Paper
4 May 2007 Processing multi-species terahertz spectra for detection of a particular species
Author Affiliations +
Abstract
The development of tera-hertz sources and receivers recently has given rise to a desire to do spectroscopy on molecular rotational spectra in this region. In particular the use of semiconductor photo-mixers provides practical packaging concepts and good sensitivity for building absorption spectrometers. This paper deals with some data processing concepts associated with absorption spectra from such spectrometers. Specifically, the concepts of multivariate analysis developed and applied over the last 25 years to infrared spectroscopy appear to be very useful to the analysis of tera-hertz absorption spectra. These concepts are particularly useful to address problems associated with: spectra baseline variations, species concentration estimates and spectral cross talk assessments when multiple species are present in the measurement sample. This latter element is of particular importance in tera-hertz spectroscopy when exploring multiple species with overlapping rotation spectral bands. In this paper some basics of multivariate analysis are reviewed. The baseline signatures or backgrounds of our photo-mixer absorption spectrometer are described and processed. Algorithm results are presented that remove backgrounds and other instrument spectral artifacts. Finally, a simulated example of separating overlapping spectra is presented.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert Noll "Processing multi-species terahertz spectra for detection of a particular species", Proc. SPIE 6549, Terahertz for Military and Security Applications V, 65490S (4 May 2007); https://doi.org/10.1117/12.718368
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Absorption

Spectrometers

Data modeling

Gases

Molecular spectroscopy

Statistical modeling

Autoregressive models

Back to Top