In this paper, the THz spectra of several weakly absorptive chemical materials are measured by a novel technology:
reference-free THz transmission spectroscopy. We then introduce a new THz spectral feature recognition method by
applying pattern recognition method in explosive spectra classification and identification. Two types of artificial neural
networks (ANNs): self organized mapping (SOM) and multilayer perceptron (MLP) and are used through repetitive
modeling and adequate training with high accuracy rate and low alarm rate. The results indicate that the reference-free
technique is a viable and valid spectroscopic modality by replacing the conventional absorbance curves with phase
curves as features for identification and classification. The phase spectroscopy method is especially favorable for long
distance and large size THz sensing and imaging systems by ignoring the imperfect beam amplitude profile. We also
show that it is feasible to apply these two ANNS on the identification of different types of explosives, amino acid and
many other chemicals. It provides an effective method in material inspection and identification using THz-TDS.
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