Comparative analysis of LDA, PLS-DA, SVM, RF, and voting ensemble for discrimination origin in greenish-white to white nephrites using LIBS

As there are distinct variations in economic value for greenish-white to white nephrites based on their geographical origin, it is crucial to develop a robust origin discrimination method for them. The reported correlation between the intensity of spectra and material properties gives us a clue that such a correlation may exist in nephrites worldwide. In this study, 364 pieces of greenish-white to white nephrite jades from different locations, including Qiemo, Qinghai, Xiuyan and Yecheng in China, South Korea, and Russia, were analyzed using laser-induced breakdown spectroscopy (LIBS). Four machine learning methods, including linear discriminant analysis (LDA), support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and an ensemble learning approach known as a voting classifier for origin discrimination were then employed. The results show a higher training accuracy of 99.81% (LDA), 94.01% (SVM), 100% (PLS-DA), 98.08% (RF), and 99.93% (voting classifier), with corresponding testing accuracies of 96.13%, 93.04%, 94.99%, 95.90%, and 99.93%, respectively. By appropriately selecting voting weights, the voting classifier effectively mitigates misclassification, achieving balanced accuracy for each origin. Therefore, the LIBS analyses could be utilized in the origin discrimination of greenish-white to white nephrite jades, offering valuable insights for accurately evaluating these gemstones, based on the successful application of various machine learning methods in the origin discrimination of nephrite jades. An integrated voting ensemble method was further introduced, providing new possibilities for rapid discrimination in diverse industries, including gemstone trading, manufacturing, archaeology, and more. Utilizing LIBS and four machine learning methods in conjunction with a voting classifier, accurate classification results could be achieved. This approach effectively improves and balances the accuracy rates for each origin.