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Differentiation of NaCl, NaOH, and β-Phenylethylamine Using Ultraviolet Spectroscopy and Improved Adaptive Artificial
Angxin Tong1,2,3, Xiaojun Tang2, Haibin Liu1
1School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China.
ACS Omega
|April 10, 2023
Summary
An improved artificial bee colony algorithm enhances back-propagation artificial neural network classification for NaCl, NaOH, and β-phenylethylamine detection using UV spectroscopy. This IAABC-BP-ANN method offers superior accuracy and avoids common algorithm defects.
Area of Science:
- Analytical Chemistry
- Computational Chemistry
- Spectroscopy
Background:
- Artificial neural networks (ANNs) are powerful tools for classification but can suffer from local optima.
- The artificial bee colony (ABC) algorithm is prone to prematurity and local optimization issues.
- Accurate classification of chemical compounds like NaCl, NaOH, and β-phenylethylamine (PEA) is crucial in various analytical applications.
Purpose of the Study:
- To enhance the classification performance of the back-propagation artificial neural network (BP-ANN) algorithm.
- To address the limitations of the artificial bee colony (ABC) algorithm, specifically prematurity and local optimization.
- To develop and validate a novel IAABC-BP-ANN algorithm for identifying NaCl, NaOH, PEA, and their mixtures using UV spectroscopy.
Main Methods:
- A combined improved adaptive artificial bee colony (IAABC) algorithm and BP-ANN algorithm was developed.
- The IAABC algorithm was enhanced with adaptive local search and mutation factors to improve global optimization and avoid prematurity.
- Principal component score vectors from UV spectra were used as input for the BP-ANN, with IAABC optimizing weights and thresholds.
Main Results:
- The IAABC-BP-ANN algorithm demonstrated superior performance compared to discriminant analysis, SVM variants, BP-ANN, and the basic ABC-BP-ANN.
- Evaluation metrics including accuracy, recall, precision, and F-score confirmed the enhanced classification capabilities.
- Regression parameters for mixtures were accurately obtained using the IAABC-BP-ANN model.
Conclusions:
- The IAABC-BP-ANN algorithm effectively overcomes the limitations of traditional ABC and BP-ANN methods.
- This IAABC-BP-ANN approach combined with UV spectroscopy presents a promising tool for the identification and detection of NaCl, NaOH, PEA, and their mixtures.
- The study highlights the potential of hybrid intelligent algorithms in complex chemical analysis.
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