tailieunhanh - Báo cáo hóa học: " Research Article Process Neural Network Method: Case Study I: Discrimination of Sweet Red Peppers Prepared by Different Methods"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Process Neural Network Method: Case Study I: Discrimination of Sweet Red Peppers Prepared by Different Methods | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 290950 8 pages doi 2011 290950 Research Article Process Neural Network Method Case Study I Discrimination of Sweet Red Peppers Prepared by Different Methods Sevcan Unluturk 1 Mehmet S. Unluturk 2 Fikret Pazir 3 and Alper Kuscu4 1 Food Engineering Department Izmir Institute of Technology 35430 Izmir Turkey 2 Department of Software Engineering Izmir University of Economics Sakarya Caddesi No. 156 Balcova 35330 Izmir Turkey 3Food Engineering Department Ege University 35040 Izmir Turkey 4 Faculty of Agriculture Suleyman Demirel University 32260 Isparta Turkey Correspondence should be addressed to Mehmet S. Unluturk Received 2 November 2010 Accepted 3 February 2011 Academic Editor Enrico Capobianco Copyright 2011 Sevcan Unluturk et al. 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. 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. 1. Introduction Excluding analytical chemistry approaches for the evaluation of food quality there are numerous alternative methods holistic methods that do not focus on the analysis of single .