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Machine learning based on holographic scattering spectrum for mixed pollutants analysis.

Qiannan Duan1, Zhaoyi Xu2, Shourong Zheng2

  • 1Department of Environment Science, Shaanxi Normal University, Xi'an, 710062, China; State Key Laboratory of Pollution Control and Resource Reuse, Jiangsu Key Laboratory of Vehicle Emissions Control, School of the Environment, Nanjing University, Nanjing, 210023, China.

Analytica Chimica Acta
|January 1, 2021
PubMed
Summary

This study introduces a new method for detecting complex pollutants using holographic spectrum and machine learning (ML). A trained convolutional neural network (CNN) model can quantitatively analyze mixed pollutants, simplifying chemical analysis.

Keywords:
High-throughputHolographic scattering spectrumMachine learningMixed pollutants

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Data Science

Background:

  • Traditional methods for analyzing complex pollutants are often expensive and time-consuming.
  • Machine learning (ML) shows great potential for advancing scientific analysis, but requires sufficient data.
  • Developing efficient data strategies is crucial for ML applications in scientific testing.

Purpose of the Study:

  • To present a novel strategy for the rapid detection of mixed pollutants.
  • To demonstrate the synergistic application of holographic spectrum and convolutional neural networks (CNNs).
  • To overcome data challenges in ML-driven pollutant analysis.

Main Methods:

  • Utilizing holographic spectrum technology to capture detailed spectral information.
  • Developing and training a convolutional neural network (CNN) model.
  • Applying the CNN to extract spectral features for quantitative analysis of mixed pollutants.

Main Results:

  • The trained CNN model successfully performed quantitative analysis of mixed pollutants.
  • The strategy effectively extracted relevant spectral information for analysis.
  • Demonstrated the feasibility of using spectral data with CNNs for pollutant detection.

Conclusions:

  • The proposed strategy offers a rapid and efficient approach for detecting complex pollutants.
  • Holographic spectrum combined with CNNs provides a valuable tool for analyzing complex chemical systems.
  • This ML-driven method has the potential to significantly advance environmental monitoring and chemical analysis.