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Open Access

Process Neural Network Method: Case Study I: Discrimination of Sweet Red Peppers Prepared by Different Methods

  • Sevcan Unluturk1,
  • Mehmet S. Unluturk2Email author,
  • Fikret Pazir3 and
  • Alper Kuscu4
EURASIP Journal on Advances in Signal Processing20112011:290950

https://doi.org/10.1155/2011/290950

Received: 2 November 2010

Accepted: 3 February 2011

Published: 27 February 2011

Abstract

This study utilized a feed-forward neural network model along with computer vision techniques to discriminate sweet red pepper products prepared by different methods such as freezing and pureeing. The differences among the fresh, frozen and pureed samples are investigated by studying their bio-crystallogram images. The dissimilarity in visually analyzed bio-crystallogram images are defined as the distribution of crystals on the circular glass underlay and the thin or the thick structure of crystal needles. However, the visual description and definition of bio-crystallogram images has major disadvantages. A methodology called process neural network (ProcNN) has been studied to overcome these shortcomings.

Keywords

Computer VisionNetwork ModelQuantum InformationNeural Network ModelMajor Disadvantage

Publisher note

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Authors’ Affiliations

(1)
Food Engineering Department, Izmir Institute of Technology, Izmir, Turkey
(2)
Department of Software Engineering, Izmir University of Economics, Izmir, Turkey
(3)
Food Engineering Department, Ege University, Izmir, Turkey
(4)
Faculty of Agriculture, Suleyman Demirel University, Isparta, Turkey

Copyright

© Sevcan Unluturk et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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