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

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.