mitigation strategies for low-aw foods containing fat.

HIGHLIGHTS

Evaluation of the effect of Lactobacillus sakei strain L115 on Listeria monocytogenes at different conditions of temperature by using predictive interaction models

In this study, the inhibitory capacity of Lactobacillus sakei strain L115 against Listeria monocytogenes has been assayed at 4, 8, 11, 15 and 20 °C in broth culture. Besides, the use of predictive microbiology models for describing growth of both microorganisms in monoculture and coculture has been proposed. A preliminary inhibitory test confirmed the ability of Lb. sakei strain L115 to prevent the growth of a five-strain cocktail of L. monocytogenes. Next, the growth of microorganisms in isolation, i.e. in monoculture, was monitored and kinetic parameters maximum specific growth rate (μ_sp;max) and maximum population density (N_max) were estimated by fitting the Baranyi model to recorded data. Inhibition coefficients (α) were calculated for the two kinetic parameters tested (μ_sp:max and N_max) to quantify the percentage of reduction of growth when the microorganisms were in coculture in comparison with monoculture. The kinetic parameters were input into three interaction models, developed based on modifications of the Baranyi growth model, namely Jameson effect, new modified version of the Jameson effect and Lotka-Volterra models. Two approaches were utilized for simulation, one using the monoculture μ_sp;max, under the hypothesis that the growth potential is similar under monoculture and coculture conditions provided the environmental conditions are not modified, and the other one, based on adjusting the monoculture kinetic parameter by applying the corresponding α to reproduce the observed μ_sp;max under coculture conditions, assuming, in this approach, that the existence of a heterogeneous population can change the growth potential of each microbial population. It was observed that in coculture, μ_sp;max of L. monocytogenes decreased (e.g., α = 31% at 4 °C) and the N_max was much lower than that of monoculture (e.g., α = 36% at 4 °C). The best simulation performance was achieved applying α to adjust the estimated monoculture growth rate, with the modified Jameson and Lotka-Volterra models showing better fit to the observed microbial interaction data as demonstrated by the fact that 100% data points fell within the acceptable simulation zone (±0.5 log CFU/mL from the simulated data). More research is needed to clarify the mechanisms of interaction between the microorganisms as well as the role of temperature.

Predictive Model of Listeria monocytogenes Growth in Queso Fresco

Listeria monocytogenes is a hardy psychrotrophic pathogen that has been linked to several cheese-related outbreaks in the United States, including a recent outbreak in which a fresh cheese (queso fresco) was implicated. The purpose of this study was to develop primary, secondary, and tertiary predictive models for the growth of L. monocytogenes in queso fresco and to validate these models using nonisothermal time and temperature profiles. A mixture of five strains of L. monocytogenes was used to inoculate pasteurized whole milk to prepare queso fresco. Ten grams of each fresh cheese sample was vacuum packaged and stored at 4, 10, 15, 20, 25, and 30°C. From samples at each storage temperature, subsamples were removed at various times and diluted in 0.1% peptone water, and bacteria were enumerated on Listeria selective agar. Growth data from each temperature were fitted using the Baranyi model as the primary model and the Ratkowsky model as the secondary model. Models were then validated using nonisothermal conditions. The Baranyi model was fitted to the isothermal growth data with acceptable goodness of fit statistics (R² = 0.928; root mean square error = 0.317). The Ratkowsky square root model was fitted to the specific growth rates at different temperatures (R² = 0.975). The tertiary model developed from these models was validated using the growth data with two nonisothermal time and temperature profiles (4 to 20°C for 19 days and 15 to 30°C for 11 days). Data for these two profiles were compared with the model prediction using an acceptable prediction zone analysis; >70% of the growth observations were within the acceptable prediction zone (between -1.0 and 0.5 log CFU/g). The model developed in this study will be useful for estimating the growth of L. monocytogenes in queso fresco. These predictions will help in estimation of the risk of listeriosis from queso fresco under extended storage and temperature abuse conditions.

