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Machine learning for total organic carbon analysis of environmental water samples using high-throughput colorimetric sensors

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Abstract

Due to the complexity of nonlinear reactions, the analysis of environmental samples often relies on expensive equipment as well as tedious and time-consuming experimental procedures. Currently, the efficient machine learning (ML) strategy based on big data offers some new insights for the analysis of complex components in the environmental field. In this study, ML was applied for the analysis of total organic carbon (TOC). We prepared a special colorimetric sensor (c-sensor) by inkjet printing. The sensor reacted with water samples in a high-throughput process, producing characteristic patterns to map TOC information in water samples. To quickly acquire TOC information on c-sensors, a ML model was proposed to describe the relationship between the c-sensor and TOC value. According to this study, the c-sensor and ML can be effectively applied to TOC information analysis of environmental water samples, which provides convenience for environmental research. It is foreseeable that ML has a broad prospect of application in environmental research.

Graphical abstract: Machine learning for total organic carbon analysis of environmental water samples using high-throughput colorimetric sensors

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Supplementary files

Article information


Submitted
11 Nov 2019
Accepted
11 Feb 2020
First published
11 Feb 2020

Analyst, 2020, Advance Article
Article type
Paper

Machine learning for total organic carbon analysis of environmental water samples using high-throughput colorimetric sensors

R. Luo, G. Ma, S. Bi, Q. Duan, J. Chen, Y. Feng, F. Liu and J. Lee, Analyst, 2020, Advance Article , DOI: 10.1039/C9AN02267H

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