Application of Machine Learning to Terahertz Spectroscopic Imaging of Reagents Hidden By Thick Shielding Materials
Kosuke Murate, Hiroki Kanai, Kodo Kawase
Abstract
We achieved high identification accuracy of reagents hidden by thick shielding materials, by combining injection-seeded terahertz (THz) wave parametric generator measurements and machine learning analysis. The analysis performance of three methods, support vector machine (SVM), k-nearest neighbor, and random forest, was compared in an attempt to identify the optimal approach. SVM proved to be the best model. Conventional systems could only identify reagents through premeasured shields; however, incorporation of machine learning allowed us to identify the reagents through shielding materials that had not been premeasured. Moreover, spectroscopic imaging of the reagents revealed the distribution pattern of the reagents, even through thick shielding materials that attenuated THz frequencies such that they were close to the noise level.