A Sensory-Nutritional Screening Framework to Reduce Water Dependency in Food Industry through Smart Ingredient

This research advances a system-oriented sensory-nutritional framework to reduce water dependency in food processing through rigorously validated ingredient substitution. The framework integrates high-resolution sensory analytics (descriptive profiling, hedonic surface mapping, and psychophysical thresholds) with nutrient bioavailability modeling, DIAAS-based protein quality indices, and cradle-to-gate virtual water life cycle assessment. Safety assurance was embedded via HACCP-aligned dynamic checkpoints, enabling predictive risk control during reformulation. Applied to cereal and dairy matrices, the framework achieved a ۲۶-۳۳% reduction in embedded water footprint while safeguarding product identity. Multivariate statistical modeling (PCA, HCA, PLS-R) confirmed >۹۴% preservation of sensory fidelity, particularly flavor-texture congruence across consumer segments. Nutritional robustness was demonstrated through conservation of indispensable amino acids, stability of thermo-labile micronutrients, and predictive modeling of glycemic responses within FAO/WHO benchmarks. Chemometric deconvolution revealed protein-starch interaction strength, hydration kinetics, and matrix viscoelasticity as mechanistic determinants of consumer acceptance, rheological stability, and shelf life. Beyond sustainability, this framework signifies a paradigm shift: coupling integrated sensory-nutritional metrics with risk-based safety protocols provides the food industry with a transformative decision-support tool. Future integration with AI-driven predictive modeling, flavoromics, and nutrigenomics will further extend its potential, enabling real-time optimization of substitution strategies toward both sustainability goals and personalized nutrition.

کلیدواژه ها:

Sensory-nutritional screening ، Water footprint reduction ، Ingredient substitution ، Food safety and quality control ، Sustainable food processing