Machine learning toward high-performance electrochemical sensors

Author(s)

Giordano, Gabriela F. ; Ferreira, Larissa F. ; Bezerra, italo R. S. ; Barbosa, Julia A. ; Costa, Juliana N. Y. ; Pimentel, Gabriel J. C. ; Lima, Renato S.

• Document type: Journal article
• Source: ANALYTICAL AND BIOANALYTICAL CHEMISTRY; v. 415, n. 18, p. 10-pg., 2023-01-13.

Abstract

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications. (AU)

• FAPESP's process: 20/09102-4 - Wearable capacitors in leaves and machine learning for real-time analysis of multiple plant physiological parameters
• Grantee: Júlia Adorno Barbosa
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• FAPESP's process: 18/24214-3 - Impedimetric sensor and machine learning for in-situ monitoring of nanoparticles
• Grantee: Larissa Fernanda Ferreira
• Support Opportunities: Scholarships in Brazil - Doctorate