Abstract
The direct method features of finding the weight coefficients of the mixed molecular spectrum components on the basis of their reference samples are considered in this paper. It has been established that the presence of additive noise in the output mixed spectrum generates a noise component with an unidentified probability distribution law in the found weight coefficients. The power generated by the noise can be several orders of magnitude higher than the power of the output signal additive noise. It is shown that the use of numerical methods for suppressing this noise, which is not based on its statistical characteristics, in particular, the median filtration, expands the limits of SNR, in which the proposed method maintains efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gallego AL, Guesalagu AR, Bordeu E, Gonza X, Lez AS (2011) Rapid measurement of phenolics compounds in red wine using Raman spectroscopy. IEEE Trans Instrum Meas 60(2):507–512
Potgieter-Vermaak S, Maledi N, Wagner N, Van Heerden J, Van Grieken R, Potgieter J (2011) Raman spectroscopy for the analysis of coal. Raman Spectrosc 42(2):123–129
Wang Q, Allred D, Knight I (1995) Deconvolution of the Raman spectrum of amorphous carbon. Raman Spectrosc 26(12):1039–1043
Presser V (2009) Metamictization in zircon, Part I: Riman investigation following a Rietveld approach: profile line dtconvolution technique. Raman Spectrosc 40(5):491–498
Fraser R, Suzuki E (1996) Resolution of overlapping absorption bands by least squares procedures. Anal Chem 38(12):1770–1773
Sundius T (1996) Computer fitting of Voigt profiles to Raman lines. Raman Spectrosc 1(5):471–488
Singh RK, Singh SN, Ashtana BP, Pathak CM (1994) Deconvolution of Lorentzian Raman linewidth: techniques of polynomial fitting and extrapolation. Raman Spectrosc 25(6):423–428
Talulian SA (2003) Attenuatid total reflection Fourier transform infrared spectroscopy: a method of choice for studying membrane proteins and lipids. Biochemistry 42(41):11898–11907
Alsmeyer F, Marquardt W (2004) Automatic generation of peak-shaped models. Appl Spectrosc 58(8):986–994
Lorenz-Fonfria V, Padros E (2004) Curve-fitting overlapped bands: quantification and improvement of curve-fitting robustness in the presence of errors in the model and in the data. Analyst 129(12):1243–1250
Kauppinen JK, Moffatt DJ, Muntsch HH, Cameron DG (1981) Fourier transforms in the computation of self-deconvoluted and firstorder derivative spectra of overlapped band contours. Anal Chem 53(9):1454–1457
Kauppinen JK, Moffatt DJ, Mantsch HH, Cameron DG (1981) Fourier self-deconvolution: a method for resolving intrinsically overlapped bands. Appl Spectrosc 35(3):271–276
Lorenz-Fonfria VA, Padros DE (2005) Maximum entropy deconvolution of infrared spectra: use of a novel entropy expression without sign restriction. Appl Spectrosc 59(4):474–486
Lorenz-Fonfria VA, Padros DE (2008) Method for the estimation of the mean Lorentzian bandwidth in spectra composed of an unknown number of highly overlapped bands. Appl Spectrosc 62(6):689–700
Crilly PB (1991) A quantitative evaluation of various iterative deconvolution algorithms. IEEE Trans Instrum Meas 40(3):558–562
Jansson PA (1984) Deconvolution: with applications in spectroscopy. Academic, New York
Senga Y, Minami K, Kawata S, Minami S (1984) Estimation of spectral slit width and blind deconvolution of spectroscopic data by homomorphic filtering. Appl Opt 23(10):1601–1608
Sarkar SC, Dutta PK, Roy NC (1998) A blind-deconvolution approach for chromatographic and spectroscopic peak restoration. IEEE Trans Instrum Meas 47(4):941–947
Yuan J, Hu Z, Sun J (2005) High-order cumulant-based blind deconvolution of Raman spectra. Appl Opt 44(35):7595–7601
Yuan J (2009) Blind deconvolution of x-ray diffraction profiles by using highorder statistics. Opt Eng 48(7):076501–076505
Babichev S, Korobchynskyi M, Lahodynskyi O, Korchomnyi O, Basanets V, Borynskyi V (2018) Development of a technique for the reconstruction and validation of gene network models based on gene expression profiles. East-Eur J Enterp Technol 1(4–91):19–32
Morawski RZ, Miekina A, Barwicz A (1996) Combined use of Tikhonov deconvolution and curve fitting for spectrogram interpretation. Instrum Sci Technol 24(3):155–167
Miekina A, Morawski R, Barwicz A (1997) The use of deconvolution and iterative optimization for spectrogram interpretation. IEEE Trans Instrum Meas 46(4):1049–1053
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Olszewski, S. et al. (2020). Some Features of the Numerical Deconvolution of Mixed Molecular Spectra. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, vol 1020. Springer, Cham. https://doi.org/10.1007/978-3-030-26474-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-26474-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26473-4
Online ISBN: 978-3-030-26474-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)