Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 71,
  • Issue 12,
  • pp. 2653-2660
  • (2017)

Wavelength Selection Method Based on Differential Evolution for Precise Quantitative Analysis Using Terahertz Time-Domain Spectroscopy

Not Accessible

Your library or personal account may give you access

Abstract

Quantitative analysis of component mixtures is an important application of terahertz time-domain spectroscopy (THz-TDS) and has attracted broad interest in recent research. Although the accuracy of quantitative analysis using THz-TDS is affected by a host of factors, wavelength selection from the sample’s THz absorption spectrum is the most crucial component. The raw spectrum consists of signals from the sample and scattering and other random disturbances that can critically influence the quantitative accuracy. For precise quantitative analysis using THz-TDS, the signal from the sample needs to be retained while the scattering and other noise sources are eliminated. In this paper, a novel wavelength selection method based on differential evolution (DE) is investigated. By performing quantitative experiments on a series of binary amino acid mixtures using THz-TDS, we demonstrate the efficacy of the DE-based wavelength selection method, which yields an error rate below 5%.

© 2017 The Author(s)

PDF Article
More Like This
Wavelength selection of terahertz time-domain spectroscopy based on a partial least squares model for quantitative analysis

Qingxiao Ma, Chun Li, Biao Wang, Xin Ma, and Ling Jiang
Appl. Opt. 60(19) 5638-5642 (2021)

Quantitative analysis of water distribution in human articular cartilage using terahertz time-domain spectroscopy

Euna Jung, Hyuck Jae Choi, Meehyun Lim, Hyeona Kang, Hongkyu Park, Haewook Han, Byung-hyun Min, Sangin Kim, Ikmo Park, and Hanjo Lim
Biomed. Opt. Express 3(5) 1110-1115 (2012)

Mean estimation empirical mode decomposition method for terahertz time-domain spectroscopy de-noising

Xiaoli Qiao, Xinming Zhang, Jiaojiao Ren, Dandan Zhang, Guohua Cao, and Lijuan Li
Appl. Opt. 56(25) 7138-7145 (2017)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.