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This research applies a Multi-Adapter PPO framework enhanced by cross-attention mechanisms to optimize wavelength selection for Laser-induced breakdown spectroscopy (LIBS). By treating feature extraction as a reinforcement learning task, the method significantly outperforms traditional optimization algorithms like Particle Swarm Optimization in balancing prediction accuracy with computational efficiency. The work represents a tangible advancement in applying machine learning techniques to solve high-dimensional spectral data challenges within scientific instrumentation.
Multi-Adapter PPO: A Cross-Attention Enhanced Wavelength Selection Framework for LIBS Quantitative Analysis
arXiv:2606.17476v1 Announce Type: new Abstract: Laser-induced breakdown spectroscopy (LIBS) quantitative analysis faces critical challenges in wavelength selection due to high-dimensional spectral data and the fundamental trade-off between prediction accuracy and feature efficiency. This paper presents a novel Multi-Adapter PPO framework that transforms wavelength selection into a reinforcement learning problem, leveraging cross-attention mechanisms and multiple specialized adapters to capture complex spectral relationships. Our approach outperforms traditional Particle Swarm Optimization (PSO) by an average of 28.4\% in comprehensive score and 45.2\% in prediction accuracy across steel and coal datasets. The proposed method demonstrates superior performance in balancing prediction accuracy with feature efficiency, achieving state-of-the-art results in LIBS quantitative analysis while maintaining interpretability and computational efficiency. We released our code and dataset here: https://github.com/Hflying/MAPPO
Abstract:Laser-induced breakdown spectroscopy (LIBS) quantitative analysis faces critical challenges in wavelength selection due to high-dimensional spectral data and the fundamental trade-off between prediction accuracy and feature efficiency. This paper presents a novel Multi-Adapter PPO framework that transforms wavelength selection into a reinforcement learning problem, leveraging cross-attention mechanisms and multiple specialized adapters to capture complex spectral relationships. Our approach outperforms traditional Particle Swarm Optimization (PSO) by an average of 28.4\% in comprehensive score and 45.2\% in prediction accuracy across steel and coal datasets. The proposed method demonstrates superior performance in balancing prediction accuracy with feature efficiency, achieving state-of-the-art results in LIBS quantitative analysis while maintaining interpretability and computational efficiency. We released our code and dataset here: this https URL
Machine Learning (cs.LG)
arXiv:2606.17476 [cs.LG]
(or arXiv:2606.17476v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.17476 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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