Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/56716
Title: Machine learning models for prediction of Escherichia coli O157:H7 growth in raw ground beef at different storage temperatures
Authors: Al, S.
Uysal, Ciloglu, F.
Akcay, A.
Koluman, A.
Keywords: Escherichia coli O157:H7
Ground beef
Machine learning methods
Microbial growth prediction
Beef
Digital storage
Escherichia coli
Food safety
Forestry
Machine learning
Mean square error
Multiple linear regression
Neural networks
Risk assessment
E.coli O157:H7
Escherichia coli O157:H7
Ground beef
Growth performance
Machine learning methods
Machine learning models
Mean squared error
Microbial growth
Microbial growth prediction
Shiga toxin-producing Escherichia coli
Forecasting
article
artificial neural network
beef
controlled study
Escherichia coli O157
Escherichia coli O157:H7
food chain
food safety
growth
inoculation
machine learning
microbial growth
multiple linear regression analysis
nonhuman
prediction
prevention
random forest
Shiga toxin producing Escherichia coli
storage temperature
support vector machine
temperature
Publisher: Elsevier Ltd
Abstract: Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain. The growth performance of E. coli O157:H7 at different temperatures in raw ground beef spiked with cocktail inoculum was investigated using machine learning (ML) models to address this problem. After spiking, ground beef samples were stored at 4, 10, 20, 30 and 37 °C. Repeated E. coli O157 enumeration was performed at 0–96 h with 21 times repeated counting. The obtained microbiological data were evaluated with ML methods (Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR)) and statistically compared for valid prediction. The coefficient of determination (R2) and mean squared error (MSE) are two essential criteria used to evaluate the model performance regarding the comparison between the observed value and the prediction made by the model. RF model showed superior performance with 0.98 R2 and 0.08 MSE values for predicting the growth performance of E. coli O157 at different temperatures. MLR model predictions were obtained further from the observed values with 0.66 R2 and 2.7 MSE values. Our results indicate that ML methods can predict of E. coli O157:H7 growth in ground beef at different temperatures to strengthen food safety professionals and legal authorities to assess contamination risks and determine legal limits and criteria proactively. © 2023
URI: https://doi.org/10.1016/j.meatsci.2023.109421
https://hdl.handle.net/11499/56716
ISSN: 0309-1740
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Teknoloji Fakültesi Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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