Identification of iron ore brands by multi-component analysis and chemometric tools.

Abstract

Identification of iron ore brand is one of the most important precautions against fraud in the international iron ore trade. However, the identification of iron ore brand can be sophisticated, due to fact that the role played by multi-component in iron ore brand identification was unclear. This study aims to establish an objective approach to identify iron ore brands based on their multi-component content. A total of 1469 batches of iron ore samples, covering 16 commonly consumed iron ore brands from 3 countries, were analyzed for multi-component content. It was investigated that 10 primary, minor, and trace chemical components varied significantly in contents according to different iron ore brands. This prospective relationship between the multi-component contents and the iron ore brand was then used to place 16 brands into 12 groups and 8 brands of them were correctly identified by a flowchart. Furthermore, chemometric tools such as linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and support vector machine (SVM) were applied to construct models to simultaneously discriminate 16 iron ore brands. Both the training and test results proved that LDA performed best in this circumstance. In the LDA method, MgO, Fe, SiO2, and P are the feature components contributing the most to the identification of 16 brands of iron ore. Based on the findings, the multi-components are distinct variables to establish an internationally recognized model of iron ore brand identification.