Analysis on influencing factors of detecting chemical oxygen demand in water by ultraviolet absorption spectroscopy

Zhou, Kunpeng; Liu, Zhiyang; Cong, Menglong; Man, Shanxin

doi:10.1007/s11801-022-2093-7

Optoelectron. Lett.

2022

DOI: 10.1007/s11801-022-2093-7

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Analysis on influencing factors of detecting chemical oxygen demand in water by ultraviolet absorption spectroscopy

Kunpeng Zhou

Zhiyang Liu

Menglong Cong

et al.

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2024

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Optimizing Chemical Oxygen Demand Prediction in Spectroscopy Through Hybrid Feature Engineering and Regression-Based Similarity Analysis

Jia,

Zhou,

et al. 2024

Water

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This paper presents a hybrid feature extraction and regression-based machine learning approach for predicting COD concentrations in water samples using spectral data. The method integrates SK-Best and FA to tackle high dimensionality and information redundancy in small datasets. SK-Best identifies key absorbance features, enhancing predictive reliability, while FA reduces dimensionality and extracts valuable information for similarity prediction. The combination of SK-Best, FA, and Linear Regression achieves strong prediction performance (R2 ~ 0.87, MAE = 0.23), demonstrating interpretability, flexibility, and robustness in small datasets. This approach offers a promising solution for real-time water quality monitoring and will be further optimized for broader applications.

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Optimizing Chemical Oxygen Demand Prediction in Spectroscopy Through Hybrid Feature Engineering and Regression-Based Similarity Analysis

Jia,

Zhou,

et al. 2024

Water

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show abstract

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Analysis on influencing factors of detecting chemical oxygen demand in water by ultraviolet absorption spectroscopy

Cited by 1 publication

References 6 publications

Optimizing Chemical Oxygen Demand Prediction in Spectroscopy Through Hybrid Feature Engineering and Regression-Based Similarity Analysis

Optimizing Chemical Oxygen Demand Prediction in Spectroscopy Through Hybrid Feature Engineering and Regression-Based Similarity Analysis

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