(untitled) (Statistics, Optimization & Information Computing, 2026)
The roasting process greatly influences cocoa beans’ quality and antioxidant potential, transforming phenolic compounds and flavour precursors. However, the conventional method of evaluating roasting levels relies on subjective human perception, which can lead to inconsistencies in quality control. This study proposes an intelligent and objective classification system for cocoa bean roasting levels (light, medium and dark) based on RGB images, using a Hybrid Multi- Objective Golden Eagle Optimiser approach integrated with Graph Convolutional Networks (HMOGEO–GCN). The study uses 1000 images for each roasting class. The ResNet18 model is a feature extractor optimised through multi-objective GEO to balance accuracy, computational cost and convergence speed. GCN strengthens population topology learning for a more effective global search. Using a 10-fold cross-validation, experimental results show that HMOGEO–GCN achieves superior performance, with an average accuracy of 90.5% ± 0.8, an F1-score of 0.913 and an AUC of 0.904. This surpasses the performance of standard CNNs and single optimisation approaches. Furthermore, the classification results demonstrate a strong correlation with chemically reported antioxidant activity and total polyphenol content, showing the potential of this model for predictive quality control in the cocoa roasting process. This approach contributes to developing deep learning for food analysis and supports implementing intelligent, precision cocoa roasting systems in industry.