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• A method to correct the effect of fruit size on the NIR spectra was proposed. • The method was to facilitate the identification of internal diseases in apples. • An equation to calculate extinction coefficient of transmitted light was developed. • The extinction coefficient was used to modify the transmission spectrum. Moldy core is an internal disease of apples that can be detected using transmission near-infrared spectroscopy. However, the transmission spectra of apples are affected by the size of the fruit (i.e., the optical path length). When transmission spectra are used to distinguish between healthy and diseased apples, healthy apples with large diameters are often misclassified as diseased, while apples with moldy cores and small diameters are often misclassified as healthy. To solve this problem, a spectral correction method, based on the size of the fruit is proposed herein. Spectra and transverse diameter information for 327 Fuji apples were obtained in this study. Using experiments, we verified that the rate of light extinction in apple flesh varied log-linearly with thickness. In this case, spectra of healthy apples with diameters of 80 mm were selected as a reference. Comparing the spectra of 327 apples with varying diameters against this reference, we developed an equation to calculate the extinction coefficient of transmitted light within the fruit. Transmission spectra were modified according to this extinction coefficient. Error back propagation artificial neural networks and support vector machine models were established based on corrected versus original spectra. These results showed that the accuracy of the classification models based on the corrected spectra was much higher, yielding accuracy rates of 98.04% for the training set and 90.20% for the test set. Clearly, the effect of fruit size on the transmission spectra could be corrected to improve the identification of diseased apples using this method. [ABSTRACT FROM AUTHOR]

• Develop an online system for moldy core detection. • Stability study of spectra acquired in three fruit orientations using SNR and ACR. • Analysis of spectral propagation in different orientations. • Comparison to moldy core detection models of local and global orientations. Moldy core is an internal disease afflicting otherwise healthy apples. This paper reports the results of a study on the effect of fruit orientation on the online detection of moldy core in Fuji apples using Vis/NIR transmission spectroscopy. Using a self-designed online spectral measurement system, transmission spectra in the range 550–900 nm were collected from apples positioned in three orientations: vertical calyx-stem axis with stem upward (T1), horizontal calyx-stem axis (T2), and vertical calyx-stem axis with stem downward (T3). In order to evaluate the spectral stability of the system in different orientations, the area change rate (ACR) and signal-to-noise ratio (SNR) were introduced. The propagation of light inside the fruit was also analyzed. After excluding outliers, 244 apple samples were divided into a training set (113 healthy apples and 70 diseased apples) and a test set (38 healthy apples and 23 diseased apples) at a ratio of 3:1. After spectral pretreatment, a support vector machine (SVM) was used to develop compensation models for the detection of moldy core in apples at all orientations (global model) and each separate orientation (local models). The results show that T2 provides the best performance in our system, and that the measurement orientation significantly affects the recognition accuracy of moldy core in apples. In contrast, the global model is insensitive to changes in fruit orientation, and the recognition accuracy of the global SVM model is 100% in all three orientations. Thus, it is considered that the global SVM model is a promising candidate for the online detection of moldy core in apples using transmission spectroscopy, regardless of the spectral measurement orientation. [ABSTRACT FROM AUTHOR]

• The maturity of bagged 'Fuji' apple is determined by Vis-NIR spectroscopy. • Starch index is used to determine the maturity level of apple. • The characteristic wavelengths and spectral indexes related to apple maturity are extracted. • Machine learning classifiers are established to predict apple maturity on three levels. Determination of apple maturity in orchards is very important to determine the harvest time and postharvest storage conditions. The aim of this study is to investigate the ability of visible and near-infrared (Vis-NIR) spectroscopy to determine the maturity of bagged 'Fuji' apples using the starch index as the maturity index. Using the starch index, 846 apples were divided into three maturity levels (immature, harvest maturity, and eatable maturity). Principal component analysis, the random frog (RF) algorithm, and the RF algorithm combined with the successive projection algorithm (RF-SPA) were used to extract the principal components or characteristic wavelengths of the spectral data. Five machine learning algorithms, namely, the least squares support vector machine (LSSVM), the probabilistic neural network, the extreme learning machine, the partial least squares discrimination analysis, and linear discriminant analysis (LDA), were used to develop a calibration model. By comparing the results of different modeling methods, it was determined that the prediction performance of the RF-SPA-LSSVM model based on 15 characteristic wavelengths was the best. The classification accuracy of the prediction set was 89.05% and the area under the receiver operating characteristic curve of the three types of apples was greater than 0.9210. In addition, four spectral indexes related to the chlorophyll content were used to predict apple maturity. The classification accuracies of the LDA models based on the spectral indexes were 77.63%–80.95%, which were lower than that of the calibration model based on the characteristic wavelength. The results show that the maturity of bagged 'Fuji' apples can be accurately and nondestructive determined by Vis-NIR spectroscopy. The selected characteristic wavelengths and spectral indexes can provide a reference for development of a nondestructive device for determination of apple maturity. [ABSTRACT FROM AUTHOR]