ArticleACS omega2026
Rapid Classification of Bauxite Ores from Different Mining Areas by BBO-XGBoost Combined with CC-LIBS.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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