The extraction of nitrogenous compounds from cod (Cadus morhua) offal by enzymic hydrolysis with papain, the membrane separation of the peptides in the soluble fraction and their freeze and membrane concentration were investigated with the aim of obtaining a high yield of functional products. The following aspects were covered: kinetics of the hydrolysis and of the enzyme inactivation, separation of insoluble solids and yield of nitrogen under different operating conditions (water to fish offal ratio, temperature and particle size of the raw material). Three of the processing options -the hydrolyses, without and with added water, and the water extraction of the minced fish offal followed by hydrolysis of the residue- gave nitrogen yields of 51%, 62% and 69% of the total nitrogen in the fish offal, and required water: fish offal ratios of 0,1 and 1.86 respectively. The peptides in the hydrolysate supernatants were not amenable to membrane separation but those in the water extract supernatant could be split by pH precipitation and ultrafiltration. Eight functional properties of the potential products were evaluated. The hydrolysate supernatants lacked most of them except for high solubility but their low ash content and the particular molecular weight distribution of their peptides may be useful in special feeding diets. The pH precipitate and the concentrate from the ultrafiltration of the supernatant from the pH precipitation exhibited all the functional properties tested to some extent. The precipitate exhibited low solubility and high buffering capacity, high viscosity and good foaming properties. The concentrate had good solubility, gel strength and emulsifying properties.

No description available.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution- based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users’ information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This article presents a knowledge-based approach to the design of document-based retrieval systems. We conducted two empirical studies investigating the users' behavior using an online catalog. The studies revcaled a range of knowledge elements which are necessary for performing a successful search. We proposed a semantic network based representation to capture these knowledge elements. The findings we derived from our empirical studies were used to construct a knowledge-based retrieval system. We performed a laboratory experiment to calculate the search performance of our system. The experiment showed that our system out-performed a conventional retrieval system in recall and user satisfaction. The implications of our study to the design of document-based retrieval systems are also discussed in this article.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.