Machine learning toward high-performance electrochemical sensors

Autor(es)

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

• Tipo de documento: Artigo Científico
• Fonte: ANALYTICAL AND BIOANALYTICAL CHEMISTRY; v. 415, n. 18, p. 10-pg., 2023-01-13.

Resumo

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)

• Processo FAPESP: 20/09102-4 - Capacitores vestíveis em folhas e machine learning para análise em tempo real de múltiplos parâmetros fisiológicos de plantas
• Beneficiário: Júlia Adorno Barbosa
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 18/24214-3 - Sensor impedimétrico e machine learning para o monitoramento in-situ de nanopartículas
• Beneficiário: Larissa Fernanda Ferreira
• Modalidade de apoio: Bolsas no Brasil - Doutorado