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Ripe fruit provides essential nutrients for the human body. To fulfill the needs of consumers, the practice of artificial ripening has become more common. Artificial ripening not only degrades the quality of fruit but also impairs health. The potential of hyperspectral imaging coupled with machine learning to quickly and uninvasively identify differently ripened bananas was explored in this study. A total of 300 banana samples that were naturally ripened or ripened by ethephon or calcium carbide were characterized by their hyperspectral images. To improve the accuracy of classification models and to reduce the effects of noise and irregular surfaces, different preprocessing strategies were investigated. Recursive feature elimination (RFE) and the successive projections algorithm (SPA) were employed to select feature wavelengths. Four classification methods – extreme gradient boosting (XGBoost), support vector machine (SVM), multi-layer perceptron (MLP), and partial least square discriminant analysis (PLS-DA) – were applied for ripening identification. The results showed that the best classification model was XGBoost based on full wavelengths, which achieved an area under the macro-average receiver operating characteristic curve of 0.9796 and high training, cross-validation, and testing accuracy values of 95.02%, 92.31%, and 91.16%, respectively. The RFE-XGBoost model reduces the data dimension and maintains satisfactory performance. Both XGBoost models achieved 100% correct differentiation between naturally ripened and artificially ripened bananas. Hence, the method may be employed to quickly and uninvasively classify differently ripened bananas.

Rapid and Uninvasive Characterization of Bananas by Hyperspectral Imaging with Extreme Gradient Boosting (XGBoost)