ArticleScientific reports2021
Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology.iScience · 2026Review
- Fungal Growth Risk Prediction and Optimal Regulation Method for Food Storage Based on the Forward Reachable Set.Foods (Basel, Switzerland) · 2026Article
- Applying machine learning to classify table olives using bacterial metataxonomic data.NPJ science of food · 2025Article
- Thirty years of the Baranyi and Roberts model: Predictions, precautions, risk assessment, and beyond.Journal of biosciences · 2025Review
- Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces.Antibiotics (Basel, Switzerland) · 2024Review
- Impact of Biotic and Abiotic Factors onMicroorganisms · 2024Article
- The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products.Foods (Basel, Switzerland) · 2023Review
- Prediction ofLife (Basel, Switzerland) · 2023Article
- Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites.Computational and structural biotechnology journal · 2023Article
- Use of industry 4.0 technologies to reduce and valorize seafood waste and by-products: A narrative review on current knowledge.Current research in food science · 2023Review
- Article
- Viability ofCurrent research in food science · 2022Article
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Authors and funding
3 authors.
Funding
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Abstract
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.
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