Antimicrobial Activity of Oregano Essential Oil Incorporated in Sodium Alginate Edible Films: Control of Listeria monocytogenes and Spoilage in Ham Slices Treated with High Pressure Processing

The aim of the study was to evaluate the efficacy of oregano essential oil (OEO) incorporated in Na-alginate edible films when applied to sliced ham inoculated with a cocktail of Listeria monocytogenes strains, with or without pretreatment by high pressure processing (HPP). Microbiological, physicochemical and sensory analyses (in Listeria-free slices) were performed, while, the presence/absence and the relative abundance of each Listeria strain, was monitored by pulsed field gel electrophoresis (PFGE). The OEO incorporation in the films, caused approximately 1.5 log reduction in Listeria population at 8 and 12 °C at the end of the storage period, and almost 2.5 log reduction at 4 °C. The HPP treatment caused 1 log reduction to the initial Listeria population, while levels kept on decreasing throughout the storage for all the tested temperatures. The pH of the samples was higher in the cases where HPP was involved, and the samples were evaluated as less spoiled. Furthermore, the presence of OEO in the films resulted in color differences compared to the control samples, whilst the aroma of these samples was improved. In conclusion, the combined application of HPP and OEO edible films on the slices, led to a significant reduction or absence of the pathogen.

Modelling inactivation of Staphylococcus spp. on sliced Brazilian dry-cured loin with thermosonication and peracetic acid combined treatment

Ultrasound (US) has a high capacity to increase food safety. Although high and/or moderate temperature in combination with US has been studied, the knowledge about cooling/low temperatures as well as its combined effect with chemical preservation methods is scarce. Therefore, the aim of this study was to describe the inactivation of Staphylococcus spp. (SA) present in the natural microbiota of sliced Brazilian dry-cured loin (Socol, BDL) using US (40 kHz and 5.40 W/g) at 1.6-17.9 kJ/g, temperature (T) between 6.4 and 73.6 °C and peracetic acid (PA) between 5.5 and 274.5 mg/L employing the Central Composite Rotatable Design. The model fully describes how the combination of US, T, and PA affects SA inactivation. In BDL, an increase in US acoustic energy density (kJ/g) allows the reduction of T necessary to inactivate SA because of the occurrence of synergistic effect. However, US applied at low T was inefficient. On the other hand, PA was more efficient at low T, since high T degraded this compound at different rates according to the holding T. Therefore, the data indicates a relation between the technologies used in the combined decontamination of sliced BDL improving dry-cured meat safety.

Shelf life prediction and food safety risk assessment of an innovative whole soybean curd based on predictive models

The aim of the present study is to predict the shelf life and evaluate the risk profile of an innovative whole soybean curd (WSC). Two main spoilage strains were isolated from spoiled WSC and identified as B. subtilis and B. cereus. The origin analysis confirmed that B. subtilis and B. cereus originated from soybean materials and survived in soybean curd. For microbial contamination analysis, thermotolerant coliforms, E. coli and S. aureus were not detected in soybean curd. The predicted shelf life of WSC and okara-filtered curd that was stored at 10 °C were 141.95 h (5.91 d) and 206.25 h (8.59 d), respectively. Moreover, the models applied in this study exhibited great fitting goodness and the predicted growth parameters were fail-safe. To conclude, introduction of okara into soybean curd reinforced the initial contamination level but didn't significantly increase the risk profile of WSC.

Predictive Model for Growth of Bacillus cereus at Temperatures Applicable to Cooling of Cooked Pasta

A model was developed to predict the growth of Bacillus cereus from spores during cooling of cooked pasta. Cooked pasta was inoculated with a cocktail of four strains of heat-shocked (80 °C/10 min) B. cereus spores to obtain a final spore concentration of approximately 2 log CFU/g. Thereafter, growth was determined at isothermal temperatures starting at 10 °C and every three degrees up to 49 °C. Samples were removed periodically and plated on mannitol egg yolk polymyxin agar. The plates were incubated for 24 hr at 30 °C. Baranyi, Huang, and modified Gompertz primary growth models were used to fit growth data. The modified Ratkowsky secondary model was used to fit growth rates determined by the primary growth models with respect to temperature. All three primary models fitted the growth data well. The modified Ratkowsky secondary model adequately fit growth rates generated by the three primary models (R² values ranging from 0.96 to 0.98). After acceptable prediction zone (APZ) validation and goodness of fit statistical analyses, it was determined that the Baranyi primary growth model was best suited for these data. For both single-rate exponential cooling and biphasic linear cooling model validation, all Baranyi model predictions (n = 24 and 28, respectively) fell within the APZ (-1.0 to 0.5 log CFU/g). The model will assist institutional food service settings to determine the safety of cooked pasta subjected to longer cooling times or stored at improper temperatures. PRACTICAL APPLICATION: Predictive model can be used to estimate extent of microbial growth during cooling of cooked pasta and in designing HACCP program and setting of critical control levels. Retail food industry would need fewer challenge studies to validate the safety of their products. The model will provide regulatory agencies and food industry with an objective means of assessing the microbial risk and ensuring that the public is not at risk of acquiring food poisoning.

