(38-4) 41 * << * >> * Russian * English * Content * All Issues

Substance identification by error deformed spectra
N.S. Vasil’ev, A.N. Morozov

 

Bauman Moscow State Technical University

PDF, 536 kB

Full text of article: Russian language.

DOI: 10.18287/0134-2452-2014-38-4-856-864

Pages: 856-864.

Abstract:
Substance identification by their luminescence spectra is a highly sensitive and non distraction method. If a signal level is low then recognition errors may occur. The aim of this work was to define the identification algorithm with error probability control. For this purpose, the value of dissimilarity measure in the form of Spectral Angle Mapper (SAM) was analyzed. The relation between errors in measured spectra and the dissimilarity measure distribution was defined. The accuracy of the statistical hypothesis was used in spectral library search. The resulting algorithm was tested on more than 4000 sample spectra. The case when the measured spectra contained a signal of unknown source was analyzed, as well as the case when the measured spectra might contain either a signal or be equal to noise.

Key words:
identification; dissimilarity measure; similarity index; match factor; database retrieval; luminescence; chemometrics; spectral library search; spectral angle mapper; SAM.

Citation:
Kruchinin AY. Industrial datamatrix barcode recognition with random tilt and rotate the camera. Computer Optics 2014; 38(4): 856-864. DOI: 10.18287/0134-2452-2014-38-4-865-870.

References:

  1. Guti´errez-Rodriguez, A.E. New dissimilarity measures for ultraviolet spectra identification / A.E. Guti´errez-Rodriguez, M.A. Medina-P´erez, J.F. Martinez-Trinidad [et al.] // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). – 2010. – V. 6256 – P. 220-229.
  2. Stephen, S.E. Optimization and testing of mass spectral library search algorithms for compound identification / S.E. Stein, D.R. Scott // Journal of the American Society for Mass Spectrometry. – 1994. – Vol. 5(9). – P. 859-866.
  3. Kruse, F.A. The spectral image processing system (SIPS) – interactive visualization and analysis of imaging spectrometer data / F.A. Kruse, A.B. Lefkoff, J.W. Boardman [et al.] // Remote Sensing of Environment. – 1993. – Vol. 44(2-3). – P. 145-163.
  4. Paclik, P. A study on design of object sorting algorithms in the industrial application using hyperspectral imaging / P. Paclik, R. Leitne, R.P.W. Duin // Journal of Real-Time Image Processing. – 2006. – Vol. 1(2). – P. 101-108.
  5. Bodis, L. A novel spectra similarity measure / L. Bodis, Al. Ross, E. Pretsch // Chemometrics and Intelligent Laboratory Systems. – 2007. – Vol. 85(1). – P. 1-8.
  6. Paclik, P. Dissimilarity-based classification of spectra: computational issues / P. Paclik, R.P.W. Duin // Real-Time Imaging. – 2003. – Vol. 9(4). – P. 237-244.
  7. Qun, G. Comparison of several chemometric methods of libraries and classifiers for the analysis of expired drugs based on Raman spectra / G. Qun, L. Yan, L. Hao [et al.] // Journal of Pharmaceutical and Biomedical Analysis. – 2014. – Vol. 94(0). – P. 58-64.
  8. Hartstra, J. How to approach substance identification in qualitative bioanalysis / J. Hartstra, J.P. Franke, R.A. Zeeuw  // Journal of Chromatography B: Biomedical Sciences and Applications. – 2000. – V. 739(1). – P. 125-137.
  9. Tan, N. Application of multiple statistical tests to enhance mass spectrometrybased biomarker discovery / N. Tan, W. Fisher, K. Rosenblatt, H. Garner // BMC Bioinformatics. – 2009. – Vol. 10(1). – P. 144.
  10. Fisher, R.A. Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population / R.A. Fisher// Biometrika. – 1915. – Vol. 10(4). – P. 507-521.
  11. Fisher, R.A. On the “probable error” of a coefficient of correlation deduced from a small sample / R.A. Fisher // Metron. – 1921. – Vol. 1. – P. 3-32.
  12. Kochikov, I.V. Numerical procedures for substances identification and concentration calculation in the open athmosphere by processing a single ftir measurement / I.V. Ko­chikov, A.N. Morozov, I.L. Fufurin // Computer Optics. – 2012. – Vol. 36(4). – P. 554-561. – ISSN 0134-2452.
  13. Glagolev, K.V. Technique for obtaining and processing spectral information with static fourier spectrometer / K.V. Glagolev, Ig.S. Golyak, Il.S. Golyak [et al.] // Optics and Spectroscopy. – 2011. – Vol. 110(3). – P. 449-455.
  14. High luminosity spectral instruments / V.A. Vagin, M.A. Ger­shun, G.N. Zhizhin, K.I. Tarasov. – Moscow: “Nauka” Publisher, 1988. – 332 p. – (In Russian).
  15. Basics of Fourier spectroradiometry / A.N. Morozov, S.I. Svetlichny. – Moscow: “Nauka” Publisher, 2014. – 456 p. – (In Russian).
  16. Golyak, Il.S. Sampling-free analysis of chemical compounds using a static Fourier-transform spectrometer / Il.S. Golyak, A.A. Esakov, N.S. Vasilev, A.N. Morozov // Optics and Spectroscopy. – 2013. – V. 115(6). – P. 884-888.
© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail:journal@computeroptics.ru; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20