Cultivar-based astringency screening in peaches using fluorescence spectroscopy coupled with dimensionality reduction

Abstract

From the perspective of quality assurance of peach fruit, there is a need to establish a non-destructive method for evaluating astringency. This study aimed to construct a screening method for classifying astringency in peach fruit by combining fluorescence spectroscopy with dimensionality reduction techniques. The excitation emission matrices (EEM) of the peach peel surface were measured, and the content of components related to astringency was determined. Dimensionality reduction was applied on the obtained EEM data using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), and astringency classification models were constructed using support vector machine (SVM) with the obtained features as input. As a result, classification models using data from all cultivars showed overall accuracies of 50–70%, while models constructed by dividing data for each cultivar achieved overall accuracies of over 80% for all cultivars. These results suggest the potential of fluorescence spectroscopy for astringency screening by cultivar in peach fruit.