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The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
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Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
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Related Experiment Video

Updated: May 23, 2025

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Rapid COD Sensing in Complex Surface Water Using Physicochemical-Informed Spectral Transformer with UV-Vis-SWNIR

Jiacheng Liu1,2,3, Xiao Liu1, Xueji Wang1

  • 1Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.

Environmental Science & Technology
|March 7, 2025
PubMed
Summary

A new deep learning model, the physicochemical-informed spectral Transformer (PIST), accurately measures chemical oxygen demand (COD) in complex water bodies using UV-vis-SWNIR spectroscopy. This advancement improves water quality sensing and generalizability in environmental monitoring.

Keywords:
UV−vis-SWNIR spectroscopychemical oxygen demand (COD)chemometricsdeep learningphysicochemical-informed learningtransformerwater quality sensing

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Data Science

Background:

  • Accurate water quality monitoring is crucial for sustainable resource management.
  • Measuring chemical oxygen demand (COD) is vital for assessing water health.
  • Existing spectroscopic methods face challenges in complex water environments due to limited spectral understanding and generalizability.

Purpose of the Study:

  • To introduce a novel deep learning model for enhanced water quality sensing.
  • To improve the accuracy and generalizability of chemical oxygen demand (COD) measurements in diverse water bodies.
  • To integrate physicochemical knowledge into spectral analysis for domain adaptation.

Main Methods:

  • Development of the physicochemical-informed spectral Transformer (PIST) model.
  • Combination of PIST with ultraviolet-visible-shortwave-near-infrared (UV-vis-SWNIR) spectroscopy.
  • Validation using extensive surface water spectral data from the Yangtze River and Poyang Lake.

Main Results:

  • PIST achieved a high coefficient of determination (R^2) of 0.9008 for COD sensing.
  • Significant reduction in root mean squared error (RMSE) by 45.20% and 29.38% compared to SVR and CNN models.
  • Demonstrated notable accuracy and generalizability in complex water environments.

Conclusions:

  • The PIST model represents a significant advancement in water quality sensing using spectroscopy and deep learning.
  • The integration of physicochemical information enhances spectral encoding and domain adaptation.
  • PIST offers a robust and accurate solution for rapid, large-scale water quality monitoring.