A Study of Deep Learning for Quantitative Analysis of Vitamin A in Cattle          Blood

Mizuki SHIBASAKI; Tetsuhito SUZUKI; Moriyuki FUKUSHIMA; Shin-ichi NAGAOKA; Yuichi OGAWA; Naoshi KONDO

doi:10.2477/jccjie.2024-0012

A Study of Deep Learning for Quantitative Analysis of Vitamin A in Cattle Blood

Mizuki SHIBASAKI, Tetsuhito SUZUKI, Moriyuki FUKUSHIMA, Shin-ichi NAGAOKA, Yuichi OGAWA, Naoshi KONDO

Author information

Mizuki SHIBASAKI
Laboratory of Bio-Sensing Engineering, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
Tetsuhito SUZUKI
Laboratory of Bio-Sensing Engineering, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
Productive Environment System Laboratory, Department of Environmental Science and Technology, Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie 514-8507, Japan
Moriyuki FUKUSHIMA
Laboratory of Bio-Sensing Engineering, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
Shin-ichi NAGAOKA
Laboratory of Bio-Sensing Engineering, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
Affinity Science Corporation, 1-11-1 Nishigotanda, Shinagawa-ku, Tokyo 141-0031, Japan
Department of Chemistry, Faculty of Science and Graduate School of Science and Engineering, Ehime University, Matsuyama, Ehime 790-8577, Japan
Yuichi OGAWA
Laboratory of Bio-Sensing Engineering, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
Graduate School of Agricultural Science, Tohoku University, 468-1, Aoba, Aramaki-aza, Aoba-ku, Sendai, Miyagi 980-8572, Japan
Naoshi KONDO
Laboratory of Bio-Sensing Engineering, Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan

Keywords: Deep learning, Neural network, Fluorescence excitation-emission spectroscopy, Vitamin A, Retinol, Cattle blood, random forest

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Supplementary material

2024 Volume 10 Article ID: 2024-0012

DOI https://doi.org/10.2477/jccjie.2024-0012

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Abstract

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.

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