acePredicting antifouling paint particle contamination based on 16S rRNA gene sequencing data using random forest-based machine learning
Antifouling paints often contain biocides designed to inhibit biological growth, and antifouling paint particles (APPs) have been previously shown to affect microbial communities in sediment. Given that typical methods for monitoring for APP presence can be specialized and challenging, alternative methods using simple, standardized, and universal approaches, such as 16S rRNA amplicon sequencing, would be highly valuable. This study uses a field-based mesocosm approach to train a random forest-based (supervised)…
A random forest model trained on 16S rRNA microbial community data from a field mesocosm predicted antifouling paint particle presence in sediment, with perfect detection of presence in test set and correct classification of all uncontaminated and 3 of 5 contaminated real-world Baltic Sea sites.
The same presence-prediction model failed to identify 2 of 5 APP-contaminated field sites and could not predict particle concentration with sufficient accuracy.
Model could not predict APP concentration accurately and missed 2 of 5 contaminated field sites, indicating presence-only utility and incomplete generalization across geography and season.
Evidence
- Peer-reviewedMicrobiology Spectrum2026-08-03
How should this claim be treated?
Truvace Impact Record TRV-2026-0643, v1: “Predicting antifouling paint particle contamination based on 16S rRNA gene sequencing data using random forest-based machine learning.” Truvace, 2026-08-04. /record/TRV-2026-0643 (accessed at citation time). sha256 55e504090979e740…
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