Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5146
Title: The discrimination of raw and UHT milk samples contaminated with penicillin G and ampicillin using image processing neural network and biocrystallization methods
Authors: Ünlütürk, Sevcan
Pelvan, Merve
Ünlütürk, Mehmet S.
Ünlütürk, Sevcan
Pelvan, Merve
Izmir Institute of Technology. Food Engineering
Keywords: Ampicillin
Antibiotic residues
Biocrystallization
Food analysis
Veterinary residues
Neural networks
Penicillin G
Issue Date: Nov-2013
Publisher: Academic Press Inc.
Source: Ünlütürk, S., Pelvan, M., and Ünlütürk, M.S. (2013). The discrimination of raw and UHT milk samples contaminated with penicillin G and ampicillin using image processing neural network and biocrystallization methods. Journal of Food Composition and Analysis, 32(1), 12-19. doi:10.1016/j.jfca.2013.06.007
Abstract: This paper utilized a neural network for texture image analysis to differentiate between milk, either raw or ultra high temperature (UHT) with antibiotic residues (e.g., penicillin G and ampicillin) and milk without antibiotic residues. The biocrystallization method was applied to obtain biocrystallogram images for milk samples spiked with penicillin G and ampicillin at different concentration levels. The biocrystallogram images were used as an input for a designed neural network called the image processing neural network (ImgProcNN). The visual differences in these images that were based on textural properties, including the distribution of crystals on the circular grass underlay, the thin or thick structure of the crystal needles, and the angles between the branches and the side needles, were used to discriminate the antibiotic-free milk samples from samples with antibiotic residues. The visual description and definition of these images have major disadvantages. In this study, the ImgProcNN was developed to overcome the shortcomings of these visual descriptions and definitions. Overall, the neural network achieved an average recognition performance between 86% and 100%. This high level of recognition suggests that the neural network used in this paper has potential as a method for discriminating raw and UHT milk samples contaminated with different antibiotics.
URI: https://doi.org/10.1016/j.jfca.2013.06.007
http://hdl.handle.net/11147/5146
ISSN: 0889-1575
0889-1575
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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