On the Flame Spectrum Recovery by Using a Low-Spectral Resolution Sensor

  • Luis Arias
  • Sergio Torres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


In this paper, the Maloney-Wandell and Imai-Berns recovering spectrum techniques are evaluated to extract the continuous flame spectrum, by using three principal components from training matrices constructed from a flame’s spectrum database. Six different sizes of training matrices were considered in the evaluation. To simulate the Maloney-Wandell and Imai-Bern methods, a commercial camera sensitivity was used as a base in the extraction process. The GFC (Goodness-of-fit coefficient) and RMSE (Root-mean-square error) quality metrics were used to compare the performance in the recovering process. The simulation results shown a better performance by using the Maloney-Wandell method in the recovering process, with small sizes of training matrices. The achieved results make of the recovering-spectral techniques a very attractive tools for designing advanced monitoring strategies for combustion processes.


recovering techniques flame spectrum optical sensors 


  1. 1.
    Docquier, N., Candel, S.: Combustion control and sensors: a review. Progress in Energy and Combustion Science 28, 107–150 (2001)CrossRefGoogle Scholar
  2. 2.
    Arias, L., Torres, S., Sbarbaro, D., Farias, O.: Photodiode-based sensor for flame sensing and combustion-process monitoring. Appl. Opt. 47, 66–77 (2008)CrossRefGoogle Scholar
  3. 3.
    Hernandez, R., Ballester, J.: Flame imaging as a diagnostic tools for industrial combustion. Combustion and Flame 155, 509–528 (2008)CrossRefGoogle Scholar
  4. 4.
    Manolakis, D., Marden, D., Shaw, G.A.: Hyperspectral image processing for automatic target detection applications. Lincoln Laboratory J. 14(1), 79–116 (2003)Google Scholar
  5. 5.
    Landgrebe, D.: Hyperspectral image data analysis. IEEE Signal Processing Magazine 19(1), 17–28 (2002)CrossRefGoogle Scholar
  6. 6.
    Maloney, L.T., Wandell, B.A.: Color constancy: a method for recovering surface spectral reflectance, vol. 3, pp. 29–33 (1986)Google Scholar
  7. 7.
    Imai, F.H., Berns, R.S.: Spectral estimation using trichromatic digital cameras, vol. 3, pp. 42–48 (1999)Google Scholar
  8. 8.
    Imai, F., Rosen, M., Berns, R.: Comparative study of metrics for spectral match quality, pp. 492–496 (2002)Google Scholar
  9. 9.
    Lopez, M., Hernández, J., Valero, E., Nieves, J.: Colorimetric and spectral combined metric for the optimization of multispectral systems, pp. 1685–1688 (2005)Google Scholar
  10. 10.
    Nieves, J.L., Valero, E.M., Hernandez, J., Romero, J.: Recovering fluorescent spectra with an RGB digital camera and color filters using different matrix factorizations, vol. 46, pp. 4144–4154 (2007)Google Scholar
  11. 11.
    Lopez, M., Hernandez, J., Valero, E., Romero, J.: Selecting algorithms, sensors, and linear bases for optimum spectral recovery of skylight, vol. 24, pp. 942–956 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luis Arias
    • 1
  • Sergio Torres
    • 2
  1. 1.Department of Electrical EngineeringUniversity of ConcepcioConcepcionChile
  2. 2.Center for Optics and Photonics CEFOPUniversity of ConcepcionConcepcionChile

Personalised recommendations