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Related Concept Videos

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview01:02

Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

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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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UV–Vis Spectrometers01:14

UV–Vis Spectrometers

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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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UV–Vis Spectrum01:30

UV–Vis Spectrum

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When light passes through a substance, a portion of the light is absorbed while the remaining light is reflected or transmitted. If the molecule absorbs light between the wavelengths of 180–400 nm range, the UV spectrum is obtained, and if it absorbs light in the 400–780 nm wavelength range, the visible spectrum is obtained.     
The UV–Vis spectrum of a molecule is the plot of its absorbance versus wavelength. The plot is drawn by taking molar...
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UV–Vis Spectroscopy: Beer–Lambert Law01:09

UV–Vis Spectroscopy: Beer–Lambert Law

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The Beer-Lambert law describes the relationship between absorbance and concentration, which combines the principles established by scientists Johann Heinrich Lambert and August Beer. Lambert's law states that when light passes through a medium, the loss in intensity is directly proportional to the original intensity and the path length of the light. Beer's law proposed that the transmittance of a solution remains constant if the product of concentration and path length is constant. The...
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UV–Vis Spectroscopy of Conjugated Systems01:32

UV–Vis Spectroscopy of Conjugated Systems

7.0K
Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
One of the factors influencing λmax is the extent...
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UV–Vis Spectroscopy: Woodward–Fieser Rules01:29

UV–Vis Spectroscopy: Woodward–Fieser Rules

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UV–Visible absorption spectra of conjugated dienes arise from the lowest energy π → π* transitions. The light-absorbing part of the molecule is called the chromophore, and the substituents directly attached to the chromophore are called auxochromes. A strong correlation exists between the absorption maxima, λmax, and the structure of a conjugated π system. The Woodward–Fieser rules predict the value of λmax for a given...
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Related Experiment Video

Updated: Jul 6, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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An improved prediction model for COD measurements using UV-Vis spectroscopy.

Li Guan1, Yijun Zhou2, Sen Yang1

  • 1Industrial Perception and Intelligent Manufacturing Equipment Engineering Research Center of Jiangsu Province, Nanjing Vocational University of Industry Technology Nanjing 210023 China li.guan.nangong@foxmail.com.

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|January 4, 2024
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Summary
This summary is machine-generated.

This study introduces an improved deep learning model for accurately measuring Chemical-Oxygen Demand (COD) in surface water. The new method enhances UV-Vis spectroscopy by reducing noise and improving generalization for reliable water quality monitoring.

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Organic contaminants remain a significant water pollution issue in China, necessitating accurate monitoring of Chemical-Oxygen Demand (COD).
  • UV-Vis spectroscopy offers a rapid, green detection method for online COD monitoring, but is susceptible to turbidity-induced spectral noise and poor model generalization.
  • Existing detection models struggle with interference from particulate matter in complex surface water samples, limiting accuracy.

Purpose of the Study:

  • To enhance the performance of traditional UV-Vis based COD detection models.
  • To address noise sensitivity and poor generalization issues in surface water spectral analysis.
  • To develop a robust and accurate COD prediction model using deep learning and advanced spectral preprocessing.

Main Methods:

  • Implemented an improved discrete wavelet transform-based noise filter to mitigate spectral noise.
  • Developed a novel deep learning network specifically designed to improve COD detection model generalization.
  • Collected and utilized a dataset of 2259 UV-Vis absorption spectra and corresponding COD values from water samples.
  • Integrated the noise reduction algorithm and the novel COD detection network into a complete prediction model pipeline.

Main Results:

  • The developed COD prediction model demonstrated significant improvements in noise tolerance.
  • The model achieved high accuracy in predicting Chemical-Oxygen Demand from UV-Vis spectra.
  • Experimental results validated the effectiveness of the combined deep learning and preprocessing approach.

Conclusions:

  • The proposed deep learning model, combined with wavelet-based noise filtering, offers a superior solution for online COD monitoring.
  • This approach effectively overcomes the limitations of traditional methods in handling complex surface water matrices.
  • The study provides a robust and accurate tool for assessing water quality and managing pollution.