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1.
J Food Sci ; 76(3): E291-7, 2011 Apr.
Article in English | MEDLINE | ID: mdl-21535829

ABSTRACT

UNLABELLED: An image analysis method was developed to quantify the gaping, bruising, and blood spots of red salmon (Oncorhynchus nerka) fillets. Images of 15 fillets with various levels of gaping were taken either with a dSLR camera, or with a video camera. Also, the same fillets were recorded using the same camera both under regular illumination, and under polarized illumination. In either case, light at an angle was used to highlight the gapes in the flesh. An image analysis method was developed that can adaptively apply an L*threshold value to the image depending on the average color of the fillet, and quantify the resulting percent of the fillet area that has an L* value less than or equal to L*threshold. Polarized lighting changed the color by eliminating artifacts resulting from reflections. It is recommended to use polarized light for this purpose. Both cameras could be used adequately to quantify defects. The proper setting of the L*threshold value depended on the camera and on the polarized light. No correlation could be found between the average L* value of the fillets and the L*threshold value. It was possible to quantify the gaping, bruising, and blood spots on the salmon fillets using this method, which can be the first step toward the automation of this operation. PRACTICAL APPLICATION: Gaping, bruising, and blood spots can be recognized and quantified by analyzing images of salmon fillets. Polarized light is recommended to eliminate color artifacts caused by reflected light. This can be used to automate the detection of these defects.


Subject(s)
Food Analysis/methods , Salmon , Seafood/analysis , Animals , Artifacts , Color , Image Processing, Computer-Assisted , Light , Photography , Quality Control , Seafood/classification , Seafood/economics , Video Recording
2.
J Food Sci ; 75(3): E157-62, 2010 Apr.
Article in English | MEDLINE | ID: mdl-20492289

ABSTRACT

After harvesting, salmon is sorted by species, size, and quality. This is generally manually done by operators. Automation would bring repeatability, objectivity, and record-keeping capabilities to these tasks. Machine vision (MV) and image analysis have been used in sorting many agricultural products. Four salmon species were tested: pink (Oncorhynchus gorbuscha), red (Oncorhynchus nerka), silver (Oncorhynchus kisutch), and chum (Oncorhynchus keta). A total of 60 whole fish from each species were first weighed, then placed in a light box to take their picture. Weight compared with view area as well as length and width correlations were developed. In addition the effect of "hump" development (see text) of pink salmon on this correlation was investigated. It was possible to predict the weight of a salmon by view area, regardless of species, and regardless of the development of a hump for pinks. Within pink salmon there was a small but insignificant difference between predictive equations for the weight of "regular" fish and "humpy" fish. Machine vision can accurately predict the weight of whole salmon for sorting.


Subject(s)
Body Weight , Fisheries/methods , Image Processing, Computer-Assisted , Oncorhynchus/growth & development , Animals , Artificial Intelligence , Automation/methods , Body Size , Regression Analysis
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