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Measurement Techniques

, Volume 61, Issue 12, pp 1187–1195 | Cite as

Compound Methods of Spectral Analysis of Nonuniform Flow of Grain Mixtures

  • E. K. Algazinov
  • A. O. DonskikhEmail author
  • D. A. Minakov
  • A. A. Sirota
Article
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Problems involved in the classification of elements of grain mixtures and the determination of their varietal membership for the purpose of identifying grains infected with fusarium wilt are considered. The problems are solved based on a combination of spectral measurements conducted by different optical methods in different ranges of wavelengths. A system of measuring compound reflection and transmission spectra in the course of processing a nonuniform flow of elements of grain mixtures is described. An analysis of spectral measurements with the use of neural network classification for classification and dimension-reducing algorithms is performed. Dependences of the estimates of the classification errors on the number of features determined with a reduction in the dimensions of the initial measurements are presented. Estimates of the errors in a classification of the elements of the grain mixtures for different combinations of optical methods of spectral analysis are obtained.

Keywords

spectral analysis elements of grain mixtures rapid analysis classification of images 

References

  1. 1.
    M. Huang, Q. G. Wang, Q. B. Zhu, et al., “Review of seed quality and safety tests using optical sensing technologies,” Seed Sci. & Technol., 43, 337–366 (2015).CrossRefGoogle Scholar
  2. 2.
    S. R. Delwiche, Chen Yud-Ren, and W. R. Hruschka, “Differentiation of hard red wheat by near-infrared analysis of bulk samples,” Cereal Chem., 72, No. 3, 243–247 (1995).Google Scholar
  3. 3.
    D. Wu, L. Feng, Y. He, and Y. Bao, “Variety identification of Chinese cabbage seeds using visible and near-infrared spectroscopy,” Trans. ASABE, No. 51, 2193–2199 (2008).Google Scholar
  4. 4.
    D. Giacomo and D. Stefania, “A multivariate regression specimen for detection of fumonisins content in maize from near infrared spectra,” Food Chem., No. 141, 4389–4294 (2013).Google Scholar
  5. 5.
    P. Sirisomboon, Y. Hashimoto, and M. Tanaka, “Study on non-destructive evaluation methods for defect pods for green soybean processing by near-infrared spectroscopy,” J. Food Eng. No. 93, 502–512 (2009).Google Scholar
  6. 6.
    E. K. Algazimov, M. A. Dryuchenko, D. A. Minakov, et al., “Methods for the measurement of the spectral characteristics and identification of elements of grain mixtures in real-time separation systems,” Izmer. Tekhn., No. 1, 36–41 (2014).Google Scholar
  7. 7.
    A. O. Donskikh, D. A. Minakov, A. A. Sirota, and V. A. Shulgin, “Methods of analysis and classification of the components of grain mixtures based on measuring the reflection and transmission spectra,” Sci. Study & Res.: Chem. & Chem. Eng., Biotechn., Food Industry, No. 18 (3), 291–302 (2017).Google Scholar
  8. 8.
    E. M. Babishov, V. A. Gol’dfarb, D. A. Minakov, et al., Patent 2489215 RF, IPC B07C 99/00, “A laser sorter,” Izobret. Polezn. Modeli, No. 22 (2013).Google Scholar
  9. 9.
    V. A. Shul’gin, E. M. Babishov, V. A. Gol’dfarb, et al., Patent 2521215 RF, IPC B07C 5/34, “A fiber-optic laser sorter,” Izobret. Polezn. Modeli, No. 18 (2014).Google Scholar
  10. 10.
    S. Mahesh, A. Manickavasagan, D. S. Jayas, et al., “Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes,” Biosyst. Eng., No. 101, 50–57 (2008).Google Scholar
  11. 11.
    X. Yang, H. Hong, Z. You, and F. Cheng, “Spectral and image integrated analysis of hyperspectral data for waxy corn seed variety classification,” Sensors, No. 15 (7), 15578–15594 (2015).Google Scholar
  12. 12.
    W. Kong, C. Zhang, F. Liu, et al., “Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis,” Sensors, No. 13 (7), 8916–8927 (2013).Google Scholar
  13. 13.
    E. Bauriegel, A. Giebel, and W. B. Herppich, “Hyperspectral and chlorophyll fluorescence imaging to analyse the impact of fusarium culmorum on the photosynthetic integrity of infected wheat ears,” Sensors, No. 11 (4), 3765–3779 (2011).Google Scholar
  14. 14.
    H. B. Yao, Z. Hruska, R. Kincaid, et al., “Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imaging,” Biosyst. Eng., No. 115, 125–135 (2013).Google Scholar
  15. 15.
    GOST 31646–2012, Seed Grains. A Method of Determining the Content of Fusiorum Seeds.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • E. K. Algazinov
    • 1
  • A. O. Donskikh
    • 1
    Email author
  • D. A. Minakov
    • 1
  • A. A. Sirota
    • 1
  1. 1.Voronezh State UniversityVoronezhRussia

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