The direct identification of mineral species in raw rocks was performed using laser induced breakdown spectroscopy (LIBS). A total of 162 sulfide rocks with mineralogical relevance in the copper industry were analyzed. These contained bornite (Cu₅FeS₄), chalcocite (Cu₂S), chalcopyrite (CuFeS₂), covellite (CuS), enargite (Cu₃AsS₄), molybdenite (MoS₂), and pyrite (FeS₂). The samples were collected from different mining locations to account for sample variability. Unsupervised multivariate methods like principal component analysis (PCA) and dendrogram analysis were explored, while supervised pattern recognition techniques, such as soft independent modelling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), K-nearest neighbor (KNN), decision tree analysis and artificial neural networks (ANNs) were compared. The sensitivity test performed on the LIBS data shows that the models KNN, SIMCA, PLS-DA and ANN achieve an average classification accuracy of 96.2, 98.1, 90.6 and 100%, respectively. In contrast, the robustness test of the models SIMCA and PLS-DA yields accuracies of 97.7 and 98.8%, respectively. The correct identification of very similar species in terms of their elemental composition such as bornite/chalcopyrite and chalcocite/covellite is also achieved.

中文翻译：

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激光诱导击穿光谱和化学计量学方法测定铜基矿物质

使用激光诱导击穿光谱法（LIBS）对生岩石中的矿物种类进行直接鉴定。在铜工业中，总共分析了162种具有矿物学相关性的硫化物岩石。它们包含斑铁矿（Cu ₅ FeS ₄），黄铜矿（Cu ₂ S），黄铜矿（CuFeS ₂），科弗利特（CuS），顽辉石（Cu ₃ AsS ₄），辉钼矿（MoS ₂）和黄铁矿（FeS ₂）。从不同的采矿地点收集样品，以说明样品的可变性。探索了无监督多元方法，例如主成分分析（PCA）和树状图分析，而有监督模式识别技术，例如类比的软独立建模（SIMCA），偏最小二乘判别分析（PLS-DA），K近邻（ KNN），决策树分析和人工神经网络（ANN）进行了比较。对LIBS数据进行的敏感性测试表明，模型KNN，SIMCA，PLS-DA和ANN分别实现了96.2、98.1、90.6和100％的平均分类精度。相比之下，SIMCA模型和PLS-DA模型的鲁棒性测试分别产生了97.7和98.8％的准确度。