Application of mathematical models and digital filters and their Processors of spectral analysis for aromatic compounds gas in a fluorescent chemical

Document Type : Research Paper


1 Faculty of Informatics, Department of Technical Cybernetics, Samara State University, Samara, RUSSIA, st..

2 Faculty of Informatics, Department of Technical Cybernetics, Samara State University, Samara, RUSSIA, st. Molodogvardeyskaya, 151, Building 1.

3 Faculty of Biology, Samara State University, 443011, Russia, Samara, Building 22b, st. Academician Pavlova, 1.


This work studies the effect of different methods of spectra processing on the aromatic compounds (benzene, toluene and xylene). keeping up high-spatial objectives is progressively basic and various methodologies are utilized to check fragrant compounds that anticipate choosing a specific technique from research centre determinations. One notable part of all types of signal systems is the flexibility of adaptation. In addition, a spatial exactness is not fundamental to get a range of an expansive number of fragrant compounds where more prominent characterization and statistical mean are critical. Moreover, sufficiently low deviations of the expected values are achieved from the true values and the standard deviation to determine the properties of fragrant compounds compared with those of the aromatic compounds. A persistent baseline rectification is smoothed and performed followed by normalizing the rectified spectrum to their area. The auto fluorescence foundation is subtracted, for the pure range analysis, by utilizing scientific approaches: polynomial estimation (Poly Fit) and method Processors Gases Improved. The accuracy and reliability obtained are not complete and can be increased by developing algorithms, selecting other parameters and improving the quality of the training sample by eliminating the unwanted data. This could be done by increasing the sample size and studying it in more detail to avoid inaccuracies during the transition between concentrations Gas.


[1] N. Brown and P. Bladon, ”Spectroscopy and structure of (1, 3-diketonato) boron difluorides and related compounds,” Journal of the Chemical Society A: Inorganic, Physical, Theoretical, pp. 526-532, 1969.
[2] P. Cadusch, M. Hlaing, S. Wade, S. McArthur, and P. Stoddart, ”Improved methods for fluorescence background
subtraction from Raman spectra,” Journal of Raman Spectroscopy, vol. 44, pp. 1587-1595, 2013.
[3] T. T. Cai, D. Zhang, and D. Ben–Amotz, ”Enhanced chemical classification of Raman images using multiresolution wavelet transformation,” Applied spectroscopy, vol. 55, pp. 1124-1130, 2001.
[4] J. D. A. Espinoza, V. Sazhnikov, S. Sabik, D. Ionov, E. Smits, S. Kalathimekkad, et al., ”Flexible optical chemical
sensor platform for BTX,” Procedia Engineering, vol. 47, pp. 607-610, 2012.
[5] R. Heinrich, A. Popescu, A. Hangauer, R. Strzoda, and S. H¨ofling, ”High performance direct absorption spectroscopy of pure and binary mixture hydrocarbon gases in the 6–11 $$ rupmu $$ m range,” Applied Physics B,
vol. 123, pp. 1-9, 2017.
[6] R. Kengne-Momo, P. Daniel, F. Lagarde, Y. Jeyachandran, J. Pilard, M. Durand-Thouand, et al., ”Protein interactions investigated by the Raman spectroscopy for biosensor applications,” International Journal of Spectroscopy,
vol. 2012, 2012.
[7] A.-I. Z. Khalaf, M. Alboedam, H. jwad Abidalhussein, and A.-Z. S. Hassan, ”Detecting levels amino acids for
proteins of different for patients with myeloma and comparing them using a portable Raman spectrometer,”
EurAsian Journal of BioSciences, vol. 14, pp. 2029-2036, 2020.
[8] A.-I. Z. Khalaf, M. Alboedam, H. J. Abidalhussein, and A.-Z. S. Hassan, ”The role of blood proteins and nucleic
acids in the detection of multiple Myeloma based on Raman spectroscopy,” EurAsian Journal of BioSciences, vol.
14, pp. 1955-1963, 2020.
[9] A. Khlebunov, D. Ionov, P. Komarov, V. Aristarkhov, V. Sazhnikov, A. Petrov, et al., ”An experimental system
for investigating the characteristics of optical sensor materials,” Instruments and Experimental Techniques, vol.
52, pp. 132-136, 2009.
[10] J. Luo, K. Ying, P. He, and J. Bai, ”Properties of Savitzky–Golay digital differentiators,” Digital Signal Processing, vol. 15, pp. 122-136, 2005.
[11] T. C. E. Marcus, M. H. Ibrahim, N. H. Ngajikin, and A. I. Azmi, ”Optical path length and absorption cross
section optimization for high sensitivity ozone concentration measurement,” Sensors and Actuators B: Chemical,
vol. 221, pp. 570-575, 2015.
[12] P. Mosier-Boss, S. Lieberman, and R. Newbery, ”Fluorescence rejection in Raman spectroscopy by shifted-spectra,
edge detection, and FFT filtering techniques,” Applied Spectroscopy, vol. 49, pp. 630-638, 1995.
[13] A. O’Grady, A. C. Dennis, D. Denvir, J. J. McGarvey, and S. E. Bell, ”Quantitative Raman spectroscopy of
highly fluorescent samples using pseudosecond derivatives and multivariate analysis,” Analytical chemistry, vol.
73, pp. 2058-2065, 2001.
[14] T. Ouyang, C. Wang, Z. Yu, R. Stach, B. Mizaikoff, B. Liedberg, et al., ”Quantitative analysis of gas phase IR
spectra based on extreme learning machine regression model,” Sensors, vol. 19, p. 5535, 2019.
[15] F. Pena-Pereira, I. Costas-Mora, V. Romero, I. Lavilla, and C. Bendicho, ”Advances in miniaturized UV-Vis
spectrometric systems,” TrAC Trends in Analytical Chemistry, vol. 30, pp. 1637-1648, 2011.
[16] L. Quintero, S. Hunt, and M. Diem, ”Denoising of raman spectroscopy signals,” in Poster presented at the 2007
R2C Multi Spectral Discrimination Methods Conference, 2007.
[17] A. P. Shreve, N. J. Cherepy, and R. A. Mathies, ”Effective rejection of fluorescence interference in Raman
spectroscopy using a shifted excitation difference technique,” Applied spectroscopy, vol. 46, pp. 707-711, 1992.
[18] S. Twiss, D. Teague, J. Bozek, and M. Sink, ”Application of infrared spectroscopy to exhaust gas analysis,”
Journal of the Air Pollution Control Association, vol. 5, pp. 75-83, 1955.
Volume 12, Special Issue
December 2021
Pages 109-122
  • Receive Date: 14 October 2020
  • Revise Date: 22 January 2021
  • Accept Date: 28 February 2021