Food Engineering / Gıda Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/12

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Subject: search.filters.subject.Pollock roeWoS Q: search.filters.wosQuartile.Q4

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  • Article
    Citation - WoS: 6
    Citation - Scopus: 7
    Quality Evaluation of Alaska Pollock (theragra Chalcogramma) Roe by Image Analysis. Part Ii: Color Defects and Length Evaluation
    (Taylor and Francis Ltd., 2012) Balaban, Murat Ömer; Chombeau, Melanie; Gümüş, Bahar; Cırban, Dilşat
    In the second part of the study of the quality evaluation of pollock roe by image analysis, methods to quantify the color defects (green spots, dark strips, dark color, and uneven coloring due to freezer burn) were developed. Dark roes can be detected by their average L* value. Dark strips can be detected by quantifying the percentage of pixels that have an L* value below an L * threshold. Since there is wide variation among the average colors of the roes, this L * threshold value must be auto-adjusting to the color of the individual roe. Green spots can be detected by their darker color and by ignoring red blood vessels by setting an upper a * threshold. In this study, identifying pixels with L* values less than the L * threshold = 66% of the L * average of the roe, and a* values less than an a * threshold = 20 successfully detected dark strips and green spots. Detection and quantification of uneven color and freezer burn required a smoothing of the roe colors to reduce details. The color primitives method was used, with a setting of a color threshold (CT) = 75. The resulting images were analyzed by setting L * threshold values of 60, 65, 70, 75, 80, and 85% of L * average of individual roes. More surface area of the roe was judged as defective with increasing L * threshold. With proper selection of L * threshold, a * threshold, and CT value, image analysis can accurately quantify the color defects of pollock roe. Practical Application Abstract: Automation of pollock roe sorting by color would streamline the operation, reduce error rates, and help with standardization of quality. Combined with other capabilities of machine vision such as sorting by weight, this technology can be used for multiple purposes simultaneously. © 2012 Copyright Taylor and Francis Group, LLC.
  • Article
    Citation - WoS: 12
    Citation - Scopus: 19
    Quality Evaluation of Alaska Pollock (theragra Chalcogramma) Roe by Image Analysis. Part I: Weight Prediction
    (Taylor and Francis Ltd., 2012) Balaban, Murat Ömer; Chombeau, Melanie; Gümüş, Bahar; Cırban, Dilşat
    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.