Optimization model for mineral composition data analysis and its application in jade classification

Abstract The classification of jade grade has always been a very critical part of the jade industry, and improving the accuracy of jade grade classification is of great significance to the sustainable development of the jade industry. The study constructs a mineral identification classification model based on Raman spectroscopy + PCA through Raman spectroscopy and PCA principal component analysis and analyzes the data of jade grades and constituents. The actual performance of this paper’s model is explored by comparing its effectiveness with other algorithmic models in jade classification and the accuracy of classification parameters. The model in this paper is feasible in classifying the four grades of Hetian jade (seed material, gobi material, shanliushui material, and shanmu material). Green dense jade’s main minerals are <unk>-quartz and a few other minerals, including albite, hematite, graphite, and tourmaline. The main compositions of the sample jade are SiO 2 , Al 2 O 3 , and K 2 O. The overall accuracy of this paper’s model in classifying Xinjiang Hotan jade grades is 97.9%, which is significantly higher than that of the KNN classification algorithm and SVM classification algorithm. The total accuracy of this paper’s model on each parameter of jade grade is 85, which is higher than the 60 of the KNN algorithm and the 62 of the SVM algorithm, and the classification accuracy grade is high.