Prediction of the Antibacterial Effect of Ozone Against Listeria Isolated from Chicken Meat Using a Machine Learning Approach
DOI:
https://doi.org/10.24925/turjaf.v13i2.446-452.7321Keywords:
Listeria, Ozone, Antimicrobial Effect, XGBoost, Machine LearningAbstract
In this study, an XGBoost-based prediction model with 99.99% accuracy was developed to predict the antibacterial effects of ozone gas on Listeria spp. isolated from poultry plants and chicken meat. Prior to the machine learning process, various pre-processing procedures were performed on 75 pieces of data obtained from experimental data and 70% of the data were randomly allocated as training set and 30% as test set. In this study, five different machine learning algorithms were tested with default settings and the performance of the models were compared. According to the R² score, the XGBoost algorithm was found to be the most successful model. Hyper-parameter optimization was performed to improve the accuracy performance of the XGBoost model. As a result of the study, it was observed that the antibacterial effect on Listeria spp. increased with the increase in the duration of ozone gas application, especially at the end of 20 minutes, Listeria ivanovii, Listeria monocytogenes and Listeria innocua species were completely inhibited. In conclusion, it was determined that the antibacterial effect of ozone on Listeria spp. may vary from species to species and ozone application has potential as an effective antibacterial method in food safety practices. The research findings demonstrate the industrial applicability of predictive models in the field of food safety.
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