Research on classification methods for rubber based on terahertz time-domain spectroscopy with data fusion strategy

Abstract

Rubber, an essential industrial product, plays a crucial role in industry and human life. However, some manufacturers of rubber use inferior, recycled or low-cost rubber materials to replace high-quality rubber products. Therefore, in order to ensure industrial production, protect natural environment and ensure personal safety, it is of great economic and social significance to enable rapid and non-destructive qualitative identification of rubber products. In this paper, eight different types of black rubber, including Nitrile Butadiene Rubber (NBR), Choloroprene Rubber (CR) and Ethylen Propylen Diene Monomer (EPDM), were selected as research objects. Terahertz Time-Domain Spectroscopy (THz-TDS) technology was utilized to get the absorption spectra, refractive index spectra and time-domain spectra of these samples for exploring the differences between different types of rubber in terahertz spectra. To reduce the data volume, three band selection algorithms were employed to extract features from the absorption spectra and refractive index spectra, and temporal features of time-domain spectra were extracted for subsequent data processing. In the selection of classification model algorithms, K-Nearest Neighbor (KNN), Random Forest (RF) and Supervised Kohonen Neural Networks (S-Kohonen) were employed to recognize these spectral data. To fully exploit the latent information within these data and considering the possibility of the loss of partial information in single-type signal, a feature-level data fusion method was applied to different optical parameter signals to obtain more information about terahertz spectra during the process of data modeling. The results of the research indicated that the model with the highest prediction accuracy was the KNN model, with an accuracy of 97.37%, when refractive index spectra and time-domain spectra were fused as inputs, the F1-score reached 99.64. The performance of data fusion was noticeably better than that of a single-type signal. This study validates the feasibility of using terahertz time-domain spectroscopy for qualitative identification of different types of rubber.