• Asian-Australasian Journal of Animal Sciences
• /
• v.30 no.4
• /
• pp.569-575
• /
• 2017

Objective: The objective of this research was to study the electrical properties and quality of frozen-thawed chicken breast meat and to investigate the relationship between these parameters at different times of frozen storage. Methods: Thawed samples of chicken breast muscles were evaluated after being kept in frozen storage at $-18^{\circ}C$ for different periods of time (1, 2, 3, 4, 5, 6, 7, and 8 months). Results: The results showed that water-holding capacity (WHC) and protein solubility decreased while thiobarbituric acid-reactive substances content increased with increasing storage time. The impedance module of samples decreased during 8-month frozen storage. Pearson correlation coefficients showed that the impedance change ratio (Q value) was significantly (p<0.05) related to pH, color, WHC, lipid oxidation and protein solubility, indicating a good relationship between the electrical properties and qualities of frozen-thawed chicken breast meat. Conclusion: Impedance measurement has a potential to assess the quality of frozen chicken meat combining with quality indices.

• Journal of Information Management
• /
• v.40 no.4
• /
• pp.1-22
• /
• 2009

Large-scaled information extraction plays an important role in advanced information retrieval as well as question answering and summarization. Information extraction can be defined as a process of converting unstructured documents into formalized, tabular information, which consists of named-entity recognition, terminology extraction, coreference resolution and relation extraction. Since all the elementary technologies have been studied independently so far, it is not trivial to integrate all the necessary processes of information extraction due to the diversity of their input/output formation approaches and operating environments. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In this study, we define scientific as a set of 10 types of named entities and technical terminologies in a biomedical domain. in order to automatically extract these entities from scientific documents at once, we develop a framework for scientific core entity extraction which embraces all the pivotal language processors, named-entity recognizer, co-reference resolver and terminology extractor. Each module of the integrated system has been evaluated with various corpus as well as KEEC 2009. The system will be utilized for various information service areas such as information retrieval, question-answering(Q&A), document indexing, dictionary construction, and so on.