OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: James C. Wyant
  • Vol. 46, Iss. 23 — Aug. 10, 2007
  • pp: 6042–6046

Background subtraction in pulsed photoacoustics through neural-network processing

V. B. Slezak, A. L. Peuriot, M. G. González, and G. D. Santiago  »View Author Affiliations


Applied Optics, Vol. 46, Issue 23, pp. 6042-6046 (2007)
http://dx.doi.org/10.1364/AO.46.006042


View Full Text Article

Enhanced HTML    Acrobat PDF (829 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We report on the application of neural-network processing to pulsed photoacoustics for improving the detection limit by subtracting the window-heating-associated background. This technique was applied to the measurement of ethylene traces excited by a TEA (transverse electrical discharge in gas at atmospheric pressure) CO 2 laser. The signal contains a term that shows absorption saturation, characteristic of the absorbing gas, and another, generated by window heating, linearly dependent on laser energy. At low concentrations, normalization to laser energy is not possible owing to the different absorption mechanisms. To overcome this problem we relied on a neural-network filter, trained with experimentally obtained patterns, that subtracts the background and returns the sample concentration. This way, we reduced the detection limit to 20% of the previous limit obtained by reading the main resonance peak amplitude.

© 2007 Optical Society of America

OCIS Codes
(000.2170) General : Equipment and techniques
(300.6430) Spectroscopy : Spectroscopy, photothermal

ToC Category:
Spectroscopy

History
Original Manuscript: February 9, 2007
Revised Manuscript: June 11, 2007
Manuscript Accepted: June 15, 2007
Published: August 9, 2007

Citation
V. B. Slezak, A. L. Peuriot, M. G. González, and G. D. Santiago, "Background subtraction in pulsed photoacoustics through neural-network processing," Appl. Opt. 46, 6042-6046 (2007)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-46-23-6042


Sort:  Year  |  Journal  |  Reset  

References

  1. M. W. Sigrist, In Air Monitoring by Spectroscopic Techniques (Wiley, 1994).
  2. C. Brand, A. Winkler, P. Hess, A. Miklós, Z. Bozóki, and J. Sneider, "Pulsed-laser excitation of acoustic modes in open high-Q photoacoustic resonators for trace gas monitoring: results for C2H4," Appl. Opt. 34, 3257-3266 (1995). [CrossRef] [PubMed]
  3. P. Repond and M. Sigrist, "Photoacoustic spectroscopy on trace gases with continuously tunable CO2 laser," Appl. Opt. 35, 4065-4085 (1996). [CrossRef] [PubMed]
  4. A. Miklós, P. Hess, and Z. Bozóki, "Application of acoustic resonators in photoacoustic trace gas analysis and metrology," Rev. Sci. Instrum. 72, 1937-1955 (2001). [CrossRef]
  5. M. González, G. Santiago, A. Peuriot, V. Slezak, and C. Mosquera, "Improved pulsed photoacoustic detection by means of an adapted filter," J. Phys. IV 125, 677-679 (2005).
  6. M. G. González, G. Santiago, A. Peuriot, and V. Slezak, "Recovery of noisy pyroelectric-detector signals through neural-network processing," Rev. Sci. Instrum. 76, 053104 (2005). [CrossRef]
  7. A. Peuriot, G. Santiago, and C. Rosito, "Numerical and experimental study of stable resonators with diffractive output coupling," Opt. Eng. 41, 1903-1907 (2002). [CrossRef]
  8. V. Slezak, "Signal processing in pulsed photoacoustic detection of traces by means of a fast Fourier transform-based method," Rev. Sci. Instrum. 74, 642-644 (2003). [CrossRef]
  9. M. G. González, G. D. Santiago, A. L. Peuriot, and V. B. Slezak, "Pulsed optoacoustic detection of ethylene by means of TEA CO2 laser," An. AFA 17, 110-114 (2005).
  10. S. Haykin, Neural Networks (Mcmillan College, 1994).
  11. J. Hertz, A. Krogh, and R. Palmer, Introduction to the Theory of Neural Computation (Addison-Wesley, 1994).
  12. M. Riedmiller and H. Braun, "A direct adaptive method for faster backpropagation learning: the RPROP algorithm," IEEE International Conference on Neural Networks (IEEE, 1993), pp. 586-591. [CrossRef]

Cited By

Alert me when this paper is cited

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

OSA is a member of CrossRef.

CrossCheck Deposited