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| ファイル | 記述 | サイズ | フォーマット | |
|---|---|---|---|---|
| jccjie.2024-0012.pdf | 1.62 MB | Adobe PDF | 見る/開く |
| タイトル: | A Study of Deep Learning for Quantitative Analysis of Vitamin A in Cattle Blood |
| 著者: | Shibasaki, Mizuki Suzuki, Tetsuhito Fukushima, Moriyuki Nagaoka, Shin-ichi Ogawa, Yuichi Kondo, Naoshi |
| 著者名の別形: | 芝崎, 美月 鈴木, 哲仁 福島, 護之 長岡, 伸一 小川, 雄一 近藤, 直 |
| キーワード: | Deep learning Neural network Fluorescence excitation-emission spectroscopy Vitamin A Retinol Cattle blood random forest |
| 発行日: | 2024 |
| 出版者: | Society of Computer Chemistry, Japan |
| 誌名: | Journal of Computer Chemistry, Japan -International Edition |
| 巻: | 10 |
| 論文番号: | 2024-0012 |
| 抄録: | Deep learning in combination with fluorescence excitation-emission spectroscopy was studied to quantitatively analyze vitamin A (retinol) in cattle blood. The neural network model being obtained with the deep learning predicted the vitamin-A levels with a coefficient of determination (R²) of 0.93 with respect to the experimental values. The combination of the deep learning and fluorescence excitation-emission spectroscopy has a potential to predict the vitamin-A level in the cattle blood accurately, rapidly and inexpensively and to improve production of marbled beef with maintaining cattle health. It could also be applied to quantitative vitamin-A assays of various biological tissues, foods and so on as well as to those of blood samples besides cattle. |
| 著作権等: | This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (CC BY-NC-ND) 4.0 License. |
| URI: | http://hdl.handle.net/2433/291282 |
| DOI(出版社版): | 10.2477/jccjie.2024-0012 |
| 出現コレクション: | 学術雑誌掲載論文等 |
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