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In Analytica chimica acta

Determination of complex pollutants often involves many high-cost and laborious operations. Today's pop machine-learning (ML) technology has exhibited their amazing successes in image recognition, drug designing, disease detection, natural language understanding, etc. ML-driven samples testing will inevitably promote the development of related subjects and fields, but the biggest challenge ahead for this process is how to provide some intelligible and sufficient data for various algorithms. In this work, we present a full strategy for rapid detecting mixed pollutants through the synergistic application of holographic spectrum and convolutional neural network (CNN). The results have shown that a well-trained CNN model could realize quantitative analysis of the mixed pollutants by extracting spectral information of matters, suggesting the strategy's value in facilitating the study of complex chemical systems.

Duan Qiannan, Xu Zhaoyi, Zheng Shourong, Chen Jiayuan, Feng Yunjin, Run Luo, Lee Jianchao


High-throughput, Holographic scattering spectrum, Machine learning, Mixed pollutants