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Discriminating Drying Method of Tarhana Using Computer Vision
Author(s) -
Kurtulmuş F.,
Gürbüz O.,
Değirmencioğlu N.
Publication year - 2014
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/jfpe.12092
Subject(s) - artificial intelligence , food science , computer science , pattern recognition (psychology) , mathematics , chemistry
Tarhana is a traditionally fermented wheat flour product of T urkey which has high nutritional value. A rapid and objective evaluation of tarhana quality by assessing the used drying method is important for producers and packaging companies. A computer vision method was developed to discriminate between drying methods of tarhana. Tarhana samples were prepared with three drying methods: sun dried, oven dried and microwave dried. An image acquisition station was constituted under artificial illumination. Different types of machine learning methods and feature selection methods were tested to find an effective system for the discrimination between drying methods of tarhana using visual texture features with different color components. Experimental results showed that the best accuracy rate (99.5%) was achieved with a K ‐nearest‐neighbors classifier through the feature model based on stepwise discriminant analysis. Practical Applications The computer vision system proposed in this study can be used as an inspection tool for discrimination of drying method of tarhana by producers and packaging companies.