| Project/Area Number |
26750023
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Eating habits
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| Research Institution | Prefectural University of Hiroshima |
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Principal Investigator |
Mabuchi Ryota 県立広島大学, 人間文化学部, 助教 (00632671)
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| Research Collaborator |
Tanimoto Shota 県立広島大学, 人間文化学部, 教授 (80510908)
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| Project Period (FY) |
2014-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2015: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2014: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | メタボロミクス / 魚肉 / 品質評価 / 部位判別 / 鮮度評価 / おいしさ評価 / 代謝成分 / 予測モデル / おいしさ評価系 / 味覚センサー / メタボローム解析 / OPLS解析 / 呈味モデル / ハマチ（ブリ）筋肉 / 酸味 / 旨味コク / 新たな魚肉鮮度評価法 / 貯蔵日数予測モデル / メタボリックプロファイル / メタボライト / 一般生菌数 / K値 / OPLS回帰分析 / ハマチ筋肉 / 部位判別分析 / 代謝成分プロファイル / OPLS-DA / 血合肉 / 鮮度評価指標 / 水産食品 / 養殖ハマチ / 質量分析 |
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| Outline of Final Research Achievements |
Metabolomics technology, which is gathering attention as a new method for assessing food quality, was used to assess the quality of fish meat. Yellowtail, a typical red-fleshed fish, was used as the model in this experiment. 1) To find applications of metabolomics in fish meat, an analysis to distinguish the various muscle parts in the yellowtail was conducted. In particular, it distinguished the dorsal, caudal, and ventral portions, which do not vary greatly in appearance. 2) It identified the changes in metabolic components associated with storage. A predicting model for the storage period was constructed and compared with the existing method for assessing freshness, and the results indicated its effectiveness. 3) The correlation between the values for various tastes obtained through the taste sensor and information of the metabolic components were analyzed. A predicting model of taste was then constructed while identifying the metabolic components associated with the various tastes.
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