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Classi ﬁ cation of minerals and the assessment of lithium and beryllium content in granitoid rocks by laser-induced breakdown spectroscopy

This study demonstrates that LIBS mapping and spatially resolved local analysis is an e ﬃ cient and practical approach for the classi ﬁ cation of mineral grains (quartz, feldspar, biotite, amphibole) and for prospecting of technologically relevant, low- Z elements ( e.g. Be and Li) in granitoid rock samples. We tested three statistical approaches (classi ﬁ cation tree (CT) based on indicator elements, linear discriminant analysis (LDA) and random forest (RF)) for the classi ﬁ cation of the mineral grains and found that each of the three methods provides fairly similar, very good classi ﬁ cation accuracies. RF and LDA provided better than 92% accuracy for all minerals, whereas CT showed a somewhat poorer (around 80%) accuracy for quartz in particular. Our results also demonstrate that using multiple analytical locations within each grain and resting the classi ﬁ cation on the majority vote of these individual analysis gives more reliable discrimination (grain-based accuracy is better than location-based accuracy). We also demonstrated that LIBS elemental mapping can provide valuable information about the distribution of chemical elements among the minerals, especially if it is combined with matrix-matched calibration of emission intensity data. We illustrated this by the successful assessment of ng to m g amounts of Be and Li in the studied mineral grains. Our results suggest that mining for Be and Li in granitoid rocks should be aiming for biotite and amphibole grains.