Abstract: Apple moldy core is a major disease affecting the internal quality of apple fruit. However, due to the lack of effective means to accurately detect moldy core in apple fruits, detection of moldy core in apples has become a major problem to be solved in the apple industry. To date, there have been no reports on the use of spectroscopy for distinguishing various degrees of moldy core decay in apple fruits. The objective of this study was to develop a non-destructive method for the detection of various degrees of moldy core decay in apple fruits using near infrared transmittance spectroscopy, successive projections algorithm (SPA), and multi-class classification algorithms partial least square-discriminant analysis (PLS-DA), back propagation artificial neural network (BP-ANN), support vector machine (SVM) and deep belief network (DBN). For developing a model to determine the degree of moldy core in apples, 225 samples were selected including a training set of 150 samples and a test set of 75 samples. The model consisted of several layers of restricted Boltzmann machine (RBM) network, which achieved eigenvector projection, and one layer of BP network, which allowed the classification of various degrees of moldy core based on the output eigenvector. The Sd value was calculated by dividing the lesion area by the total cross-sectional area. It was proposed that Sd = 0, 0 < Sd ≤ 10%, 10% < Sd < 30% and Sd ≥ 30 indicated health, mild, moderate and severe degrees, respectively. The classification accuracy of the DBN model was 88.00%, suggesting good performance of the model, and it was compared with those of the BP-ANN and SVM models.

Key words: moldy core in apples, degree of disease, transmittance spectroscopy, deep belief network (DBN), restricted Boltzmann machine (RBM)