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Methods of Measuring the Spectral Characteristics and Identifying the Components of Grain Mixtures in Real-Time Separation Systems

  • Optophysical Measurements
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Measurement Techniques Aims and scope

The recognition of the components of grain mixtures based on measurements of the reflection and luminescence spectra to increase the photoseparation efficiency is considered. Methods of measuring the spectra are presented, as well as the results of comparative investigations of the characteristics of the algorithms for recognizing the components of grain mixtures from their spectral characteristics.

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References

  1. S. Satake, T. Ito, and N. Ikeda, Pat. 5638961 US, “Cereal grain color sorting apparatus” (1997).

  2. S. Satake, T. Ito, and N. Ikeda, Pat. 5779058 US, “Color sorting apparatus for grains” (1998).

  3. I. N. Sarycheva et al., “Early diagnosis of tooth caries by laser-induced fluorescence,” Ross. Stomatol., No. 3, 50–56 (2012).

  4. I. N. Sarycheva et al., Patent No. 2464549 RF, “An optical fiber device for recording fluorescence,” Izobret. Polezn. Modeli, No. 29 (2012).

  5. E. A. Kirsanov and A. A. Sirota, Data Processing in Spatially Distributed Radio Monitoring Systems: Statistical and Neural Network Approaches [in Russian], Fizmatlit, Moscow (2012).

    Google Scholar 

  6. A. L. Tatuzov, Neural Networks in Radar Problems [in Russian], Radiotekhnika, Moscow (2009).

    Google Scholar 

  7. S. Osovskii, Neural Networks for Data Processing [in Russian], Finansy i Statistika, Moscow (2002).

    Google Scholar 

  8. I. I. Frolov and R. Kh. Sadykhov, “The construction of a classifier based on a support vector machine for recognizing symbols,” Dokl. Beloruss. Gos. Univ. Informat. Radioelektr. (2008), No. 1, pp. 103–108.

  9. B. Scholkopf et al., Advances in Kernel Methods. Support Vector Learning, MIT Press, Cambridge (USA) (1998).

    Google Scholar 

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This research was supported by the Ministry of Science of the Russian Federation under the program “Development of Cooperation between Russian Universities and Industrial Enterprises” (Government Resolution No. 218 dated April 9, 2010 – Phase 3, Grant No. 02.G25.31.0002).

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Authors and Affiliations

  1. Voronezh State University, Voronezh, Russia

    E. K. Algazinov, M. A. Dryuchenko, D. A. Minakov, A. A. Sirota & V. A. Shul’gin

Authors
  1. E. K. Algazinov
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  2. M. A. Dryuchenko
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  3. D. A. Minakov
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  4. A. A. Sirota
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  5. V. A. Shul’gin
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Corresponding author

Correspondence to M. A. Dryuchenko.

Additional information

Translated from Izmeritel’naya Tekhnika, No. 1, pp. 36–41, January, 2014.

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Algazinov, E.K., Dryuchenko, M.A., Minakov, D.A. et al. Methods of Measuring the Spectral Characteristics and Identifying the Components of Grain Mixtures in Real-Time Separation Systems. Meas Tech 57, 54–61 (2014). https://doi.org/10.1007/s11018-014-0406-3

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  • DOI: https://doi.org/10.1007/s11018-014-0406-3

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