Classification for Glucose and Lactose Terahertz spectra based on SVM and DNN methods
- Resource Type
- Conference
- Authors
- Li, Kaidi; Chen, Xuequan; Pickwell-MacPherson, Emma
- Source
- 2020 45th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2020 45th International Conference on. :1-1 Nov, 2020
- 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
Training
Spectroscopy
Neural networks
Support vector machine classification
Security
Time-domain analysis
Chemicals
- Language
- ISSN
- 2162-2035
We propose an approach based on support vector machine(SVM) and deep neural networks (DNN) to classify chemical substances under different experimental conditions in terahertz time-domain spectroscopy (THz-TDS). 372 groups of independent signals under different conditions were measured to provide a sufficient training set. 99% accuracy for the SVM and 89.6% for the DNN method are realized in the test set. These excellent classification results show the high potentials in chemical recognition, security detection or clinical diagnosis.