Classification and Quality Analysis of Rice Grain Based on Dimensional Measurement During Hydrothermal Treatment

Classification and Quality Analysis of Rice Grain Based on Dimensional Measurement During Hydrothermal Treatment

© 2021 by IJETT Journal
Volume-69 Issue-8
Year of Publication : 2021
Authors : Suman Kumar Bhattacharyya, Sagarika Pal, Subrata Chattopadhyay
DOI :  10.14445/22315381/IJETT-V69I8P218

How to Cite?

Suman Kumar Bhattacharyya, Sagarika Pal, Subrata Chattopadhyay, "Classification and Quality Analysis of Rice Grain Based on Dimensional Measurement During Hydrothermal Treatment," International Journal of Engineering Trends and Technology, vol. 69, no. 8, pp. 145-154, 2021. Crossref,

The shape and dimensional appearance of rice kernels have a significant role in their classification. In this research, various dimensional parameters of rice grain have been measured and analyzed for their gradation using the Decision Tree Learning model, and also the grain samples have been treated through a typical Hydrothermal method for determining the characteristics related to its quality. Image processing techniques have been used for such measurement, and eight Indian rice varieties with low to high amylose content (15%–28% d.b.) have been chosen for the experimentation. Dimensional changes of rice kernel during Hydrothermal treatment have been modeled mathematically, and from the model equation, a new parameter termed the average logistic growth rate (KAvg ) has been obtained. It has been observed that the average logistic growth rate of the rice kernel is negatively correlated with its amylose content (Correlation coefficient -0.9618) and cooking time for food preparation (Correlation coefficient -0.9499), indicating a strong relationship with rice quality. All the experimental observations finally confirm that the combined Decision Tree Learning model and the mathematical model of rice grain growth during Hydrothermal treatment for grain quality analysis is a novel idea for getting a more precise classification of rice.

Classification by Decision Tree Learning model, Dimensional changes of rice, Hydrothermal treatment, Logistic growth rate, Mathematical model.

