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 degrees 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

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°C), inoculum level (3, 5, or 7 log10 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°C, respectively. The probabilities of achieving 5-log reductions in TSB and IF by treatment with 400 MPa at 25°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

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℃, 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

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 10(5) and the growth kinetic data of Listeria monocytogenes with an initial cell count of 10(4). 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 10(1), 10(2), and 10(3) and growth with average of initial cell numbers of 10(-1), 10(0), and 10(1). The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 10(1) 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

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

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

DataSheet2_Data mining for prediction and interpretation of bacterial population behavior in food.csv

Although bacterial population behavior has been investigated in a variety of foods in the past 40 years, it is difficult to obtain desired information from the mere juxtaposition of experimental data. We predicted the changes in the number of bacteria and visualize the effects of pH, aw, and temperature using a data mining approach. Population growth and inactivation data on eight pathogenic and food spoilage bacteria under 5,025 environmental conditions were obtained from the ComBase database (www.combase.cc), including 15 food categories, and temperatures ranging from 0°C to 25°C. The eXtreme gradient boosting tree was used to predict population behavior. The root mean square error of the observed and predicted values was 1.23 log CFU/g. The data mining model extracted the growth inhibition for the investigated bacteria against aw, temperature, and pH using the SHapley Additive eXplanations value. A data mining approach provides information concerning bacterial population behavior and how food ecosystems affect bacterial growth and inactivation.</p