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Increasing globalization of our food supply combined with recent outbreaks of foodborne disease has heightened concerns over food safety issues around the globe. In response to these growing concerns, both by government and consumers, many nations are looking at food traceability as a means to restore confidence in the food supply and limit damages incurred by the sale and distribution of unsafe products. Traceability, which allows for the tracking of food products through all steps of production, distribution, and sales, can provide information on the nature, origin, and quality of a product; allowing consumers to make more informed purchasing decisions while providing the mechanisms for quick and thorough product recall procedures. Implementing traceablity systems will require improved vertical intergration between entities and the development of standars for the collection and dissemination of traceability data. Fortunately, rapid advances in information technology have made it possible to implement traceability systems within the food industry. This review will explore the current trends toward tracebility in the U.S. seafood industry. Current legislation, both here and abroad, and its effects on the seafood industry will be explored, and the design of a traceability system will be discussed.

Quality parameters for surimi from Pacific Whiting were detennined. Three different computer analyses were used: these included a traditional multiple linear regression, a model that documents the quality issues in a non-linear neural network framework, and an induction-type machine learning system to establish relationships of the on-board handling, physical characteristics and dockside storage condition on product quality. The strengths and weaknesses of each model are discussed.

There have been tremendous changes in aquatic food products over the past 25 years based on seafood research. The future of seafood research over the next 25 years is difficult to predict, but the ...

A procedure for analyzing categorical purchase intent data from consumer tests is demonstrated using data from a test of fresh market specialty potato varieties. Due to the probabilistic nature of purchase intent a fractional model is based, just as a binary choice analysis using probit or logit would be, on a non-linear cumulative distribution function. The results from a fractional model are compared to an ordinal model. Participants in the consumer test evaluated six unreleased varieties and one commonly available variety of potato, the Yukon Gold. Four of the new varieties had colored red or purple flesh rated highly for antioxidant content. The effect of antioxidant information and other variables on the probability of purchase for red and purple potatoes was compared to the effect on the yellow potatoes using a fractional model and an ordinal model. Other variables include liking, price, gender, age, education, income, potato usage frequency, health interest, and food interest. As expected hedonic ratings and price had significant positive and negative effects respectively on purchase intent for both categories of potatoes. Antioxidant information, whether simple or detailed, increased purchase intent for the colorful potato varieties. Interactive variables between health interest and antioxidant information level demonstrate the impact of personal health interest in conjunction with the information.