COMPARATIVE ANALYSIS OF ORGANIC AND CHEMICALLY PROCESSED FOODS THROUGH AI-ASSISTED STATISTICAL EVALUATION USING SHAP, LIME AND NNQ ALGORITHMS.

Abstract

This study employs advanced AI-driven statistical analyses to compare organic and processed foods across multiple dimensions, including nutritional content, health outcomes, consumer behavior, and environmental impact. AI models reveal significant nutritional differences, with organic foods exhibiting higher levels of vitamins and antioxidants and lower pesticide residues, while processed foods contain more additives. Predictive machine learning models associate organic food consumption with reduced risks of chronic diseases, contrasting with the elevated health risks linked to processed food intake. Consumer sentiment analysis and clustering algorithms highlight distinct preference patterns favoring organic products for health and environmental reasons. Environmental assessments demonstrate lower carbon footprints and water usage in organic food production. Robustness checks, including cross-validation, sensitivity analyses, and confounder controls, ensure model reliability and fairness. Explainability techniques such as SHAP and LIME enhance transparency and bias detection. This comprehensive AI statistical framework advances understanding of the complex differences between organic and processed foods, supporting informed consumer and policy decisions.