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

Author(s): Run Luo ¹ ^, ² ^, ³ ^, ⁴ ^, ⁵ , Guodong Ma ¹ ^, ² ^, ³ ^, ⁴ ^, ⁵ , Sifan Bi ¹ ^, ² ^, ³ ^, ⁴ ^, ⁵ , Qiannan Duan ⁶ ^, ⁷ ^, ⁸ ^, ⁹ ^, ¹⁰ , Jiayuan Chen ¹ ^, ² ^, ³ ^, ⁴ ^, ⁵ , Yunjin Feng ¹ ^, ² ^, ³ ^, ⁴ ^, ⁵ , Fenli Liu ¹ ^, ² ^, ³ ^, ⁴ ^, ⁵ , Jianchao Lee ¹ ^, ² ^, ³ ^, ⁴ ^, ⁵

Publication date (Electronic): 2020

Journal: The Analyst

Publisher: Royal Society of Chemistry (RSC)

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Abstract

Process of total organic carbon (TOC) prediction using colorimetric sensors and machine learning (ML).

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.