Estimation Models of the Amberjack (<i>Seriola</i>) Quality by Using Fuzzy Inference
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- NAKAMURA Makoto
- Graduate School of Fisheries Science, National Fisheries University
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- WATANABE Toshiaki
- Graduate School of Fisheries Science, National Fisheries University
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- SHIIGI Tomoo
- Department of Ocean Mechanical Engineering, National Fisheries University
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- TOKUNAGA Kazuhiro
- Department of Ocean Mechanical Engineering, National Fisheries University
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- OHTA Hiromitsu
- Department of Ocean Mechanical Engineering, National Fisheries University
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- MAEDA Toshimichi
- Department of Food Science and Tachnology, National Fisheries University
Bibliographic Information
- Other Title
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- ファジィ推論を用いたブリ属の鮮度推定モデル
Abstract
<p>This research intends to improve quality control while maintaining the skill level of distributers of marine products by designing models which ensure accurate non-destructive estimates of the freshness of fish meat (K value) by using fuzzy inference. A total of 240 sample fish of the genus Seriola (Japanese amberjack, greater amberjack, and yellowtail amberjack) were used to construct the models. Relationships between fish coloration and K value from sample acquisition until 72 hours later under refrigeration at -2°C, +2°C, and +6°C were investigated. By analyzing the results of the relationships between fish coloration and K value statistically, it was found that a combination of the body surface color indexes within 7 reflected well the degree of freshness of fish meat. The fuzzy inference models constructed from these index sets, as antecedent-part variables, were evaluated. The results of both simulation and experimental evaluations demonstrate that the models are robust, with the residuals of the K value indicating an accuracy within 9.34%. Therefore, the models were shown to be highly useful for quality control in the distribution of fresh fish.</p>
Journal
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- Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
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Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 30 (1), 509-516, 2018
Japan Society for Fuzzy Theory and Intelligent Informatics
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Details 詳細情報について
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- CRID
- 1390001205187614336
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- NII Article ID
- 130006351460
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- ISSN
- 18817203
- 13477986
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed