Web system for the objective evaluation of the sensory quality of chocolate using machine learning
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Abstract
This study introduces the design, implementation, and validation of a web-based platform, developed in Python, for the objective assessment of chocolate’s sensory quality utilizing state-of-the-art machine learning techniques. The intrinsic subjectivity in sensory evaluation—arising from heterogeneous consumer and expert perceptions—poses a significant challenge to the chocolate industry. Addressing this limitation, the proposed system integrates structured data on ingredients, geographic origin, and sensory descriptors, augmented with evaluative scores from both consumers and connoisseurs. A predictive model based on the XGBoost algorithm was trained and rigorously validated, achieving a root mean square error (RMSE) of 0.3867, outperforming alternative regression approaches in predictive accuracy. The platform enables users to input new chocolate profiles, receive automated quality predictions, contribute evaluations, and access in-depth analytical insights. The dataset comprises over 1,700 chocolate samples rated by industry experts across diverse origins. The full source code and dataset are openly available at: https://bit.ly/4ci1P4Y
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