Nondestructive Determination of Apple Internal Qualities Using Near-Infrared Hyperspectral Reflectance Imaging
2015
Dong, Jinlei | Guo, Wenchuan
Hyperspectral reflectance imaging technology in near-infrared regions (900–1,700 nm) was used to evaluate soluble solids content (SSC), firmness, moisture content (MC), and pH values of ‘Fuji’ apples during a 13-week storage period. Totally, 167 apples were divided into calibration set (125) and prediction set (42) based on the joint x-y distance sample set partitioning method. Mean spectrum of the regions of interest in the hyperspectral image of each apple was used for analysis. Two typical variable selection methods, i.e., successive projection algorithm (SPA) and uninformative variable elimination (UVE), were applied to extract the characteristic variables from full spectra (FS). The partial least squares (PLS) regression, least squares support vector machine (LSSVM), and backpropagation (BP) network modeling methods were used to establish models to predict SSC, firmness, MC, and pH of apples, respectively. The results showed that the SSC and MC could be predicted exactly by all developed models, and SPA-LSSVM and FS-BP could be used to predict pH value roughly. All models failed to predict firmness. The SPA-LSSVM model had better comprehensive ability in determining SSC, MC, and pH than others, with the correlation coefficient of prediction of 0.961, 0.984, and 0.882 and residual predictive deviation of 3.49, 5.51, and 2.06, respectively. The results demonstrated the feasibility of using near-infrared hyperspectral reflectance imaging technology as a non-invasive method for predicting SSC, MC, and pH of apples simultaneously.
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