Identification terahertz spectra for the dyestuffs based on principal component analysis and Savitzky-Golay filter

Abstract Terahertz time-domain spectroscopy (THz-TDS) was employed to measure the absorption spectra for three types of dyestuff at the frequency range of 0.3–2.2 THz. The raw spectra data were implemented dimensionality reduction by using principal component analysis (PCA). Depending on the weak cluster trend determined by the score plot, different levels of Savitzky-Golay (SG) smoothing combined with PCA processing was performed for elevating the recognition rate. The recognition effects were assessed by fuzzy c-means (FCM) and k-means clustering techniques, which consistently demonstrated the combination of polynomial order 1 and window size 5 for SG smoothing achieved the highest accuracy of 94.44%. For k-means and FCM clustering, the identification accuracies of raw spectra were 87.5% and 84.72% respectively, suggesting the elevation recognition rate by using SG smoothing with polynomial order 1 and window size 5. Our results suggested SG smoothing coupled with PCA was a potent method to cope with THz spectra recognition for dyestuffs.