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  • Research Article
  • Open Access

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

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  • 2Email author,
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EURASIP Journal on Advances in Signal Processing20112011:290950

  • Received: 2 November 2010
  • Accepted: 3 February 2011
  • Published:


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.


  • Computer Vision
  • Network Model
  • Quantum Information
  • Neural Network Model
  • Major Disadvantage

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Authors’ Affiliations

Food Engineering Department, Izmir Institute of Technology, 35430 Izmir, Turkey
Department of Software Engineering, Izmir University of Economics, Sakarya Caddesi No. 156 Balcova, 35330 Izmir, Turkey
Food Engineering Department, Ege University, 35040 Izmir, Turkey
Faculty of Agriculture, Suleyman Demirel University, 32260 Isparta, Turkey


© 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.