Machine learning for total organic carbon analysis of environmental water samples using high-throughput colorimetric sensors
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) based on big data offers some new study thoughts for complex components analysis in environmental area. In this study, ML 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 put forward in regards of the relations between c-sensors and TOC values. 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.