Classification of Terahertz Reflection Spectra using Machine Learning Algorithms

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

A successful implementation of terahertz screening systems requires a development of reliable and efficient identification algorithms. Dimensionality reduction methods are applied to lower the dimensionality of multivariate data while retaining most of the information. Here, we focus on Principal component analysis (PCA) and linear discriminant analysis (LDA) for analysis and classification of terahertz reflection spectra. The complete data set consists of more than 5000 reflection spectra of six active materials. We found that LDA is better for grouping the spectra resulting in highly accurate classification of terahertz spectra. Furthermore, we compare the classification of referenced and non-referenced reflection spectra eligible for real-world applications of terahertz spectroscopy.

Original languageEnglish
Title of host publicationIRMMW-THz 2022 - 47th International Conference on Infrared, Millimeter, and Terahertz Waves.
PublisherIEEE
Publication date26 Sep 2022
Article number9895909
ISBN (Electronic)9781728194271
DOIs
Publication statusPublished - 26 Sep 2022
Event47th International Conference on Infrared, Millimeter and Terahertz Waves, IRMMW-THz 2022 - Delft, Netherlands
Duration: 28 Aug 20222 Sep 2022

Conference

Conference47th International Conference on Infrared, Millimeter and Terahertz Waves, IRMMW-THz 2022
Country/TerritoryNetherlands
CityDelft
Period28/08/202202/09/2022
SeriesInternational Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)
ISSN2162-2035

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