Data mining for predicting bacterial population behavior
Using accumulated data
Experimental data collected sporadically on many types of foods and bacteria are currently not well utilized. However, the modern computational techniques,enable to make use of accumulated data. In our laboratory, we used the increase and decrease of bacteria over the past 40 years for predicting bacteiral population behavior. We have found that information on various foods can also be combined by machine learning. Why don't you make use of the experimental data that are lying dormant in research institutes for the development of the field of food microbiology?
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The purpose of this study is to predict bacteria population behavior in more than 1000 different environments using machine learning. This is done by using multidimensional information and data visualization. The relationship between multidimensional information and bacteria growth and death behavior can be predicted by Machine Learning.
The researchers will use machine learning to predict Listeria growth and death behavior based on multidimensional information, such as temperature, pH, water activity, time, initial bacteria count, food group, food name.
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References
Hiura, S., Koseki, S., Koyama, K. Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database, 2021, Scientific Reports, 11, 1, 10613. https://doi.org/10.1038/s41598-021-90164-z Koyama, K, Kubo, K., Hiura, S., Koseki, S. Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning. 2022 Journal of Microbiological Methods, January 192 ,106366 https://doi.org/10.1016/j.mimet.2021.106366