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Classification for glucose and lactose Terahertz spectra based on SVM and DNN methods
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Li, Kaidi, Chen, Xuequan, Zhang, Rui and Pickwell-MacPherson, Emma (2020) Classification for glucose and lactose Terahertz spectra based on SVM and DNN methods. Transactions on Terahertz Science and Technology, 10 (6). pp. 617-623. doi:10.1109/TTHZ.2020.3013819 ISSN 2156-342X.
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WRAP-Classification-glucose-lactose-terahertz-spectrums-Pickwell-MacPherson-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1303Kb) | Preview |
Official URL: https://doi.org/10.1109/TTHZ.2020.3013819
Abstract
In recent decades, terahertz (THz) radiation has been widely applied in many chemical and biomedical areas. Due to its ability to resolve the absorption features of many compounds noninvasively, it is a promising technique for chemical recognition of substances such as drugs or explosives. A key challenge for THz technology is to be able to accurately classify spectral measurements acquired in unknown complicated environments, rather than those from ideal laboratory conditions. Support vector machine (SVM) and deep neural networks (DNNs) are powerful and widely adopted approaches for complex classification with a high accuracy. In this article, we explore and apply the SVM and DNN methods for classifying the frequency spectra of glucose and lactose. We measured 372 groups of independent signals under different conditions to provide a sufficient training set. The classification accuracies achieved were 99% for the SVM method and 89.6% for the DNN method. These high classification accuracies demonstrate great potential in chemical recognition.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QP Physiology T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TP Chemical technology |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | |||||||||
| Library of Congress Subject Headings (LCSH): | Terahertz spectroscopy -- Methods, Glucose -- Metabolism, Terahertz spectroscopy, Biophysics -- Research, Medicine -- Research, Support vector machines, Neural networks (Computer science), Lactose | |||||||||
| Journal or Publication Title: | Transactions on Terahertz Science and Technology | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 2156-342X | |||||||||
| Official Date: | November 2020 | |||||||||
| Dates: |
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| Volume: | 10 | |||||||||
| Number: | 6 | |||||||||
| Page Range: | pp. 617-623 | |||||||||
| DOI: | 10.1109/TTHZ.2020.3013819 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 12 August 2020 | |||||||||
| Date of first compliant Open Access: | 12 August 2020 | |||||||||
| RIOXX Funder/Project Grant: |
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