Detection and identification of three types of kaolin using terahertz time-domain spectroscopy

Kaolin, as a versatile non-metallic mineral resource, is widely used in ceramics, casting, construction, and other fields. To rapidly and accurately identify and classify three polymorphs of kaolin, while addressing the challenge of aligning resource characteristics with application requirements, a novel method for kaolin identification and classification has been proposed. This method employs terahertz time-domain spectroscopy (THz-TDS) in conjunction with cluster analysis (CA) and principal component analysis (PCA). It facilitates the extraction of spectral data, the calculation of Euclidean distances, and effective dimensionality reduction of the dataset. Terahertz spectroscopy, a non-destructive analytical technique, was employed to obtain terahertz spectra from three kaolin specimens using a transmissive terahertz spectrometer. The refractive index, dielectric constant, and absorption coefficient of the samples within the frequency range of 0.5 THz to 2.75 THz were calculated from the THz-TDS data, utilizing established equations, including the fast Fourier transform. Using all available refractive index and absorption coefficient data within this frequency band as input variables, CA and PCA were conducted separately to determine the Euclidean distance and the first principal component (PC1) for the corresponding samples. The findings reveal significant variations in the refractive index and absorption coefficient among the analyzed samples. PCA identified three primary components for both the refractive index and absorption coefficient, which accounted for cumulative contribution rates of 99.99% and 97.26%, respectively. CA categorized the samples into three distinct groups based on Euclidean distance metrics, with the CA clusters closely aligning with the PCA results. The sandy kaolin (SZGLT-GX325) exhibited the highest PC1 score and the greatest Euclidean distance from the second group, measuring 156.16. Utilizing the CA-PCA model, the three types of kaolin were accurately identified and classified based on inter-sample variations, achieving a classification accuracy of 99.99%. This study underscores the utility of THz-TDS combined with chemometric techniques, providing a robust framework for the rapid, precise, and non-destructive analysis of kaolin, thereby demonstrating significant potential for practical applications.