Predicting the Growth of Listeria monocytogenes and Salmonella Typhimurium in Diced Celery, Onions, and Tomatoes during Simulated Commercial Transport, Retail Storage, and Display

Temperature is arguably the most important factor affecting microbial proliferation in fresh-cut produce. In this study, growth of Listeria monocytogenes in diced onions and celery and Salmonella Typhimurium in diced tomatoes was determined in modified atmosphere packages and snap-fit containers using three fluctuating temperature scenarios for transport, retail storage, and display. As expected, L. monocytogenes growth in diced onions and celery varied depending on the extent of temperature abuse, with exposure to high and intermediate temperature-abuse scenarios generally being growth supportive. A Baranyi primary model with a square-root secondary model for maximum growth rate, and a linear model for maximum population density, were used to estimate Listeria growth under fluctuating temperature. Accuracy and acceptability of the model prediction were evaluated in terms of root mean square error (RMSE) and acceptable prediction zone (APZ), respectively. Overall, growth predictions for L. monocytogenes were more accurate for celery (RMSE, 0.28 to 0.47) than onions (RMSE, 0.42 to 1.53) under the fluctuating temperature scenarios tested. However, both predictions yielded APZ values that ranged from 82 to 100% for celery and 36 to 78% for onions. In contrast, Salmonella Typhimurium populations increased more than 1 log CFU/g in diced tomatoes under the three fluctuating temperature scenarios studied. Overall, these diced products packaged under a high-oxygen atmosphere showed decreased pathogen growth compared with product stored in a passive modified atmosphere. Findings from this study will be particularly useful in assessing the risk associated with consumption of diced celery, tomatoes, and onions and in designing effective packaging strategies to minimize pathogen growth in fresh-cut produce.

Risk Assessment for Salmonellosis in Chicken in South Korea: The Effect of Salmonella Concentration in Chicken at Retail

Salmonellosis caused by chicken consumption has been a critical issue in food safety worldwide, including in Korea. The probability of illness from consumption of chicken was simulated in study, based on the recipe of Dakgalbi, a commonly eaten chicken dish in Korea. Additionally, the processing stage at slaughterhouses to decrease Salmonella concentration in broilers was modeled to explore its effect on the likelihood of illness. A Monte Carlo simulation model was created using @RISK. Prevalence of Salmonella in chickens at the retail stage was found to be predominantly important in determining the probability of illness. Other than the prevalence, cooking temperature was found to have the largest impact on the probability of illness. The results also demonstrated that, although chlorination is a powerful tool for decreasing the Salmonella concentration in chicken, this effect did not last long and was negated by the following stages. This study analyzes the effects of variables of the retail-to-table pathway on the likelihood of salmonellosis in broiler consumption, and also evaluates the processing step used to decrease the contamination level of Salmonella in broilers at slaughterhouses. According to the results, we suggest that methods to decrease the contamination level of Salmonella such as chlorination had little effect on decreasing the probability of illness. Overall, these results suggest that preventing contamination of broiler with Salmonella must be a top priority and that methods to reduce the concentration of Salmonella in broilers at slaughterhouses hardly contribute to safe consumption of Salmonella-contaminated chicken.

Predicting heat process efficiency in thermal processes when bacterial inactivation is not log-linear

The food industry widely uses the F-value which considers microbial log-linear inactivation, while microbial heat inactivation may result in a non-log-linear inactivation pattern due to genetic or phenotypical heterogeneity. This may yield discrepancies in predicting microbial heat inactivation under dynamic conditions of heat treatment. In this paper, we suggest the calculation of the equivalent time of heat treatment at a given temperature to overcome these constraints. To validate our proposal, the heat inactivation of Bacillus pumilus, showing non-log-linear behavior, was predicted for 4 different heat inactivation profiles and bacterial enumeration was performed to determine whether prediction errors were acceptable. When the proportion of residuals in an acceptable zone from 1 log (fail safe) to 0.5 log (fail dangerous) was greater or equal to 70%, the model was considered as acceptable for predictions of the tested data. The new approach gave four different temperature profiles, with 96, 85, 85 and 100% of the residuals in the acceptable zone, indicating satisfactory prediction. Thus the proposed practical alternative to simulate microbial heat inactivation kinetics is able to extend the F-value to non-log-linear inactivation patterns.