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.

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In this study the effect of various processing techniques on the fatty acid composition of mussel, a member of bivalves, was investigated. According to the results, applied techniques such as smoking, pickling, and different kinds of cooking are recorded to have caused important changes in their fatty acid compositions. It was also seen that these changes were occurred from PUFA’s, ecosapentaenoic (EPA) (C20:5 n-3), and docosahexanoic (DHA) (C22:6 n-3) which are important especially in nutrition. Generally, in accord with the processing technique, a decrease in different proportions was found in the quantity of ecosapentaenoic and docosahexanoic acids. Maximum decrease in the mentioned fatty acids was especially observed in the mussels processed by frying and cooking.

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