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Discrimination of Rice Based on Alkali Spreading Value (ASV) by Machine Vision Technique

  • Amitava Akuli
  • Anil Kumar BagEmail author
  • Arindam Sarkar
  • Abhra Pal
  • Sabyasachi Majumdar
  • Tamal Dey
  • Gopinath Bej
  • Srimoyee Chaudhury
  • Nabarun Bhattacharyya
Conference paper
  • 81 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)

Abstract

Physical and biochemical attributes are commonly used for characterization of rice. The physical attributes are related to the quantification of size, shape, colour and texture of the rice grains. Biochemical attributes are assessed from cooking and eating characteristics of rice and are termed like alkali spreading value (ASV), amylose content (AC), gel consistency (GC), grain elongation etc. Estimation of biochemical attributes are often time consuming and require meticulous effort for sample preparation, storage and manual measurement. The gelatinization temperature (GT) is related to Alkali spreading value of rice and is partly associated with the amylose content of the starch. GT has a negative correlation with cooking temperature of rice. In this paper image analysis technique has been proposed for discrimination of rice. A portable flat bed scanner has been used as the imaging device and image analysis software has been developed to measure the rate of dispersion during ASV testing. This machine vision technique is a faster and effective way to determine the ASV. The results obtained are promising towards this new approach for objective estimation of ASV.

Keywords

Alkali spreading value Gelatinization temperature Spreading index Digital image analysis 

Notes

Acknowledgments

The authors would like to thank Dr. (Mrs.) Monika Joshi, Scientist, IARI, PUSA for her valuable guidance and IARI, PUSA, New Delhi from where the sample were collected for this study. The authors are also grateful to the Department of Science and Technology, DST, Govt. of India for supporting the project.

References

  1. 1.
    Du, C.J., Sun, D.W.: Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci. Technol. 15, 230–249 (2004)CrossRefGoogle Scholar
  2. 2.
    Little, R.R., Hilder, G.B., Dawson, E.H.: Differential effect of dilute alkali on 25 varieties of milled white rice. Cereal Chem. 35, 111–126 (1958)Google Scholar
  3. 3.
    Bhattacharya, K.R., Sowbhagya, C.M.: An improved alkali reaction test for rice quality. J. Food Technol. 7, 323–331 (1972)CrossRefGoogle Scholar
  4. 4.
    Bhattacharya, K.R., Sowbhagya, C.M., Indudhara Swamy, Y.M.: Quality profiles of rice: a tentative scheme for classification. J. Food Sci. 47, 564–569 (1982)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Amitava Akuli
    • 1
  • Anil Kumar Bag
    • 2
    Email author
  • Arindam Sarkar
    • 2
  • Abhra Pal
    • 1
  • Sabyasachi Majumdar
    • 1
  • Tamal Dey
    • 1
  • Gopinath Bej
    • 1
  • Srimoyee Chaudhury
    • 2
  • Nabarun Bhattacharyya
    • 1
  1. 1.Centre for Development of Advanced Computing (C-DAC)KolkataIndia
  2. 2.Department of Applied Electronics and Instrumentation EngineeringHaritage Institute of TechnologyKolkataIndia

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