Fast Prediction of Sugar Content in Dangshan Pear (Pyrus spp.) Using Hyperspectral Imagery Data

摘要

Sugar content in fruit is one of the most crucial internal quality factors and provides valuable information for predicting maturity and making commercial decisions. The distribution of sugar content in natural fruit is uneven. Conventional spectroscopy methods are not able to effectively solve this problem because they acquire spectral data from a single point or multiple points of the fruit. Therefore, this study explores the potential application of hyperspectral imaging in the wavelength range of 400-1000 nm for fast nondestructive prediction and visualization of sugar content in Dangshan pear. Hyperspectral imagery data and sugar content of pear samples were acquired in the laboratory. A mean normalization step was used to reduce the effect of sample curvature on spectral profiles. Spectra from whole fruit were extracted to build spectrum datasets. Different variable-selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), the successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), CARS-SPA, GA-SPA, and various other modeling methods such as linear partial least squares (PLS), nonlinear least squares support vector machine (LS-SVM), and back propagation artificial neural network (BP-ANN), were compared, and the results show that the linear CARS-PLS (correlation coefficient (r (pre)) = 0.8971 and prediction root mean square error (RMSEP) = 0.3937%) and GA-SPA-PLS (r (pre) = 0.8969 and RMSEP = 0.3482%) models are the optimal models for predicting sugar content in Dangshan pear. Compared with the GA-SPA-PLS model, CARS-PLS is more stable, whereas the GA-SPA-PLS model might be faster for performing a prediction task. Finally, the sugar content of pears given by hyperspectral imagery data is predicted and visualized by using the developed model. This study shows that the combination of hyperspectral imaging with lighting correction, CARS, GA-SPA variable selection methods, and PLS modeling has a great potential for nondestructive quantitative measurement and visualization of sugar content in Dangshan pear.