Rapid Classification of Bauxite Ores from Different Mining Areas by BBO-XGBoost Combined with CC-LIBS.

Focusing on the problems of complicated sample processing and long detection time of traditional bauxite ore composition testing in mining areas, this paper proposes a rapid bauxite ore classification method combining cavity-confined laser-induced breakdown spectroscopy (CC-LIBS) with Boruta feature selection and Bayesian optimization eXtreme Gradient Boosting (BBO-XGBoost), which can quickly and accurately classify bauxite ore samples from 9 different mining areas. First, the effects of different cavity configurations on the enhancement factor, signal-to-noise ratio (SNR), were investigated. When the cavity diameter is 5 mm, the enhancement factors for Al, Fe, and Ti reach 2.8, 3.1, and 2.6, respectively. Next, 900 × 12,248-dimensional spectral data under the D = 5 mm cavity configuration were preprocessed using the Boruta feature selection algorithm. Finally, the XGBoost classifier is optimized using Bayesian optimization to achieve accurate classification of bauxite. Compared with unoptimized approaches and other models, the classification method combining CC-LIBS and BBO-XGBoost achieves the best performance. The accuracy is 0.9852, the precision is 0.9861, the recall is 0.9852, and the F1-score is 0.9856. The proposed technique, combining CC-LIBS with BBO-XGBoost, not only plays a pivotal role in bauxite ore research but also provides a significant technical reference for the rapid identification of bauxite ore sources.