FoodByte: a personalized food scoring and additive risk assessment algorithm for smarter eating

Abstract

Current front-of-package food-scoring systems, such as Nutri-Score, summarize nutritional quality using population-level criteria but do not account for individual health conditions, food additives, or allergen sensitivities. We developed FoodByte, a rule-based personalized food-scoring framework integrating nutrient composition, literature-informed additive-risk heuristics, allergen detection, and disease-specific scoring rules to generate individualized food ratings. FoodByte was evaluated using 50 commercially available packaged foods across five representative health profiles (250 product-profile evaluations) and compared with Nutri-Score. Differences between scoring systems were analyzed using descriptive statistics and a cross-classified linear mixed-effects model with random intercepts for food product and health profile. Sensitivity analyses and model diagnostics assessed score divergence and algorithmic robustness. FoodByte produced systematically lower scores than Nutri-Score for many processed foods, with an estimated mean difference of −10.49 points (95% CI: −14.50 to −6.48, p = 2.88 × 10⁻⁷). Disease-specific profiles showed larger score divergence than the control profile, with mean percentage differences ranging from 24.4% to 41.6%, compared with 11.6% for controls. Variance-component analysis indicated that intrinsic nutritional characteristics established the primary baseline for food evaluation, while disease-specific personalization selectively modified recommendations for foods containing condition-relevant nutrients or additives and preserved consistent recommendations for foods broadly suitable across health profiles. Sensitivity analyses demonstrated high internal stability across algorithmic perturbations, with correlations generally exceeding 0.98. These findings demonstrate that additive-aware, disease-specific scoring can systematically modify a population-level nutrient-profiling framework while remaining internally robust. Rather than uniformly re-ranking foods, FoodByte selectively personalizes recommendations where disease-specific nutritional considerations are most relevant. Because the products were purposively selected and no clinical outcomes were evaluated, the findings should be interpreted as evidence of algorithmic divergence and internal robustness rather than clinical superiority. Future studies should validate personalized food-scoring systems against clinical outcomes, expert assessments, and consumer decision-making.