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[Artificial neural network applied for spectral overlap interference correction in ICP-AES].

Z Zhang1, S Liu, X Zeng

  • 1Department of Chemistry, Northeast Normal University, 130024 Changchun.

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|April 7, 2005
PubMed
Summary

A back-propagation artificial neural network (BP-ANN) effectively corrects spectral overlap in inductively coupled plasma atomic emission spectrometry (ICP-AES). Network performance is influenced by input data range, training sequence, and spectral noise levels.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Artificial Intelligence

Background:

  • Spectral overlap interference is a significant challenge in ICP-AES analysis.
  • Accurate quantification requires effective methods to resolve overlapping spectral lines.

Purpose of the Study:

  • To apply a back-propagation artificial neural network (BP-ANN) for correcting spectral overlap interference in ICP-AES.
  • To investigate the impact of network parameters and spectral noise on BP-ANN performance.

Main Methods:

  • Simulated spectral data for Cerium (Ce) and Praseodymium (Pr) at 413.380nm were used.
  • A BP-ANN model was trained and evaluated with varying input value ranges and training sequences.
  • The influence of noise in simulated mixture spectra was assessed.

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Main Results:

  • The BP-ANN demonstrated capability in correcting spectral overlap interference.
  • Network convergence speed was reduced by noise in simulated spectra.
  • Spectral noise significantly impacted the accuracy of network predictions.

Conclusions:

  • BP-ANN is a viable tool for mitigating spectral overlap in ICP-AES.
  • Optimizing network parameters and managing spectral noise are crucial for reliable results.