Classification of Terahertz Reflection Spectra using Machine Learning Algorithms

Abstract

The unique properties of terahertz (THz) spectroscopy shows a great potential for security and defense applications such as safe screening of persons and objects. However, a successful implementation of THz screening systems requires a development of reliable and efficient identification algorithms. Dimensionality reduction (DR) methods aim to reduce the dimensionality of the multivariate data and are therefore commonly used as a preprocessing step for classification algorithms and as an analytical tool allowing data visualization. In this paper, we compare the use of unsupervised and supervised DR methods for analysis and classification of THz reflection spectra based on their most widespread linear representatives, namely Principal Component Analysis and Linear Discriminant Analysis, respectively. To this end, both methods were applied to more than 5000 THz reflection spectra acquired from six active materials mixed at three different concentrations with polyethylene and measured at various humidity conditions. While considering scenarios with different level of complexity, we found that supervised approach provide better results because it enables efficient grouping despite intraclass variability. Furthermore, we showed that manipulating labels introduced into the supervised DR algorithm allows conditioning the data for a desired classification task such as security screening. Presented classification results show that simple machine learning algorithms are sufficient for highly accurate classification (>98.6%) of THz spectra, which will be suitable for many real-life applications of THz spectroscopy based on material identification.

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“…All of these methods can be used to analyse THz data by training the algorithm on a set of priori known samples with known classes and then applying the algorithm to new, unknown samples to predict their class. Another ML algorithm for the analysis and classification of THz spectra is also a linear discriminant analysis (LDA) [201], which is a decision-making statistical method for optimal characteristic feature search and selection without omitting other complementary features [202]. This supervised learning dimensionality reduction algorithm finds a linear combination of features that best separates the classes in a dataset.…”