ElectroML: an open-source web platform for machine learning-based analyte concentration prediction in electrochemical sensing
Year: 2026
Type: software-paper
Abstract: ElectroML is an open-source web platform for machine-learning-based analyte concentration prediction from electrochemical sensing data. The platform extracts key features from cyclic voltammetry (CV) and differential pulse voltammetry (DPV) signals and offers preprocessing tools such as outlier detection, baseline correction, and signal filtering. The platform integrates multiple machine learning (ML) algorithms with automated hyperparameter optimization and supports Leave-One-Out (LOO) and K-Fold cross-validation strategies. ElectroML enables flexible concentration prediction with bootstrap-based confidence intervals and offers customizable, publication-ready visualizations with adjustable typography, color schemes, and vector export options. Its web-based interface simplifies access to advanced electrochemical analysis and supports integration with laboratory information systems. In addition to training models on existing datasets, ElectroML can rapidly predict analyte concentrations from new sensor measurements, making it a valuable tool for environmental monitoring, medical diagnostics, and industrial quality control.
Source: SoftwareX
Institutions: Istanbul University-Cerrahpaşa, Istanbul University
Language: English
Subfield: Electrochemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W7153271747