Optimization of Fruit Pose and Modeling Method for Online Spectral Detection of Apple Moldy Core

Moldy core of apples is a fungal disease that affects many commercially popular cultivars of apples. It is difficult to distinguish moldy core of the fruit from its appearance until the fruit is cut open. The objective of this study was to detect moldy core of apples by visible near-infrared spectroscopy (NIRS). The discrimination effects of four kinds of apple on-line transportation postures were compared: the apple stem upward, the apple stem downward, the apple stem towards the transportation direction, and the apple stem perpendicular to the transportation direction. Principal component analysis (PCA) was used to extract the principal components from the transmission spectra of 600 similar to 900 nm, and then linear discriminant analysis (LDA) Mahalanobis distance (MD) and k-nearest neighbor (KNN) models were established for comparison. The partial least squares discriminant analysis (PLS-DA) model was established after the central pretreatment of 600 similar to 900 nm. Two machine learning algorithms, extreme learning machine (ELM) and support vector machine (SVM) were also used to predict moldy core of apples. The best modeling method is PLS-DA. The accuracy rate of stem upward and stem downward was 93. 75%, and the accuracy of the other two postures were more than 85%. Then according to VIP (variable importance in projection) scores, the characteristic band 690 similar to 720 nm was extracted, and the model was rebuilt. The best result of the four postures was apple stern upward. The accuracy rate of the prediction set was 93. 75%. The results showed that PLS-DA could be used as an effective method to distinguish moldy core of apples, and the stem upward can be used as an effective posture for on-line detection of moldy core of apples.