3-D stochastic modeling approach in thermal inactivation: estimation of thermal survival kinetics of Escherichia coli O157:H7 in a hamburger after exposure to desiccation stress.

Desiccation tolerance of pathogenic bacteria is one strategy for survival in harsh environments, which has been studied extensively. However, the subsequent survival behavior of desiccation-stressed bacterial pathogens has not been clarified in detail. Herein, we demonstrated that the effect of desiccation stress on the thermotolerance of Escherichia coli O157:H7 in ground beef was limited, and its thermotolerance did not increase. E. coli O157:H7 was inoculated into a ground beef hamburger after exposure to desiccation stress. We combined a bacterial inactivation model with a heat transfer model to predict the survival kinetics of desiccation-stressed E. coli O157:H7 in a hamburger. The survival models were developed using the Weibull model for two-dimensional pouched thin beef patties (ca. 1 mm), ignoring the temperature gradient in the sample, and a three-dimensional thick beef patty (ca. 10 mm), considering the temperature gradient in the sample. The two-dimensional (2-D) and three-dimensional (3-D) models were subjected to stochastic variations of the estimated Weibull parameters obtained from 1,000 replicated bootstrapping based on isothermal experimental observations as uncertainties. Furthermore, the 3-D model incorporated temperature gradients in the sample calculated using the finite element method. The accuracies of both models were validated via experimental observations under non-isothermal conditions using 100 predictive simulations. The root mean squared errors in the log survival ratio of the 2-D and 3-D models for 100 simulations were 0.25-0.53 and 0.32-2.08, respectively, regardless of the desiccation stress duration (24 or 72 h). The developed approach will be useful for setting appropriate process control measures and quantitatively assessing food safety levels.IMPORTANCEAcquisition of desiccation stress tolerance in bacterial pathogens might increase thermotolerance as well and increase the risk of foodborne illnesses. If a desiccation-stressed pathogen enters a kneaded food product via cross-contamination from a food-contact surface and/or utensils, proper estimation of the internal temperature changes in the kneaded food during thermal processing is indispensable for predicting the survival kinetics of desiccation-stressed bacterial cells. Various survival kinetics prediction models that consider the uncertainty or variability of pathogenic bacteria during thermal processing have been developed. Furthermore, heat transfer processes in solid food can be estimated using finite element method software. The present study demonstrated that combining a heat transfer model with a bacterial inactivation model can predict the survival kinetics of desiccation-stressed bacteria in a ground meat sample, corresponding to the temperature gradient in a solid sample during thermal processing. Combining both modeling procedures would enable the estimation of appropriate bacterial survival kinetics in solid food.

Study on the quality and myofibrillar protein structure of chicken breasts during thawing of ultrasound-assisted slightly acidic electrolyzed water (SAEW).

The effects of air thawing (AT), water thawing (WT), slightly acidic electrolyzed water (ET), ultrasound-assisted water thawing (WUT) and ultrasound-assisted slightly acidic electrolyzed water (EUT) on the quality and myofibrillar protein (MP) structure of chicken breasts were investigated. The results showed that WUT and EUT could significantly improve the thawing rate compared with AT, WT, and ET groups. The EUT group not only had lower thawing loss, but also their immobilized and free water contents were similar to fresh sample according to the low-field nuclear magnetic resonance (LF NMR) results. The EUT treatment had no adverse effect on the primary structure of the protein. The secondary and tertiary structures of MP were more stable in the EUT group according to Raman and fluorescence spectra. The muscle fibers microstructure from EUT group was neater and more compact compared with other thawing methods. Therefore, EUT treatment could be considered as a novel potential thawing method in the food industry.

Thermal Stability of Fructooligosaccharides Extracted from Defatted Rice Bran: A Kinetic Study Using Liquid Chromatography-Tandem Mass Spectrometry.

