Rapid Classification of Bauxite Ores from Different Mining Areas by BBO-XGBoost Combined with CC-LIBS
Yang Yanwei, Zhang Jiaxin, Zhang Lili · 2026
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Description
The authors present a fast way to tell which mining area a bauxite ore sample came from, using a laser technique plus machine learning. Traditional composition testing is slow and needs complicated sample preparation. Their method fires a laser inside a small cavity, which strengthens the light signal from the ore; a five-millimeter cavity gave the best boost for aluminum, iron, and titanium. Spectra from nine mining areas were reduced to their most informative features, and a boosted decision-tree classifier, tuned by Bayesian search, did the sorting.
The final system reached about ninety-eight and a half percent accuracy, with matching precision and recall. It beat both untuned versions and rival models. The authors propose the technique for rapid source identification of bauxite in mining, where knowing an ore's origin quickly can guide processing and quality control.
Catalog record
| CREATOR | Yang Yanwei, Zhang Jiaxin, Zhang Lili |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |