Combining PLS-DA and SIMCA on NIR data for classifying raw materials for tyre industry: A hierarchical classification model

Tyre materials are complex products, as they are prepared using a number of raw materials, each of them with its specific chemical composition and functionality in the final product. It is, therefore, of crucial importance to avoid mislabeling errors and even to verify the compliance of raw materials entering the factory.

The present study proposes a strategy that makes use of near infrared (NIR) spectroscopy combined with chemometrics for raw material identification (RMID) and compliance verification of the most common raw materials used in the tyre industry. In particular, the chemometric model developed consists of a global hierarchical classification model, which combines nested PLS-DA nodes for RMID and SIMCA nodes for compliance verification, in a two-step approach.

The global model showed satisfactory results, as a 100 % of total correct predictions and a sensitivity higher than 90 % in the test set were obtained for most of the classes of interest.

The strategy obtained has the final goal of being directly applied on the raw materials at their receiving stage in factory, with the double advantage of minimizing the risk of mislabeling and, at the same time, decreasing the number of suspicious samples that need to be analyzed in the laboratory, by means of traditional methods, for verifying their compliance.