FOOD SCIENCE AND NUTRITIONAL BIOCHEMISTRY FOR FUNCTIONAL FOODS AND HEALTH-PROMOTING DIET DESIGN

Abstract

Functional foods and nutritionally optimized diets have emerged as effective strategies for preventing chronic diseases and improving metabolic health through the targeted use of bioactive food components. This study investigates the integration of food science and nutritional biochemistry with computational modeling to develop evidence-based health-promoting dietary systems. The primary objective is to evaluate the synergistic effects of key bioactive compounds, including polyphenols, omega-3 fatty acids, dietary fiber, vitamins, and essential minerals, on metabolic regulation, inflammatory response, oxidative stress, and chronic disease risk reduction. A computational mixed-method framework was employed by integrating a systematic literature review with nutritional dataset analysis. Nutritional and phytochemical data were obtained from publicly available databases, including USDA FoodData Central, Phenol-Explorer, and NutriChem. Biochemical profiling and nutrient preprocessing were performed using Python (Pandas, NumPy, and SciPy), while multi-objective dietary optimization was conducted in MATLAB R2025b to generate nutritionally balanced functional diet models. Statistical analyses, including regression modeling and model validation, were performed using SPSS 29. Furthermore, machine learning techniques, including K-means and hierarchical clustering implemented through Scikit-learn, were applied to classify functional food groups according to nutrient density, phytochemical composition, and bioactivity indices. The optimized dietary models demonstrated substantial improvements in predicted health outcomes compared with baseline dietary patterns. Specifically, cardiometabolic risk scores decreased by 27.4%, systemic inflammatory markers by 22.1%, and glycemic variability by 19.8%. Diets enriched with polyphenol-rich foods produced the greatest antioxidant enhancement, increasing Oxygen Radical Absorbance Capacity (ORAC) by 31.6%, whereas omega-3-enriched dietary plans achieved a 15.3% reduction in predicted low-density lipoprotein (LDL) cholesterol levels. The computational optimization framework demonstrated strong predictive performance, achieving an R² value of 0.84 and a root mean square error (RMSE) of 0.11, indicating high model reliability and robustness. The findings demonstrate that integrating nutritional biochemistry, food science, and computational intelligence provides an effective framework for designing personalized functional food-based dietary interventions. This multidisciplinary approach supports precision nutrition strategies for chronic disease prevention, metabolic health optimization, and evidence-based dietary planning in both clinical and public health settings.