• A MA-SSA-optimized XGBoost-MLP...

*Result*: 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

*Further Information*

*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.*