Characteristic Wavelength Selection-based Prediction of Soluble Solids Content of Hami Big Jujubes using the Hyperspectral Imaging Technology

Abstract:

Prediction of soluble solids content (SSC) of dried Hami big jujubes was studied by means of the hyperspectral imaging technology in this work. Many spectral preprocessing methods were used to preprocess the raw spectra, and the partial least squares (PLS) models were established based on the raw spectra and preprocessed ones. The comparison and analysis showed that the best preprocessing result was achieved by the mean centering (MC) algorithm. MC-pretreated spectra were screened by the synergy interval partial least squares (si-PLS) method, and the characteristic wavelengths of SSC of Hami big jujubes were selected using a combination of a genetic algorithm (GA) and competitive adaptive reweighted sampling algorithm (CARS). The selected wavelength variables were used to build a PLS predictive model for SSC of Hami big jujubes. The results indicated that 16 characteristic wavelengths were selected and accounted for only 2% of full spectral variables in the MC-CARS-GA-si-PLS model. The performance of the newly built PLS model was better than that of the PLS model based on the full spectrum. The correlation coefficient of the prediction set (Rp), the root mean square error of prediction (RMSEP), and the relative prediction deviation (RPD) of this model were 0.93, 0.48, and 2.721, respectively. The results show that this PLS predictive model based on the wavelength selection not only is simpler but also enhances the predictive ability of such models, and can serve as a reference for quantitative analysis and study of fruits and dried fruits by the hyperspectral imaging technology.