Thermal degradation kinetics of fructooligosaccharides (FOS) in defatted rice bran were studied at temperatures of 90, 100, and 110 °C. FOS extracted from rice bran and dissolved in buffers at pH values of 5.0, 6.0, and 7.0 were prepared for the thermal treatments. The residual FOS (including 1-kestose (GF2), nystose (GF3), and 1F-fructofuranosylnystose (GF4)) contents were determined using the ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) method. The results showed that the thermal degradation kinetics of GF2, GF3, and GF4 followed a first-order kinetic model. Thermal degradation rate constants (k values) of GF2, GF3, and GF4 at different temperature and pH values were estimated using the first-order kinetic equation and SAS 9.1. As a result, these k values decreased gradually as the pH of the sample increased from 5.0 to 7.0. The Arrhenius model was applied to describe the heat dependence of the k-values. The activation energy (Ea) was calculated for each case of GF2, GF3, and GF4 degradation at pH values of 5.0, 6.0, and 7.0. The result showed that rice bran FOS is very thermostable at neutral pH while more labile at acidic pH.

Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning.

To predict bacterial population behavior in food, statistical models with specific function form have been applied in the field of predictive microbiology. Modelers need to consider the linear or non-linear relationship between the response and explanatory variables in the statistical modeling approach. In the present study, we focused on machine learning methods to skip definition of primary and secondary structure model. Support vector regression, extremely randomized trees regression, and Gaussian process regression were used to predict population growth of Escherichia coli O157 at 15 and 25 °C without defining the primary and secondary models. Furthermore, the support vector regression model was applied to predict small population of bacteria cells with probability theory. The model performance of the machine learning models were nearly equal to that of the current statistical models. Machine learning models have a potential for predicting bacterial population behavior.

Classification of food spoilage bacterial species and their sodium chloride, sodium acetate and glycine tolerance using chemometrics analysis and Raman spectroscopy.

The purpose is classification of stress tolerances of spoilage bacteria using Raman spectra and chemometrics. We obtained Raman spectra of six spoilage bacteria. Classification models were constructed with support vector machine and classified food-related stress tolerance with 90% accuracy, which provides bacterial characteristics specific to environment reducing food spoilage.

Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty.

Conventional regression analysis using the least-squares method has been applied to describe bacterial behavior logarithmically. However, only the normal distribution is used as the error distribution in the least-squares method, and the variability and uncertainty related to bacterial behavior are not considered. In this paper, we propose Bayesian statistical modeling based on a generalized linear model (GLM) that considers variability and uncertainty while fitting the model to colony count data. We investigated the inactivation kinetic data of Bacillus simplex with an initial cell count of 105 and the growth kinetic data of Listeria monocytogenes with an initial cell count of 104. The residual of the GLM was described using a Poisson distribution for the initial cell number and inactivation process and using a negative binomial distribution for the cell number variation during growth. The model parameters could be obtained considering the uncertainty by Bayesian inference. The Bayesian GLM successfully described the results of over 50 replications of bacterial inactivation with average of initial cell numbers of 101, 102, and 103 and growth with average of initial cell numbers of 10-1, 100, and 101. The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 101 were within the 95% prediction interval. In addition, parameter uncertainty could be expressed as an arbitrary probability distribution. The analysis procedures can be consistently applied to the simulation process through fitting. The Bayesian inference method based on the GLM clearly explains the variability and uncertainty in bacterial population behavior, which can serve as useful information for risk assessment related to food borne pathogens.

Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database.

In predictive microbiology, statistical models are employed to predict bacterial population behavior in food using environmental factors such as temperature, pH, and water activity. As the amount and complexity of data increase, handling all data with high-dimensional variables becomes a difficult task. We propose a data mining approach to predict bacterial behavior using a database of microbial responses to food environments. Listeria monocytogenes, which is one of pathogens, population growth and inactivation data under 1,007 environmental conditions, including five food categories (beef, culture medium, pork, seafood, and vegetables) and temperatures ranging from 0 to 25 °C, were obtained from the ComBase database ( www.combase.cc ). We used eXtreme gradient boosting tree, a machine learning algorithm, to predict bacterial population behavior from eight explanatory variables: 'time', 'temperature', 'pH', 'water activity', 'initial cell counts', 'whether the viable count is initial cell number', and two types of categories regarding food. The root mean square error of the observed and predicted values was approximately 1.0 log CFU regardless of food category, and this suggests the possibility of predicting viable bacterial counts in various foods. The data mining approach examined here will enable the prediction of bacterial population behavior in food by identifying hidden patterns within a large amount of data.

Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling.

This study was conducted to quantitatively evaluate the variability of stress resistance in different strains of Campylobacter jejuni and the uncertainty of such strain variability. We developed Bayesian statistical models with multilevel analysis to quantify variability within a strain, variability between different strains, and the uncertainty associated with these estimates. Furthermore, we measured the inactivation of 11 strains of C. jejuni in simulated gastric fluid with low pH, using the Weibullian survival model. The model was first developed for separate pH conditions and then analyzed over a range of pH levels. We found that the model parameters developed under separate pH conditions exhibited a clear dependence of survival on pH. In addition, the uncertainty of the variability between different strains could be described as the joint distribution of the model parameters. The latter model, including pH dependency, accurately predicted the number of surviving cells in individual as well as multiple strains. In conclusion, variabilities and uncertainties in inactivation could be simultaneously evaluated and interpreted via a probabilistic approach based on Bayesian theory. Such hierarchical Bayesian models could be useful for understanding individual-strain variability in quantitative microbial risk assessment. IMPORTANCE Since microbial strains vary in their growth and inactivation patterns in food materials, it is important to accurately predict these patterns for quantitative microbial risk assessment. However, most previous studies in this area have used highly resistant strains, which could lead to inaccurate predictions. Moreover, variability, including measurement errors and variability within a strain and between different strains, can contribute to predicted individual-level outcomes. Therefore, a multilevel framework is required to resolve these levels of variability and estimate their uncertainties. We developed a Bayesian predictive model for the survival of Campylobacter jejuni under simulated gastric conditions taking into account the variabilities and uncertainties. We demonstrated a high correspondence between predictions from the model and empirical measurements. The modeling procedure proposed in this study recommends a novel framework for predicting pathogen behavior, which can help improve quantitative microbial risk assessment during food production and distribution.

Application of growth rate from kinetic model to calculate stochastic growth of a bacteria population at low contamination level.

Traditional predictive microbiology is not suited for cell growth predictions for low-level contamination, where individual cell heterogeneity becomes apparent. Accordingly, we simulated a stochastic birth process of bacteria population using kinetic parameters. We predicted the variation in behavior of Salmonella enterica serovar Typhimurium cells at low inoculum density. The modeled cells were grown in tryptic soy broth at 25 °C. Kinetic growth parameters were first determined empirically for an initial cell number of 104 cells. Monte Carlo simulation based on the growth kinetics and Poisson distribution for different initial cell numbers predicted the results of 50 replicate growth experiments with the initial cell number of 1, 10, and 64 cells. Indeed, measured behavior of 85% cells fell within the 95% prediction area of the simulation. The calculations link the kinetic and stochastic birth process with Poisson distribution. The developed model can be used to calculate the probability distribution of population size for exposure assessment and for the evaluation of a probability that a pathogen would exceed critical contamination level during food storage.

Predicting sensory evaluation of spinach freshness using machine learning model and digital images.

The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels.

Prevalence and antimicrobial resistance of Shiga toxin-producing Escherichia coli in milk and dairy products in Egypt.

Food contaminated with Shiga toxin-producing Escherichia coli (STEC) represents a hazardous public health problem worldwide. Therefore, the present study was performed to elucidate the virulent and antimicrobial resistance characteristics of STEC isolated from milk and dairy products marketed in Egypt. A total of 125 samples (raw market milk, bulk tank milk, Kareish cheese, white soft cheese, and small scale-produced ice cream, 25 each) were collected for determination the prevalence and antimicrobial resistance profiling of STEC. Thirty-six STEC isolates were recovered from milk and dairy products. Serological analysis illustrated that three isolates were E. coli O157:H7 and 33 isolates belonged to different serotypes. Molecular examination indicated that all isolates harboured stx1 and/or stx2 genes, 14 isolates expressed eaeA gene and 3 isolates possessed rfbE gene. Antimicrobial resistance profiling of the isolates was both phenotypically and genetically examined. Interestingly, 31 out of 36 (86.11%) isolates were multidrug-resistant and harboured the extended-spectrum ß-lactamase encoding genes, namely, blaCTX-M-15, blaSHV-12 and blaCTX-M-14. Moreover, 12 isolates (33.33%) harboured plasmid-mediated quinolone resistant gene, qnrS. The overall conclusion of the current investigation indicated insufficient hygienic measures adopted during milking, handling, and processing leading to development of pathogenic and multidrug-resistant STEC.

Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling.

Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. In the context of microbial risk assessment, the uncertainty in the predicted microbial behavior can be an important component of the overall uncertainty. Conventional deterministic modeling approaches which provide point estimates of the pathogen's levels cannot quantify the uncertainty around the predictions. The objective of this study was to use Bayesian statistical modeling for describing uncertainty in predicted microbial thermal inactivation of Salmonella enterica Typhimurium DT104. A set of thermal inactivation data in broth with water activity adjusted to 0.75 at 9 different temperature conditions obtained from the ComBase database (www.combase.cc) was used. A log-linear microbial inactivation was used as a primary model while for secondary modeling, a linear relation between the logarithm of inactivation rate and temperature was assumed. For comparison, data were fitted with a two-step and a global Bayesian regression. Posterior distributions of model's parameters were used to predict Salmonella thermal inactivation. The combination of the joint posterior distributions of model's parameters allowed the prediction of cell density over time, total reduction time and inactivation rate as probability distributions at different time and temperature conditions. For example, for the time required to eliminate a Salmonella population of about 107 CFU/ml at 65°C, the model predicted a time distribution with a median of 0.40 min and 5th and 95th percentiles of 0.24 and 0.60 min, respectively. The validation of the model showed that it can describe successfully uncertainty in predicted thermal inactivation with most observed data being within the 95% prediction intervals of the model. The global regression approach resulted in less uncertain predictions compared to the two-step regression. The developed model could be used to quantify uncertainty in thermal inactivation in risk-based processing design as well as in risk assessment studies.

Calculating stochastic inactivation of individual cells in a bacterial population using variability in individual cell inactivation time and initial cell number.

The traditional log-linear inactivation kinetics model considers microbial inactivation as a process that follows first-order kinetics. A basic concept of log reduction is decimal reduction time (D-value), which means time/dose required to kill 90% of the relevant microorganisms. D-value based on the first-order survival kinetics model is insufficient for reliable estimations of bacterial survivors following inactivation treatment. This is because the model does not consider the inactivation curvature and variability in bacterial inactivation. However, although the D-value has some limitations, it is widely used for risk assessment and sterilization time estimation. In this study, stochastic inactivation models are used in place of the conventional D-value to describe the probability of a population containing survivors. As representative bacterial inactivation normally follows a log-linear or log-Weibull model, we calculate the time required for a specific decrease in the number of cells and the number of survival cells as a probability distribution using the stochastic inactivation of individual cells in a population. We compare the probability of a population containing survivors calculated via the D-value, an inactivation kinetics model, and the stochastic formula. The stochastic calculation can be approximately estimated via a kinetic curvature model with less than 5% difference below the probability of a population containing survivors 0.1. This stochastic formula indicates that the D-value model would over- or under-estimate the probability of a population containing survivors when applied to inactivation kinetics with curvature. The results presented in this study show that stochastic analysis using mathematical models that account for variability in the individual cell inactivation time and initial cell number would lead to a realistic and probabilistic estimation of bacterial inactivation.

Growth-Inhibitory Effect of d-Tryptophan on Vibrio spp. in Shucked and Live Oysters.

