Predicting Some Quality Properties of Different White Rice Varieties by Image Processing Technology

Document Type : Research Article

Authors

1 condedat Ph.D university of mohaghegh ardabili.

2 Associate Professor, Department of Biosystems Engineering, University of Mohaghegh Ardabili,

3 University of Mohaghegh Ardabili

Abstract

Rice is one of the most common and most consumed foods in the world. Quality properties are among the most important factors in determining the cooking and processing characteristics of rice. One of the major problems in the food industry is predicting quality properties. Therefore, in this study, it is possible to predict amylose content (AC), gelatinization temperature (GT), gel consistency (GC), protein content (PC), minimum viscosity (MV), peak viscosity (PV), final viscosity (FV), breakdown viscosity (BDV) and setback viscosity (SBV) of 100 single grain rice samples of Hashemi, Khazar and Dorefak using image processing technology in three exposure treatments including high exposure with LED lamp, high exposure with LED lamp and fluorescent and Anti-light exposure. Calibration models were developed by multivariate linear least squares (PLS) regression. Calibration coefficients of calibration coefficients of variables AC, GT, GC, PC, MV, PV, FV, BDV and SBV in Hashemi, Khazar and Darfak varieties for all treatments were R2cal ≥ 0.89, R2cal ≥ 0.95, and R2cal ≥ 0.92 respectively. prediction coefficients were obtained with R2pre ≥ 0.88, R2pre ≥ 0.94 and R2pre ≥ 0.90, respectively. The results of PLS regression showed that the variables derived from the shape and size characteristics and color variables R,, G, B, L, a and b of image processing were able to predict the rice quality parameters with good accuracy. As a result, using low-cost, non-destructive image processing technology can predict some of the qualitative properties of rice.

Graphical Abstract

Predicting Some Quality Properties of Different White Rice Varieties by Image Processing Technology

Highlights

  • All existing methods for measuring the rice quality indexes are labor-expensive or time-consuming.
  • Predicting some of the rice quality properties is done low-cost and non-destructive via image processing technology.
  • Area and area covered are the most influential variables for predicting qualitye properties of rice.
  • The best treatment for predicting quality properties is lighting by the treatment LED and fluorescence lamps with a prediction accuracy of above 0.94.

Keywords

Main Subjects

References

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Innovative Food Technologies
Volume 8, Issue 1
November 2020
Pages 1-11

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History

  • Receive Date: 16 December 2019
  • Revise Date: 09 February 2020
  • Accept Date: 08 March 2020

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