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Öğe DECONTAMINATION EFFECT OF ELECTROLYZED WATER WASHING ON FRUITS AND VEGETABLES(Slovak Univ Agriculture Nitra, 2018) Turantas, Fulya; Ersus-Bilek, Seda; Somek, Ozgul; Kuscu, AlperThe use of electrolyzed water in the washing of fruits and vegetables is a promising alternative treatment to chlorine washing. Electrolyzed water washing, is safer, healthier, reduces cleaning times, and is ready to handle. In recent years, food poisoning outbreaks which are caused by bacteria with acid tolerance response in fruits and vegetables has increased. In addition, pathogen produce cases and outbreaks linked to fresh fruits and vegetables, such as cantaloupes, strawberries, fruit salads, spinach, lettuce, celery, and tomatoes has been encountered. Nowadays, the necessity of effective and healthy decontamination processes has gained more importance. The aim of this review is to offer a complete view about electrolyzed water, its classifications and applications. Decontamination results of extant literature of electrolyzed water were also presented. Also, the effects and results of electrolyzed water decontamination on the microbial counts of fresh fruits and vegetables compared with different sanitizing agents have been summarized.Öğe Discrimination of bio-crystallogram images using neural networks(Springer, 2014) Unluturk, Sevcan; Unluturk, Mehmet S.; Pazir, Fikret; Kuscu, AlperThis study utilized a unique neural network model for texture image analysis to differentiate the crystallograms from pairs of fresh red pepper fruits from conventional and organic farms. The differences in visually analyzed samples are defined as the distribution of crystals on the circular glass underlay, the thin or thick structure of crystal needles, the angles between branches and side needles, etc. However, the visual description and definition of bio-crystallogram images has major disadvantages. A novel methodology called an image neural network (INN) has been developed to overcome these shortcomings. The 1,488 x 2,240 pixel bio-crystallogram images were acquired in a lab and cropped to 425 x 1,025 pixel images. These depicted either a conventional sweet red pepper or an organic sweet red pepper. A set of 19 images was utilized to train the image neural network. A new set of 4 images was then prepared to test the INN performance. Overall, the INN achieved an average recognition performance of 100 %. This high level of recognition suggests that the INN is a promising method for the discrimination of bio-crystallogram images. In addition, Hinton diagrams were utilized to display the optimality of the INN weights.Öğe Process Neural Network Method: Case Study I: Discrimination of Sweet Red Peppers Prepared by Different Methods(Hindawi Publishing Corporation, 2011) Unluturk, Sevcan; Unluturk, Mehmet S.; Pazir, Fikret; Kuscu, AlperThis 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.