OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editor: Gregory W. Faris
  • Vol. 4, Iss. 5 — May. 5, 2009

Improved Algorithm for Quantitative Analyses of Infrared Spectra of Multicomponent Gas Mixtures with Unknown Compositions

Michele Gianella and Markus W. Sigrist

Applied Spectroscopy, Vol. 63, Issue 3, pp. 338-343 (2009)


View Full Text Article

Acrobat PDF (336 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations
  • Export Citation/Save Click for help

Abstract

We present a major improvement of an algorithm based on a spectral library search for the quantitative analysis of multicomponent gas samples with unknown compositions. A quantitative spectral database of infrared spectra is used as a training set to compute regression coefficients. Concentrations are computed in the principal component space via principal component regression (PCR). In addition to previous algorithms, we introduce a rating for each candidate substance depending on the concentration found with PCR and a filter that removes candidates that are predicted with negative concentrations if their rating is below a certain threshold. Negative concentrations arise when the measured spectrum contains components that are not contained in the database. The PCR is recomputed until all candidates have a rating above the threshold. Then an adaptive filter "subtracts" the substance with the highest rating from both the measured spectrum and the library and appends it to a hit list. The iteration of this procedure directly produces a list of substances in order of descending importance (i.e., contribution to the measured absorbance) with their corresponding concentrations. The algorithm is tested on spectra of multicomponent surgical smoke samples. The four main components (water, methane, ethane, and ethene) are identified correctly (within the top 5 of the hit list) for an appropriate choice of the rating threshold. The algorithm describes the composition of the smoke sample correctly despite the presence of features in the spectrum that cannot be explained by the spectrum of any single substance present in the database.

Virtual Issues
Vol. 4, Iss. 5 Virtual Journal for Biomedical Optics

Citation
Michele Gianella and Markus W. Sigrist, "Improved Algorithm for Quantitative Analyses of Infrared Spectra of Multicomponent Gas Mixtures with Unknown Compositions," Appl. Spectrosc. 63, 338-343 (2009)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=as-63-3-338


Sort:  Journal  |  Reset

References

References are not available for this paper.

Cited By

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited