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20 September 2007 Identification of THz absorption spectra of chemicals using neural networks
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Abstract
Absorption spectra in the range from 0.2 to 2.6 THz of chemicals such as illicit drugs and antibiotics obtaining from Terahertz time-domain spectroscopy technique were identified successfully by artificial neural networks. Back Propagation (BP) and Self-Organizing Feature Map (SOM) were investigated to do the identification or classification, respectively. Three-layer BP neural networks were employed to identify absorption spectra of nine illicit drugs and six antibiotics. The spectra of the chemicals were used to train a BP neural network and then the absorption spectra measured in different times were identified by the trained BP neural network. The average identification rate of 76% was achieved. SOM neural networks, another important neural network which sorts input vectors by their similarity, was used to sort 60 absorption spectra from 6 illicit drugs. The whole network was trained by setting a 20×20 and a 16×16 grid, and both of them had given satisfied clustering results. These results indicate that it is feasible to apply BP and SOM neural networks model in the field of THz spectra identification.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jingling Shen, Yan Jia, Meiyan Liang, and Sijia Chen "Identification of THz absorption spectra of chemicals using neural networks", Proc. SPIE 6695, Optics and Photonics for Information Processing, 66951F (20 September 2007); https://doi.org/10.1117/12.732350
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