Classifying food ingredients using machine learning on nutritional and biochemical data

The increasing demand for healthier food alternatives has highlighted the need for nutritional intelligence in evaluating food quality. For this purpose, the nutritional and biochemical characteristics of each food ingredient should be classified to develop intelligent food recommendation systems and to help individuals make informed dietary choices. This study uses nutritional and biochemical data to investigate the applicability of machine learning techniques in the categorization of food ingredients into healthy and unhealthy. Six algorithms namely, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and Extreme Gradient Boosting were implemented and compared to assess their predictive capabilities. The experimental results indicate that XGBoost outperformed all other models, with an average accuracy of 94%, demonstrating its better generalization and predictive power based on the ensemble gradient boosting technique. Random Forest and KNN have also achieved a remarkable 92% accuracy owing to ensemble learning and distance-based decision limits, respectively. The findings demonstrate how machine learning can be effectively used to advance nutrition intelligence, as well as providing a comprehensive and extensible foundation for incorporating computational approaches into current food assessments and tailored healthcare.