Research and design of near infrared spectroscopy wastewater COD analysis system based on 1D-CNN

Yang Li; Ping Lin; Changning Hou

doi:10.1117/12.3025333

21 February 2024 Research and design of near infrared spectroscopy wastewater COD analysis system based on 1D-CNN

Yang Li, Ping Lin, Changning Hou

Proceedings Volume 13080, International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024); 1308003 (2024) https://doi.org/10.1117/12.3025333
Event: International Conference on Frontiers of Applied Optics and Computer Engineering, 2024, Kunming, China

Abstract

Chemical Oxygen Demand (COD) serves as a pivotal parameter for assessing water quality in wastewater. Traditional chemical detection methods are time-consuming and prone to secondary pollution of the environment. Near-infrared spectroscopy technology as an alternative to traditional chemical detection methods is a viable approach. However, the experimental data shows data overfitting and high interference within the sample dataset parameters，which has poor applicability in traditional quantitative and qualitative analysis methods. In this paper, a one-dimensional Convolutional Neural Network (1D-CNN) method is used to establish a relationship model between near-infrared features of water samples and the COD, which has the advantages of moderate depth, small parameter amount, and fast network training speed. It is extremely suitable for rapid detection of samples which can effectively solve the above problems. Experimental results show that the proposed model is better than random forest classification, SGD Classifier, and support vector Machine regression(SVM).

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yang Li, Ping Lin, and Changning Hou "Research and design of near infrared spectroscopy wastewater COD analysis system based on 1D-CNN", Proc. SPIE 13080, International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024), 1308003 (21 February 2024); https://doi.org/10.1117/12.3025333

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