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) CO2 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

Full Article  |  PDF Article

References

  • View by:
  • |
  • |

  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]

2005

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).

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]

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).

2003

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]

2002

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]

2001

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]

1996

1995

An. AFA

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).

Appl. Opt.

J. Phys. IV

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).

Opt. Eng.

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]

Rev. Sci. Instrum.

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]

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]

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]

Other

S. Haykin, Neural Networks (Mcmillan College, 1994).

J. Hertz, A. Krogh, and R. Palmer, Introduction to the Theory of Neural Computation (Addison-Wesley, 1994).

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]

M. W. Sigrist, In Air Monitoring by Spectroscopic Techniques (Wiley, 1994).

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (8)

Fig. 1
Fig. 1

Experimental setup.

Fig. 2
Fig. 2

Calibration curve and detection limit for ethylene diluted in pure nitrogen.

Fig. 3
Fig. 3

Temporal records: solid curve, background for pure nitrogen; dashed curve, typical signal for an ethylene–nitrogen mixture.

Fig. 4
Fig. 4

Sample (dashed curve) and window (solid curve) spectra.

Fig. 5
Fig. 5

Shot-to-shot variations of the window signal spectrum. Inset, expanded view around the first longitudinal mode.

Fig. 6
Fig. 6

Neural-network structure.

Fig. 7
Fig. 7

Performance comparison. Open circles, direct reading of the first longitudinal mode amplitude; filled circles, neural-network filtering.

Fig. 8
Fig. 8

Computed error versus number of bits.

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

S = K c e l l α ( F ) F C .
α = α 0 1 + F / F s ,
z = j = 1 15 w j f ( i = 1 64 w j i x i θ j ) θ .
e = k [ t k j w j f ( i w j i x i k θ j ) θ ] 2 ,

Metrics