Application of Vis/NIR Hyperspectral Imaging Technology in Non-Destructive Measurement of Soluble Solid Content in Lingwu Jujube
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Abstract:
Hyperspectral imaging technology was adopted to measure the soluble solid content (SSC) of Lingwu jujube fruits and provided a scientific method for the non-destructive measurement of their interior quality. The diffuse reflectance spectra of Lingwu jujube fruits were preprocessed, the successive projections algorithm (SPA) and competitive adaptive reweighed sampling (CARS) were used to select the characteristic wavelengths, and the partial least squares regression (PLSR) model and principal component regression (PCR) were employed to build the predictive model for the SSC of Lingwu jujube. The results indicated that the detrend pretreatment method provided the optimum performance, the correlation coefficient of cross calibration (Rcv) of the established PLSR model was 0.809, and the root mean square error of cross validation (RMSECV) was 1.331. The SPA and CARS were effective in the dimensionality reduction of spectral data. Based on the eight and 21 characteristic variables selected by SPA, predictive models were established using PLSR and PCR, respectively. The optimal prediction performance was presented by the CARS-PLSR model, whose correlation coefficient of prediction (Rp) and root mean square error of prediction (RMSEP) were 0.864 and 1.174, respectively. The results indicate that non-destructive measurement of the SSC of Lingwu jujube using hyperspectral imaging technology is feasible.
