Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database

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 di...

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Autores principales:	Satoko Hiura, Shige Koseki, Kento Koyama
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/951e9c94e01a47e497f7e7e4b438d616
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spelling	oai:doaj.org-article:951e9c94e01a47e497f7e7e4b438d6162021-12-02T15:53:01ZPrediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database10.1038/s41598-021-90164-z2045-2322https://doaj.org/article/951e9c94e01a47e497f7e7e4b438d6162021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90164-zhttps://doaj.org/toc/2045-2322Abstract 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.Satoko HiuraShige KosekiKento KoyamaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Satoko Hiura Shige Koseki Kento Koyama Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database
description	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.
format	article
author	Satoko Hiura Shige Koseki Kento Koyama
author_facet	Satoko Hiura Shige Koseki Kento Koyama
author_sort	Satoko Hiura
title	Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database
title_short	Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database
title_full	Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database
title_fullStr	Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database
title_full_unstemmed	Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database
title_sort	prediction of population behavior of listeria monocytogenes in food using machine learning and a microbial growth and survival database
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/951e9c94e01a47e497f7e7e4b438d616
work_keys_str_mv	AT satokohiura predictionofpopulationbehavioroflisteriamonocytogenesinfoodusingmachinelearningandamicrobialgrowthandsurvivaldatabase AT shigekoseki predictionofpopulationbehavioroflisteriamonocytogenesinfoodusingmachinelearningandamicrobialgrowthandsurvivaldatabase AT kentokoyama predictionofpopulationbehavioroflisteriamonocytogenesinfoodusingmachinelearningandamicrobialgrowthandsurvivaldatabase
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Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database

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