ABSTRACT The accurate estimation of caloric density in food products is a critical component of nutritional science and dietary management, yet experimental determination remains resource intensive. The research develops a robust computational framework for predicting caloric energy based on standard nutritional composition variables using advanced machine learning techniques. To achieve this, a dataset comprising 410 food items with seven predictors, including protein, fat, carbohydrates, sugar, dietary fiber, sodium, and potassium, was utilized to train a Gradient Boosting Decision Trees (GBDT) model. The study evaluated the efficacy of four exceptional hyperparameter optimization algorithms: BBO, BPI, GPO, and evolutionary strategies (ES). Performance was rigorously assessed using 5‐fold cross‐validation and statistical metrics including R 2 , MSE, and AARE%. The results demonstrated that the GBDT‐ES configuration achieved the best performance with a test R 2 of 0.982950 and an AARE% of 3.661596%, whereas GBDT‐GPO offered a competitive balance of accuracy and computational efficiency with the lowest runtime. Furthermore, SHAP analysis revealed that carbohydrates and fats were the primary drivers of caloric estimation, ensuring the model aligned with biological energy densities. In conclusion, the integration of evolutionary optimization with gradient boosting provides a highly precise and scientifically interpretable tool for nutritional analysis, offering a viable alternative to traditional laboratory calorimetry.
Alharbi et al. (Thu,) studied this question.