HealthInspector: A Novel Approach to Calculating Harm Scores for Food Products Based on Chemical Composition and Nutritional Profiles

Abstract

Growing Consciousness about food safety and cosmetic products formulation has resulted in the need for systems that measure probable health hazards linked to different products.HealthInspector is a web-based system using machine learning technology to analyze food and cosmetic products based on their nutritional and chemical composition. The system drives a harm score using pre-established safety thresholds for unsafe ingredients and healthy limits for good ingredients. The backend, implemented in FastAPI, handles user input and fetched data from a PostgreSQL database, while the React.js fronted present an easy to use interface to compare harm scores of various products. The scoring model uses a rule-based penalty -reword scheme, and future versions will utilize machine learning model like XGBoost and lightGBM for improved accuracy and predictive power. This study emphasizes the value of ingredients disclosure and nutritional consciousness, empowering customers with live harm score analysis.The system is scalable, flexible, and able to adapt with emerging scientific knowledge on food and cosmetic safety.