Machine Learning and Corona Effect Coupling, a New Tool for Water Quality Control: Highlighting the Impact of Microwaves on Water Samples – Summary
Faniry Raveloson1, Mickaël Gresse1, Laurent Vandanjon2*, Hugues de la Bardonnie1
1LIMEC Laboratory, 24 rue du Général Ferrié, 31500 Toulouse, France
2Laboratory of Marine Biotechnology and Chemistry (LBCM), University of South Brittany (UBS), EMR CNRS 6076, IUEM, Campus Tohannic, 56000 Vannes, France
*Corresponding author: laurent.vandanjon@univ-ubs.fr
Keywords: corona effect, boosting, bagging, weak learner, strong learner, principal component analysis
Submitted: August 9, 2025
Revised: May 4, 2026
Accepted: May 14, 2026
Published: September 24, 2026
Abstract
L’Imagerie Macroscopique à Effet Couronne (LIMEC) was used to investigate whether microwave exposure produces detectable differences in water samples from the same commercial source. Corona-discharge images of untreated and microwave-treated bottled waters (800 W, 30 s) were acquired under controlled conditions, and 102 image-derived descriptors were extracted. Feature selection combined ANOVA, LASSO regularization, correlation analysis, and Random Forest importance ranking, while classification was performed using XGBoost.
The model achieved accuracies of 74% and 78% with ROC–AUC values of 0.85 and 0.86 for the Montcalm and Mont Roucous datasets, respectively. When both water sources were combined, classification accuracy reached 80%. The results demonstrate reproducible discrimination between untreated and microwave-treated samples based on their macroscopic corona-discharge patterns. These findings indicate that LIMEC coupled with machine learning is sensitive to subtle differences associated with distinct treatment histories, without implying permanent molecular-scale modifications of water structure.