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Title: Quality evaluation of Alaska pollock (Theragra chalcogramma) roe by image analysis. Part I: Weight prediction
Authors: Balaban, Murat Ömer
Chombeau, Melanie
Gümüş, Bahar
Cırban, Dilşat
Keywords: Quality control
Weight classification
Pollock roe
Image processing
Issue Date: Jan-2012
Publisher: Taylor and Francis Ltd.
Source: Balaban, M. Ö., Chombeau, M., Gümüş, B., and Cırban, D. (2012). Quality evaluation of Alaska pollock (Theragra chalcogramma) roe by image analysis. Part I: Weight prediction. Journal of Aquatic Food Product Technology, 21(1), 59-71. doi:10.1080/10498850.2011.583377
Abstract: Roe is an important product of the Alaska pollock (Theragra chalcogramma) industry. About 31% of the value for all pollock products comes from roe, yet roe is 5% of the weight of the fish. Currently, the size (weight), color, and maturity of the roe are subjectively evaluated. The objective of this study was to develop methods to predict the weight of Alaska pollock roe based on its view area from a camera and to differentiate between single and double roes. One hundred and forty-two pollock roes were picked from a processing line in a Kodiak, AK plant. Each roe was weighed, placed in a light box equipped with a digital video camera, images were taken at two different angles from one side, then turned over and presented at two different angles again (four images for each roe). A reference square of known surface area was placed by the roe. The following equations were used to fit the view area (X) versus weight (Y) data: linear, power, and second-order polynomial. Error rates for the classification of roes by weight decreased significantly when weight prediction equations for single and double roes were developed separately. A turn angle method, a box method, and a modified box method were tested to differentiate single and double roes by image analysis. Machine vision can accurately determine the weight of pollock roe. Practical Application Abstract: An image analysis method to accurately determine if pollock roe is a single or a double was developed. Then view area versus weight correlations were found for single and double roes that reduced incorrect weight classification rates to half that of human graders. © 2012 Copyright Taylor and Francis Group, LLC.
ISSN: 1049-8850
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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