Classification of materials using terahertz spectroscopy with principal components analysis

Terahertz spectroscopy has multivariable and produces large volumes of data. Principal component analysis (PCA) is a statistics analysis method of dimensionality reduction of multivariate. We studied the feasibility of PCA method for material classification in terahertz region. The results show that PCA is able to differentiate materials obviously if initial variables are chosen properly.