Journal of Information Technology & Software Engineering
Open Access
ISSN: 2165- 7866
ISSN: 2165- 7866
Firoozeh Abdollahi
Canada
Research Article
Capillary Dynamolysis Image Discrimination Using Neural Networks
Author(s): Mehmet S. Unluturk, Sevcan Unluturk, Fikret Pazir and Firoozeh AbdollahiMehmet S. Unluturk, Sevcan Unluturk, Fikret Pazir and Firoozeh Abdollahi
Quality differences between organic and conventional fresh tomatoes (unprocessed) and frozen tomatoes (processed) are evaluated by using a capillary rising picture method (capillary dynamolysis). The best pictures showing the differences most sharply between organic and conventional samples were prepared with 0.25-0.75% silver nitrate, 0.25-0.75% iron sulphate and 30-100% sample concentration. But visual description and analysis of these images is a major challenge. Therefore, a novel methodology called Gram-Charlier Neural Network methodology (GCNN) has been studied to classify these images. Two separate GCNNs have been created for fresh and frozen cases. They are trained with the pictures of organic and conventional tomato samples from these two cases. The 2048 x 1536 pixel chromatogram images were acquired in a lab and cropped to 1400 x 900 pixel images depicting either a conventio.. Read More»
DOI:
10.4172/2165-7866.1000101