Vibrio vulnificus and Vibrio parahaemolyticus are important human pathogens that are frequently transmitted via consumption of contaminated raw oysters. A small amount of d-tryptophan (d-Trp) inhibits some foodborne pathogenic bacteria in high-salt environments. In this study, we aimed to evaluate the antibacterial effect of d-Trp on V. vulnificus and V. parahaemolyticus in culture media, artificial seawater, and shucked and live oysters. The effectiveness of d-Trp in growth inhibition of Vibrio spp. was highly dependent on environmental NaCl concentrations. Higher levels of NaCl (>4.0%) with d-Trp (>20 mM) resulted in higher and more consistent growth inhibition of both Vibrio spp. Treatment with 40 mM d-Trp significantly (P < 0.05) reduced viable V. parahaemolyticus cell counts in tryptic soy broth (TSB) with >4.0% NaCl at 25°C. In contrast, V. vulnificus was more sensitive to d-Trp (20 mM) than V. parahaemolyticus d-Trp (40 mM) treatment with NaCl (>4.5%) significantly (P < 0.05) inhibited the growth of V. parahaemolyticus and V. vulnificus in shucked oysters immersed in peptone water at 25°C throughout a 48-h incubation period. In artificial seawater, d-Trp exhibited a stronger growth-inhibitory effect on V. vulnificus and V. parahaemolyticus at 25°C than in TSB at the same level of salinity and inhibited the growth of both V. parahaemolyticus and V. vulnificus in live oysters at 25°C for 48 h. Furthermore, we tested the synergistic effect of d-Trp and salinity on the inhibition of total viable bacterial counts (TVC) at refrigeration temperature. d-Trp (40 mM) inhibited the growth of TVC in shucked oysters immersed in artificial seawater at 4°C. Therefore, these results revealed that d-Trp will serve as a novel and alternative food preservative to control Vibrio spp. in live oysters at ambient temperature and to extend the shelf-life of shucked oysters at refrigeration temperature.IMPORTANCE Oysters are the primary transmission vehicles for human Vibrio infections. Raw oyster consumption is frequently associated with gastroenteritis. The current postharvest methods, such as high-pressure processing, used to control Vibrio spp. in fresh oysters are still insufficient because of limited facilities, high cost, and potential adverse effects on production. We demonstrate that adding a small amount of d-tryptophan (d-Trp) inhibits the growths of Vibrio parahaemolyticus and Vibrio vulnificus in a high-salt environment at even ambient temperature. We further investigated the d-Trp treatment conditions and clarified the relationship between salt and d-Trp concentrations for optimal growth-inhibitory effect of Vibrio spp. The results will be useful for enhancing the effectiveness of d-Trp by increasing salinity levels. Furthermore, in a nutrientfree environment (artificial seawater), a stronger inhibitory effect could be observed at relatively lower salinity levels, indicating that d-Trp may be regarded as effective food preservation in terms of salinity reduction. Therefore, we suggest the use of exogenous d-Trp in a seawater environment as a novel and effective strategy not only for controlling Vibrio in live oysters at even ambient temperature but also for effectively retarding spoilage bacterial growth and extending the shelf life of shucked oysters at refrigeration temperature.

A Novel Approach to Predict the Growth of Staphylococcus aureus on Rice Cake.

This study aimed to investigate the growth kinetics of Staphylococcus aureus on rice cake and to determine the shelf life based on the probability model of the increase in S. aureus contamination on rice cake. Secondary models were developed based on the growth parameters derived from the Baranyi model at constant temperatures (15, 25, 35, and 45°C). External validation was then conducted using additional data under experimental conditions not used in development of the models to verify the performance and reliability of the developed model through different goodness-of-fit indices. Furthermore, the growth of S. aureus on rice cake under dynamic temperature was obtained with the root mean square error (RMSE) of 0.218 and the 90.9% acceptable prediction rate. In addition, probability models of the 1-, 2-, 3-, and 4-log increases of S. aureus on rice cake were also developed from the data, which could provide the probability and the time to a certain log increase. The results of validation demonstrated that the developed predictive model and the obtained growth parameters could be used for evaluating the growth behavior of S. aureus on rice cake under different conditions, and qualified to supply sufficient information for microbiological risk assessment studies of S. aureus on rice cake in Korea.

Alternative approach to modeling bacterial lag time, using logistic regression as a function of time, temperature, pH, and sodium chloride concentration.

The objective of this study was to develop a probabilistic model to predict the end of lag time (λ) during the growth of Bacillus cereus vegetative cells as a function of temperature, pH, and salt concentration using logistic regression. The developed λ model was subsequently combined with a logistic differential equation to simulate bacterial numbers over time. To develop a novel model for λ, we determined whether bacterial growth had begun, i.e., whether λ had ended, at each time point during the growth kinetics. The growth of B. cereus was evaluated by optical density (OD) measurements in culture media for various pHs (5.5 ∼ 7.0) and salt concentrations (0.5 ∼ 2.0%) at static temperatures (10 ∼ 20°C). The probability of the end of λ was modeled using dichotomous judgments obtained at each OD measurement point concerning whether a significant increase had been observed. The probability of the end of λ was described as a function of time, temperature, pH, and salt concentration and showed a high goodness of fit. The λ model was validated with independent data sets of B. cereus growth in culture media and foods, indicating acceptable performance. Furthermore, the λ model, in combination with a logistic differential equation, enabled a simulation of the population of B. cereus in various foods over time at static and/or fluctuating temperatures with high accuracy. Thus, this newly developed modeling procedure enables the description of λ using observable environmental parameters without any conceptual assumptions and the simulation of bacterial numbers over time with the use of a logistic differential equation.

Modeling of pathogen survival during simulated gastric digestion.

The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens.

Microbial Responses Viewer (MRV): a new ComBase-derived database of microbial responses to food environments.

ComBase is a large database of microbial responses to food environments and has attracted the attention of many researchers and food processors. Although ComBase contains a vast amount of data, it is not easy to obtain desired information from the retrieved data. In the present study, we developed a new ComBase-derived database (Microbial Responses Viewer, MRV) consisting of microbial growth/no growth data. The response was defined as representing "growth" if a significant increase in bacterial concentration (>1.0 log(10)) was observed. Alternatively, "growth" was defined as a positive value of the specific growth rate. The growth/no growth data of nineteen different microorganisms were extracted from all the data in ComBase comprising 29 kinds of microorganism. Furthermore, the specific growth rate of each microorganism was modelled as a function of temperature, pH, and water activity (a(w)) using a Poisson log-linear model, which is a family of generalized linear models (GLMs). For 16 of the 19 microorganisms, the specific growth/death rate was successfully modelled as a function of temperature, pH, and a(w) using GLM. The specific growth rate was illustrated using a two-dimensional contour plot with growth/no growth data. MRV provides information concerning growth/no growth boundary conditions and the specific growth rates of queried microorganisms. Using MRV, food processors can easily find the appropriate food design and processing conditions. This database will contribute to the efficient and safe production and distribution of processed foods.

Prediction of a required log reduction with probability for Enterobacter sakazakii during high-pressure processing, using a survival/death interface model.

A probabilistic model for predicting Enterobacter sakazakii inactivation in trypticase soy broth (TSB) and infant formula (IF) by high-pressure processing was developed. The modeling procedure is based on a previous model (S. Koseki and K. Yamamoto, Int. J. Food Microbiol. 116:136-143, 2007) that describes the probability of death of bacteria. The model developed in this study consists of a total of 300 combinations of pressure (400, 450, 500, 550, or 600 MPa), pressure-holding time (1, 3, 5, 10, or 20 min), temperature (25 or 40 degrees C), inoculum level (3, 5, or 7 log(10) CFU/ml), and medium (TSB or IF), with each combination tested in triplicate. For each replicate response of E. sakazakii, survival and death were scored with values of 0 and 1, respectively. Data were fitted to a logistic regression model in which the medium was treated as a dummy variable. The model predicted that the required pressure-holding times at 500 MPa for a 5-log reduction in IF with 90% achievement probability were 26.3 and 7.9 min at 25 and 40 degrees C, respectively. The probabilities of achieving 5-log reductions in TSB and IF by treatment with 400 MPa at 25 degrees C for 10 min were 92 and 3%, respectively. The model enabled the identification of a minimum processing condition for a required log reduction, regardless of the underlying inactivation kinetics pattern. Simultaneously, the probability of an inactivation effect under the predicted processing condition was also provided by taking into account the environmental factors mentioned above.