Quality Assessment of Tindora (Coccinia indica) Using Poincare Plot and Cartesian Quadrant Analysis
Sarkar T. Mukherjee A. Chatterjee K. Akhmetova S.O. Alipbekova A.S. Temerbayeva M. Shariati M.A. Rebezov M. Lorenzo J.M.
September 2022Springer
Food Analytical Methods
2022#15Issue 92357 - 2371 pp.
Tindora or ivy gourd (Coccinia indica) is a tropical vegetable and has the potential to become popular worldwide due to its rich nutritional characteristics, adaptability to adverse environmental condition and fast fruiting nature. The fresh samples are bright green (good) in colour; it turns yellow (intermediate quality) and ultimately turns bright red (bad or inedible as vegetable). In this paper, tindora is classified using two different colour representations of the image matrix, namely RGB and HSV. In the RGB representation, intensity of the green layer versus intensity of the red layer for each pixel position is plotted. The shape of the plot enables us to identify the tindora quality. In the case of HSV representation, the hue layer is used for classification purposes. The hue layer matrix is vectorized and the Poincare plot is computed. The distribution of the Poincare plot is analysed using Cartesian quadrant system. Based on the analysis, tindora quality is determined. The accuracy of the tindora classification using RGB representation is 68.67 and HSV representation is 94.33. The classifier used in both the representations after feature extraction is primarily threshold based and hence has low computational burden. The low computational burden makes the method ideal for incorporation into smartphones in the form of an app.
Artificial intelligence , Food quality inspection , Image analysis , Vegetables
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Department of Food Processing Technology, Malda Polytechnic, West Bengal State Council of Technical Education, Government of West Bengal, Malda, 732102, India
Government College of Engineering and Ceramic Technology, Kolkata, India
Department of Chemistry, Chemical Engineering and Ecology, Almaty Technological University, 100 Tole Bi Str., Almaty, 050012, Kazakhstan
S. D. Asfendiyarov Kazakh National Medical University, 94 Tole Bi Str., Almaty, 050012, Kazakhstan
Faculty of Engineering and Technology, Innovative University of Eurasia, 45 Lomova Str., Pavlodar, 140000, Kazakhstan
Department of Scientific Research, K.G. Razumovsky Moscow State University of Technologies and Management (The First Cossack University), 73 Zemlyanoy Val, Moscow, 109004, Russian Federation
Department of Scientific Research, V. M. Gorbatov Federal Research Center for Food Systems, 26 Talalikhina Str., Moscow, 109316, Russian Federation
Centro Tecnológico de La Carne de Galicia, Avd. Galicia No. 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, Ourense, 32900, Spain
Área de Tecnología de los Alimentos, Facultad de Ciencias de Ourense, Universidade de Vigo, Ourense, 32004, Spain
Department of Food Processing Technology
Government College of Engineering and Ceramic Technology
Department of Chemistry
S. D. Asfendiyarov Kazakh National Medical University
Faculty of Engineering and Technology
Department of Scientific Research
Department of Scientific Research
Centro Tecnológico de La Carne de Galicia
Área de Tecnología de los Alimentos
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