Prediction of Influence Transmission by Water Temperature of Fish Intramuscular Metabolites and Intestinal Microbiota Factor Cascade Using Bayesian Networks

Aquaculture is receiving attention as one of the solutions to the global food problem. Therefore, it is essential to clarify the impact of fish and their environment on the stable supply and uniformity of the quality of fish provided as meat. Nuclear magnetic resonance can comprehensively acquire metabolite information in foods nondestructively and is suitable for measuring physical properties for quality control. Moreover, recent advances in machine learning methods and artificial neural network (ANN) analysis have contributed to the analysis of comprehensive information. In this study, we sampled a wide variety of fish from the natural sea and analyzed them using a scheme incorporating ANN. As a result, it was found that anserine, an antioxidant, was found to be reduced in fish muscles, and this destabilized the homeostasis of other metabolites at low water temperature. We also concluded that the fish muscle metabolic state was stabilized in warm water. Furthermore, a relationship between water temperature and the intestinal microbiota of fish was established. In this study, we evaluated the relationship between the metabolic profile changes in fish muscle and external environmental factors and predicted connection strength and order using machine learning and ANN. We conclude that our proposed scheme for estimating the degree and direction of the influence of environmental factors on organisms by using ANN will work.