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

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.

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Correspondence to Mehmet S. Unluturk.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Unluturk, S., Unluturk, M.S., Pazir, F. et al. Process Neural Network Method: Case Study I: Discrimination of Sweet Red Peppers Prepared by Different Methods. EURASIP J. Adv. Signal Process. 2011, 290950 (2011). https://doi.org/10.1155/2011/290950

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Keywords

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