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Related Experiment Video

Updated: Mar 21, 2026

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water

Published on: June 9, 2014

Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict

Daniel Lowell Weller^1,2,3, Tanzy M T Love¹, Martin Wiedmann³

¹Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, United States.

Frontiers in Environmental Science

|March 20, 2026

Summary

Predictive models can assess Listeria contamination in agricultural water, outperforming E. coli testing. Machine learning, particularly random forests with SMOTE resampling and spatial features, offers a promising alternative for food safety monitoring.

Keywords:

Listeria Listeria (L.) monocytogenes SMOTE (synthetic minority over-sampling technique)agricultural water class imbalance food safety machine learning predictive modeling

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Area of Science:

Food Safety
Microbiology
Machine Learning

Background:

Current E. coli testing for agricultural water safety has limitations.
Predictive models show potential but require validation for specific pathogens and imbalanced data.

Purpose of the Study:

To evaluate predictive models for Listeria contamination in agricultural water.
To assess the impact of resampling techniques on model performance.

Main Methods:

Developed and compared machine learning models (random forest, regression) using microbial, physicochemical, spatial, and weather features.
Employed resampling methods (none, oversampling, SMOTE) to handle imbalanced datasets.
Validated models against E. coli baseline models.

Main Results:

Machine learning models, especially random forests, outperformed E. coli baseline models.
SMOTE resampling yielded more accurate predictions than oversampling.
Spatial and physicochemical features were key predictors for nonpathogenic Listeria and L. monocytogenes, respectively.

Conclusions:

Predictive models offer a viable alternative to E. coli testing for agricultural water safety.
Random forest learners with SMOTE resampling and relevant features can effectively predict Listeria contamination.
Findings support integrating predictive models into on-farm food safety management programs.