Integrating Biotechnology for the Analysis of Sports Nutrition Intake and Energy Consumption Among College Students

Bo Ma
Beijing Vocational College of Agriculture, 102442, China

DOI:https://doi.org/10.5912/jcb2522

Abstract:

Addressing the complex relationship between nutritional intake and energy expenditure in college students' physical activity, this study integrates biotechnology-driven insights into sports science analytics. Utilizing big data platforms to model metabolic pathways and biometric responses, the research establishes a dynamic framework for optimizing nutrient-energy balance during exercise. A data-driven Model Predictive Controller (MPC) is proposed, incorporating biotechnology principles to adapt dietary recommendations based on real-time physiological parameters. The approach employs a multi-rate sampling system enhanced with biometric data integration, transforming raw exercise and nutrition data into actionable insights. An extended dimension method, tailored for biotechnology applications, refines model accuracy by accounting for individual variations in metabolism and energy utilization. Experimental validation demonstrates the system's effectiveness, achieving a 92% accuracy rate in predicting energy expenditure while maintaining nutrient intake within recommended thresholds. This research contributes to the advancement of sports biotechnology by illustrating how integrative modeling can bridge nutrition science, exercise physiology, and data analytics. The proposed framework offers a scalable and personalized solution for sports nutrition planning, supporting biotechnology industry objectives in precision health management for academic environments.