Classification of Terahertz Reflection Spectra using Machine Learning Algorithms
- Resource Type
- Conference
- Authors
- Kristensen, Mathias Hedegaard; Cielecki, Pawel Piotr; Skovsen, Esben
- Source
- 2022 47th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz) Infrared, Millimeter and Terahertz Waves (IRMMW-THz),2022 47th International Conference on. :1-2 Aug, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Signal Processing and Analysis
Dimensionality reduction
Spectroscopy
Machine learning algorithms
Reflection
Linear discriminant analysis
Classification algorithms
Reliability
- Language
- ISSN
- 2162-2035
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.