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Intelligent Selection of Spectral Bands from High-Precision Spectroradiometer Measurements for Optimizing Cocoa Bean Classification
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Intelligent Selection of Spectral Bands from High-Precision Spectroradiometer Measurements for Optimizing Cocoa Bean Classification
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Journal Article
Intelligent Selection of Spectral Bands from High-Precision Spectroradiometer Measurements for Optimizing Cocoa Bean Classification
2025
Overview
Evaluating the spectral properties of cocoa beans based on their fermentation state (fermented, in a poor state, unfermented) is essential for ensuring their quality in the cocoa industry. This study examined the spectral response of beans in the range of 380 nm to 780 nm using the Konica-Minolta CS-2000 spectrophotometer comes from Dijon, France, a device designed to measure the spectrum of objects and sources in the visible range. Different spectral band selection methods have been applied to identify the most discriminating wavelengths for their classification. Several techniques were used: ANOVA, F-score, Lasso, Linear Discriminant Analysis (LDA), Mutual Information, and Partial Least Squares (PLS). A band selector voting process was implemented to determine standard wavelengths identified using the different methods. The selected spectral bands were then leveraged to train classification models, including Random Forest, SVM, and XGBoost. The results show that a restricted subset of wavelengths allows for effective class separation, thereby improving model performance. Among the approaches tested, ANOVA and F-score combined with Random Forest achieved an accuracy of 92.59%, while F-score and Mutual Information coupled with SVM and voting associated with SVM obtained an accuracy of 96.30%. These feature selection methods have effectively reduced dimensionality while maintaining high classification accuracy. These results open up promising prospects for the automation of quality control of cocoa beans, thus contributing to the optimization of industrial processes.