[1] B. Lurstwut and C. Pornpanomchai, Image analysis based on color, shape, and texture for rice seed (Oryza sativa L.) germination evaluation, Agriculture and Natural Resources. 51 (2017) 383-389.
[2] M. Chetima and P. Payeur, Automated Tuning of a Vision-based Inspection System for Industrial Food Manufacturing, In Proceeding of the IEEE International Instrumentation and Measurement Technology Conference (I2MTC’2012) (2012) 210- 215.
[3] H. Gujjarand M. and Siddappa, A Method for Identifying of Basmati Rice Grain of India and its Quality using Pattern Classification, International Journal Engineering Research, and Application. 3 (2013) 268-273.
[4] J. S. Aulakh and V. K. Banga, Grading of Rice Grains by Image Processing, International Journal of Engineering Research & Technology (IJERT). 1(4) (2012) 1-4.
[5] G. Ajay, M. Suneel, K. Kumar and P. Prasad, Quality Evaluation of Rice Grains using Morphological Methods, International Journal Soft Computer Engineering. 2 (2013) 35-37.
[6] S. P. Shouche, R. Rastogi, S. G. Bhagwat and J. K. Sainis, Shape analysis of grains of Indian wheat varieties, Computer and Electronics in Agriculture. 33 (2001) 55-76.
[7] B. Muangmeesri, A. Theddee, T. Patanasakpinyo, and D. Maneetham, Development of Classification System of Rice Milling Machine Using IOT Control, International Journal of Engineering Trends and Technology (IJETT). 69(1) (2021) 166- 171.
[8] N. A. Kuchekar and V. Yerigeri, Rice Grain Quality Grading Using Digital Image Processing Techniques, Journal of Electronics and Communication Engineering (IOSR-JECE). 13(3) (2018) 84- 88.
[9] V. Patil, and V. Malemath, Quality Analysis and Grading of Rice Grain Images, International Journal of Innovative Research in Computer and Communication Engineering. 3 (2015) 5672-5678.
[10] B. Yadav and V. Jindal, Modelling Changes in Milled Rice (Oryza sativa L.) Kernel During Soaking by Image Analysis, Journal of Food Engineering. 80 (2007) 359-369.
[11] B. Yadav and V. Jindal, Dimensional Changes in Milled Rice (Oryza sativa L.) Kernel During Cooking in Relation to Its Physicochemical Properties by Image Analysis, Science Direct Journal of Food Engineering. 81 (2007) 710-720.
[12] S. K. Bhattacharyya and S. Pal, Measurement of Parboiled and Non-parboiled Rice Grain Dimension during Hydro-Thermal Treatment Using Image Processing, IEEE-National Conference on Emerging Trends on Sustainable Technology and Engineering Applications (IEEE-NCETSTEA 2020) (Feb 2020) 1-5.
[13] P. Chavan, S. R. Sharma, T.C. Mittal, G. Mahajan, and S. K. Gupta, Optimization of Parboiling parameters to Improve the Quality Characteristics of Pusa Basmati 1509, Journal of Food Processing Engineering. 40(3) (2016) 1-12.
[14] E. Taghinezhad, M. H. Minaei, T. Suzuki and T. Brenner, Relationship Between Degree of Starch Gelatinization and Quality Attributes of Parboiled Rice During Steaming, Rice Science. 23(6) (2016) 339-344.
[15] S. M. Villota, A. M. Tuates and O. A. Caparnio, Cooking Qualities and Nutritional Contents of Parboiled Milled Rice, Asian Journal of Applied Science. 4(5) (2016) 1172-1178.
[16] E. Parveen, A. Alam, and H. Shakir, Assessment of Quality of Rice Grain using Optical and Image Processing Technique, International Conference on Communication Computing and Digital System (IEEE), (2017) 265-270.
[17] V. Amogha, Y. H. Shinde, A. B. Pandit, and J. B. Joshi, Image analysis-based validation and kinetic parameter estimation of rice cooking, Journal of Food Processing Engineering. 40(5) (2017) 1- 11.
[18] M. Bello, R. Baeza, and M. Tolaba, Quality Characteristics of Milled and Cooked Rice Affected by Hydrothermal Treatment, Journal of Food Engineering. 72 (2004) 124-133.
[19] T. Pan, L. Lin, L. Zhang, C. Zhang, Q. Liu, and C. Wei, Change in kernel properties, in situ gelatinization, and physicochemical properties of waxy rice with inhibition of starch branching enzyme during cooking, International Journal of Food Science and Technology. 54 (2019) 1-12.
[20] N. Danbaba, J. C. Anounye, A.S. Gana, M.E. Abo, and M. N. Ukwungwu, Grain quality characteristics of Ofada rice (Oryza sativa L.): Cooking and eating quality, International Food Research Journal. 18 (2011) 629-634.
[21] K. R. Singh and S. Chaudhury, A cascade network for the classification of rice grain based on single rice kernel, Complex & Intelligent System. 6 (2020) 321-334.
[22] A. H. Bhensjaliya and H. D. Vasava., Survey on Classification of Rice Grains Using Neural Network, International Journal of Computer Science and Engineering. 7(2) (2019) 828-831.
[23] M. H. Nayeem, M. Newaz, A. K. Chakraborty, and M. A. Z. Mamun, Application of Image Processing for Plant Disease Identification Based on SVM Technique, International Journal of Engineering Trends and Technology (IJETT). 68(1) (2020) 83-90.
[24] A. Jain, S. M. Rao, S. Sethi, A Ramesh, S. Tiwari, S K. Mandal, N. K. Singh, A. Singhal, N. Modi, V. Bansal, and C. Kalaichelvani, Effect of cooking on amylose content of rice, European Journal of Experimental Biology. 2(2) (2012) 385-388.
[25] M. Peleg, A Research notes- An Empirical Model for the Description of Moisture Sorption Curves, Journal of food science. 53(4) (1988) 1216-1219.
[26] L. Bel, D. Allard, J. M. Laurent, R. Cheddadi and A. Bar-Hen, CART algorithm for spatial data: Application to environmental and ecological data, Computational Statistics and Data Analysis. 53 (2009) 3082-3093.
[27] H. Sharma and S. Kumar, A Survey on Decision Tree Algorithm of Classification in Data Mining, International Journal of Science and Research (IJSR). 5(4) (2016) 2094-2097.
[28] P. Kapoor and R. Rani, A Survey of Classification Methods Utilizing Decision Trees, International Journal of Engineering Trends and Technology (IJETT). 22(4) (2015) 188-194.
[29] T. Kvalseth, Note on the R2 Measure of Goodness of Fit for Nonlinear Models, Bulletin of Psychonomic Society. 21(1) (1983) 79-80.
[30] N. Gogtay, S. P. Deshpande and U. M. Thatte, Principles of Regression analysis, Journal of the Association of Physician of India. 65 (2017) 48-52.