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

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.