Abstract:Using a laboratory-installed near-infrared spectroscopy system, three kinds of damaged ‘Huping’ jujube samples, and intact samples, were examined and identified to explore the influence of rotation speed on the detection of damage. Spectral information on fruits with different degrees of damage at three rotation speeds of 1.5 r/min, 2.0 r/min, and 2.5 r/min were collected by Fieldspec 3 spectrometer, and the results were calculated based on the measured rotation speed. The partial least squares (PLS) model was built and several discriminant indices were used to determine the best spectral pre-processing method from 13 methods at three rotation speeds. The partial least-squares regression coefficient (PLSRC) method and successive projections algorithm (SPA) were used to extract the characteristic wavelengths of spectra before calibration. The partial least squares-discriminant analysis (PLS-DA), extreme learning machine (ELM), and least squares support vector machines (LS-SVM) were used to establish discrimination models. The results showed that rotation speed had an impact on the detection of the damage in ‘Huping’ jujube fruits. The optimal models at the rotation speeds of 1.5 r/min, 2.0 r/min, and 2.5 r/min were PLSRC-LS-SVM, PLSRC-PLS-DA, and PLSRC-PLS-DA, respectively, and the corresponding discrimination accuracies were 92.30%, 88.46%, and 86.54%, respectively. The highest damage identification rate was found in the PLSRC-LS-SVM model established at the rotation speed of 1.5 r/min. In addition, with increasing rotation speed, the damage identification rate showed a downward trend. This study provides a theoretical reference for the development of online detection instruments for fresh jujubes.
