• A MA-SSA-optimized XGBoost-MLP...

Treffer: A MA-SSA-optimized XGBoost-MLP framework using LIBS for rapid classification and quantitative analysis of heavy metals in traditional chinese medicines.

Title:
A MA-SSA-optimized XGBoost-MLP framework using LIBS for rapid classification and quantitative analysis of heavy metals in traditional chinese medicines.
Authors:
Yasen A; Xinjiang Key Laboratory for Luminescence Minerals and Optical Functional Materials, School of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi 830054, China., Zhou Y; Xinjiang Key Laboratory for Luminescence Minerals and Optical Functional Materials, School of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi 830054, China., Gao W; State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Nanjing University of Information Science & Technology, Nanjing 210044, China., Zhang Z; Xinjiang Key Laboratory for Luminescence Minerals and Optical Functional Materials, School of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi 830054, China., Tudi R; Xinjiang Key Laboratory for Luminescence Minerals and Optical Functional Materials, School of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi 830054, China., Xiang M; Xinjiang Key Laboratory for Luminescence Minerals and Optical Functional Materials, School of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi 830054, China. Electronic address: mei811014@126.com., Abulimiti B; Xinjiang Key Laboratory for Luminescence Minerals and Optical Functional Materials, School of Physics and Electronic Engineering, Xinjiang Normal University, Urumqi 830054, China; School of Chemistry and Chemical Engineering, Xinjiang Normal University, Urumqi 830054, China. Electronic address: maryam917@163.com.
Source:
Journal of pharmaceutical and biomedical analysis [J Pharm Biomed Anal] 2026 Mar 15; Vol. 270, pp. 117296. Date of Electronic Publication: 2025 Nov 29.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Science Country of Publication: England NLM ID: 8309336 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-264X (Electronic) Linking ISSN: 07317085 NLM ISO Abbreviation: J Pharm Biomed Anal Subsets: MEDLINE
Imprint Name(s):
Publication: <2006->: London : Elsevier Science
Original Publication: Oxford ; New York : Pergamon Press, c1983-
MeSH Terms:
Drugs, Chinese Herbal*/chemistry , Drugs, Chinese Herbal*/analysis , Metals, Heavy*/analysis , Metals, Heavy*/chemistry , Medicine, Chinese Traditional*/methods , Spectrum Analysis*/methods, Algorithms ; Machine Learning ; Lasers ; Boosting Machine Learning Algorithms
Contributed Indexing:
Keywords: LIBS; MA-SSA; Quantitative analysis; Traditional Chinese medicinal Material
Substance Nomenclature:
0 (Drugs, Chinese Herbal)
0 (Metals, Heavy)
Entry Date(s):
Date Created: 20251206 Date Completed: 20260111 Latest Revision: 20260111
Update Code:
20260130
DOI:
10.1016/j.jpba.2025.117296
PMID:
41351903
Database:
MEDLINE

Weitere Informationen

Laser-Induced Breakdown Spectroscopy (LIBS) holds significant value for rapid elemental detection; however, strong spectral interference, matrix effects, and high-dimensional data characteristics pose considerable challenges to accurate quantitative analysis. To enhance the performance of LIBS quantitative analysis, this study proposes a novel machine learning framework that integrates XGBoost and Multilayer Perceptron (MLP), optimized by a Multi-dimensional Adaptive Sparrow Search Algorithm (MA-SSA). The framework employs XGBoost for automated feature selection, eliminating redundant spectral variables while retaining critical information, and utilizes MA-SSA to optimize the hyperparameters of the MLP in regression tasks, significantly improving model stability and prediction accuracy. Experimental results demonstrate that the proposed method achieves 100 % accuracy in multi-class classification, outperforming traditional classifiers such as Random Forest, XGBoost, and standalone MLP. In terms of quantitative detection, the MA-SSA-optimized model achieves an RMSE of 4.43 µg/g, surpassing other hybrid optimization models including XGBoost-SSA-MLP (RMSE=4.62 µg/g), XGBoost-PSO-MLP (RMSE=5.225 µg/g), and XGBoost-GA-MLP (RMSE=5.584 µg/g). XGBoost-based feature selection effectively reduces spectral dimensionality while maintaining predictive performance. The proposed MA-SSA algorithm further enhances convergence efficiency and generalization capability. This study provides a robust, efficient, and scalable solution for LIBS analysis, with broad application potential in the field of real-time quantitative detection.
(Copyright © 2025 Elsevier B.V. All rights reserved.)

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.