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ORIGINAL RESEARCH article
Front. Chem.
Sec. Analytical Chemistry
Volume 12 - 2024 |
doi: 10.3389/fchem.2024.1409527
Application of neural network adaptive filter method to simultaneous detection of polymetallic ions based on ultraviolet-visible spectroscopy
Provisionally accepted- Shaoyang University, Tangdukou, China
A novel neural network adaptive filter algorithm is proposed to address the challenge of weak spectral signals and low accuracy in micro-spectrometer detection. This algorithm bases on error backpropagation (BP) and least mean square (LMS), introduces an innovative BP neural network model incorporating instantaneous error function and error factor to optimize the learning process. It establishes a network relationship through the input signal, output signal, error and step factor of the adaptive filter, and defines a training optimization learning method for this relationship. To validate the effectiveness of the algorithm, experiments were conducted on simulated noisy signals and actual spectral signals. Results show that the algorithm effectively denoises signals, reduces noise interference, and enhances signal quality, the SNR of the proposed algorithm is 3-4 dB higher than that of the traditional algorithm. The experimental spectral results showed that the proposed neural network adaptive filter algorithm combined with partial least squares regression is suitable for simultaneous detection of copper and cobalt based on ultraviolet-visible spectroscopy, and has broad application prospects.
Keywords: neural network adaptive filter, Signal processing, Noise Reduction, quantitative analysis, Ultraviolet-visible spectroscopy
Received: 02 Apr 2024; Accepted: 27 Aug 2024.
Copyright: © 2024 Wu and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Fengbo Zhou, Shaoyang University, Tangdukou, China
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