基于实验室搭建的近红外光谱检测装置，对不同旋转速度下壶瓶枣的三种损伤样本进行检测识别，探明旋转速度对损伤检测的影响。依据壶瓶枣检测的旋转速度理论计算结果，利用Fieldspec 3光谱仪采集1.5 r/min、2.0 r/min和2.5 r/min三种旋转速度下不同损伤壶瓶枣的光谱信息，基于PLS模型及其判别指标从13种预处理方法中寻找不同旋转速度下的最佳的预处理方式。通过偏最小二乘回归系数法（PLSRC）和连续投影法（SPA）提取光谱特征波长点，然后建立偏最小二乘分析(PLS-DA)、极限学习机（ELM）和最小二乘支持向量机（LS-SVM）三种判别模型。结果表明：旋转速度对壶瓶枣损伤检测存在影响，三种旋转速度下的最佳模型分别为PLSRC-LS-SVM、PLSRC-PLS-DA和PLSRC-PLS-DA，判别准确率分别为92.30%、88.46%和86.54%，1.5 r/min建立的PLSRC-LS-SVM识别率最高。且随着旋转速度的增加，损伤识别率呈下降趋势。该研究为鲜枣在线检测设备的开发提供理论支持。

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.

国家自然科学基金资助项目(31271973)