Volume 17, Issue 3 (2015)
JAST 2015, 17(3): 589-600 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Taghadomi-Saberi S, Omid M, Emam-Djomeh Z, Faraji-Mahyari K. Determination of Cherry Color Parameters during Ripening by Artificial Neural Network Assisted Image Processing Technique. JAST 2015; 17 (3) :589-600
URL: http://jast.modares.ac.ir/article-23-7465-en.html
URL: http://jast.modares.ac.ir/article-23-7465-en.html
1- Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Islamic Republic of Iran.
2- Department of Food Sciences and Technology, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Islamic Republic of Iran.
3- Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
2- Department of Food Sciences and Technology, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Islamic Republic of Iran.
3- Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
Abstract: (6234 Views)
Among the different classes of physical properties of foods, color is considered the most important visual attribute in quality perception. Consumers tend to associate color with quality due to its good correlation with physical, chemical and sensorial evaluations of food quality. This study used an inexpensive method to predict sweet cherries color parameters by combining image processing and artificial neural network (ANN) techniques. The color measuring technique consisted of a CCD camera for image acquisition, MATLAB software for image analysis, and ANN for modeling. To demonstrate the usefulness of this technique, changes of cherry color during ripening were studied. After designing, training, and generalizing several ANNs using Levenberg-Marquardt algorithm, a network with 7-14-11-3 architecture showed the best correlation (R2= 0.9999) for L*, a* and b* values from Chroma meter and the machine vision system. L* and b* parameters decreased during ripening of cherries and a* parameter increased at first and then decreased. Evaluation of L*, a* and b* values showed the possibility of reliable use of this system for determination of absolute color values of foodstuffs with a much lower cost in comparison with Chroma meter.
Keywords: Cherry fruit, Color parameters L*a*b*, Artificial Neural Network, Modeling, Image processing
Article Type: Research Paper |
Subject:
Food Science and Technology
Received: 2013/12/7 | Accepted: 2014/07/16 | Published: 2015/05/1
Received: 2013/12/7 | Accepted: 2014/07/16 | Published: 2015/05/1
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |