AI Framework Boosts Wavelength Selection for LIBS Quantitative Analysis.

Laser-induced breakdown spectroscopy (LIBS) is a technique used for quantitative analysis, but it faces significant hurdles in selecting optimal wavelengths from high-dimensional spectral data. This selection process involves a fundamental trade-off between achieving high prediction accuracy and maintaining feature efficiency. To address these challenges, researchers have developed a new framework called Multi-Adapter PPO. This approach redefines wavelength selection as a reinforcement learning problem. It incorporates cross-attention mechanisms and multiple specialized adapters, allowing it to effectively capture complex relationships within the spectral data. Experimental results on steel and coal datasets demonstrate that Multi-Adapter PPO substantially outperforms traditional methods like Particle Swarm Optimization (PSO). It achieves an average improvement of 28.4% in comprehensive score and 45.2% in prediction accuracy, while also balancing accuracy with computational efficiency and interpretability. The code and dataset have been made publicly available.