University of Rochester Researchers Focus on Listeria (Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water)

Insurance Daily News

2021 JUL 08 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on listeria. According to news reporting originating from Rochester, New York, by NewsRx correspondents, research stated, “Recent studies have shown that predictive models can supplement or provide alternatives to E. coli-testing for assessing the potential presence of food safety hazards in water used for produce production. However, these studies used balanced training data and focused on enteric pathogens.”

The news journalists obtained a quote from the research from University of Rochester: “As such, research is needed to determine 1) if predictive models can be used to assess Listeria contamination of agricultural water, and 2) how resampling (to deal with imbalanced data) affects performance of these models. To address these knowledge gaps, this study developed models that predict nonpathogenic Listeria spp. (excluding L. monocytogenes) and L. monocytogenes presence in agricultural water using various combinations of learner (e.g., random forest, regression), feature type, and resampling method (none, oversampling, SMOTE). Four feature types were used in model training: microbial, physicochemical, spatial, and weather. ‘Full models’ were trained using all four feature types, while ‘nested models’ used between one and three types. In total, 45 full (15 learners*3 resampling approaches) and 108 nested (5 learners*9 feature sets*3 resampling approaches) models were trained per outcome. Model performance was compared against baseline models where E. coli concentration was the sole predictor. Overall, the machine learning models outperformed the baseline E. coli models, with random forests outperforming models built using other learners (e.g., rule-based learners). Resampling produced more accurate models than not resampling, with SMOTE models outperforming, on average, oversampling models. Regardless of resampling method, spatial and physicochemical water quality features drove accurate predictions for the nonpathogenic Listeria spp. and L. monocytogenes models, respectively. Overall, these findings 1) illustrate the need for alternatives to existing E. coli-based monitoring programs for assessing agricultural water for the presence of potential food safety hazards, and 2) suggest that predictive models may be one such alternative.”

According to the news reporters, the research concluded: “Moreover, these findings provide a conceptual framework for how such models can be developed in the future with the ultimate aim of developing models that can be integrated into on-farm risk management programs. For example, future studies should consider using random forest learners, SMOTE resampling, and spatial features to develop models to predict the presence of foodborne pathogens, such as L. monocytogenes, in agricultural water when the training data is imbalanced.”

For more information on this research see: Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water. Frontiers in Environmental Science, 2021,9. (Frontiers in Environmental Science - http://www.frontiersin.org/environmental_science). The publisher for Frontiers in Environmental Science is Frontiers Media S.A.

A free version of this journal article is available at https://doi.org/10.3389/fenvs.2021.701288.

Our news editors report that additional information may be obtained by contacting Daniel Lowell Weller, Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, United States. Additional authors for this research include Tanzy M. T. Love, Martin Wiedmann.