Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5080
Title: Process neural network method: Case study I: Discrimination of sweet red peppers prepared by different methods
Authors: Ünlütürk, Sevcan
Ünlütürk, Mehmet S.
Pazır, Fikret
Kuşçu, Alper
Ünlütürk, Sevcan
Izmir Institute of Technology. Food Engineering
Keywords: Computer vision techniques
Process neural network
Red peppers
Neural networks
Crystal structure
Issue Date: 2011
Publisher: Springer Verlag
Source: Ünlütürk, S., Ünlütürk, M. S., Pazır, F.,and Kuşçu, A. (2011). Process neural network method: Case study I: Discrimination of sweet red peppers prepared by different methods. Eurasip Journal on Advances in Signal Processing, 2011. doi:10.1155/2011/290950
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.
URI: https://doi.org/10.1155/2011/290950
http://hdl.handle.net/11147/5080
ISSN: 1687-6172
Appears in Collections:Food Engineering / Gıda Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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