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Cocoa bean classification system using artificial intelligence and electronic sensory analysis
The quality of cocoa beans is a determining factor in the food industry, as it directly influences organoleptic attributes such as flavor, aroma, and texture of derived products. Traditionally, the evaluation of cocoa quality has relied on manual and subjective processes, dependent on the expertise of tasters, which often leads to significant variations in results and limits efficiency along the value chain. To address this challenge, the present work proposes an intelligent system for evaluating cocoa bean quality based on artificial intelligence algorithms, aiming to ensure greater objectivity, accuracy, and scalability in the classification process. The system is structured into four main phases: (1) collection of dry and roasted cocoa samples, (2) data acquisition through sensors that capture relevant physical and chemical attributes, (3) training and classification of machine learning models in MATLAB).
