Determination of bagged 'Fuji' apple maturity by visible and near-infrared spectroscopy combined with a machine learning algorithm

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.