Developing a prediction method for physicochemical characteristics of Pontianak Siam orange (Citrus suhuiensis cv. Pontianak) based on combined reflectance-Fluorescence spectroscopy and artificial neural network

Abstract

The slightly sweet and acidic taste offered by Pontianak Siam oranges is influenced by the total soluble solids (TSS) and acidity in the fruit, in which, measuring these attributes is commonly performed using instruments that potentially damage the fruit's structure, thus, impractical for fresh fruit products. Moreover, the process of classifying the quality of fresh oranges has been based on physical appearance, leading to subjective results. Correspondingly, the objective of the study is to develop a prediction method for the physicochemical characteristics of Pontianak Siam oranges based on VIS-NIR-Fluorescence spectroscopy and an artificial neural network (ANN) model. The method is applicable to classify oranges based on physicochemical characteristics without damaging the fruit's structure. As a result, the best model for classifying the maturity level of Pontianak Siam oranges was obtained using a dataset with all feature combined spectra, attaining a training accuracy of 0.99 and testing accuracy of 1. The best model for predicting TSS was obtained using all feature combined spectra dataset, attaining R² training = 0.89 and R² testing = 0.91. The best model for predicting acidity was obtained using all feature reflectance spectra datasets, attaining R² training = 0.96 and R² testing = 0.97. The best model for predicting fruit firmness was obtained using all feature reflectance spectra dataset, attaining R² training = 0.97, R² testing = 0.89. Overall, the combination of Vis-NIR reflectance and fluorescence spectroscopy have the potential to be applied for non-destructive assessment of citrus quality in terms of visual classification and maturity parameters prediction.