Dynamic Detection of Fresh Jujube Varieties Based on Least Square Support Vector Machines
The present research was focused on dynamic detection of fresh jujube varieties by visible and nearinfrared spectroscopy. In the part of data gathering, fresh jujube was moving at the constant velocity of 0.1 m ·s-1 on the conveyor belt, and the visible and near-infrared diffuse reflectance spectrum (350-2500nm) was captured. In the part of data processing, moving average was employed to eliminate the spectra noise. Then the least square support vector machines (LSSVM) models were established with the original spectra and the denoised spectra (500~1500nm) of 2S5samples in the calibration set. At last, the models those were the Original/LS-SVM model and the Smooth/LS-SVM were used to predict the varieties of 90 samples in the prediction set. The results showed that the discrimination accuracy (95.56％) for the fresh jujube varieties by Original/LS-SVM model was slightly higher than the discrimination accuracy (91.11％) for the fresh jujube varieties by Smooth/LS-SVM model. And the fitting result of the former was better than the fitting result of the latter. So the Original /LS-SVM model can be taken as a dynamic detection method to detect fresh jujube varieties, but the precision and stability in the model is needed to be further improved.
Fresh jujube varieties Visible/near-infrared spectroscopy Dynamic detection Least square support vector machines
Zhao Conghui Zhang Shujuan Zhang Haihong Zhao Yanru
College of Engineering, Shanxi Agricultural University, Taigu, Shanxi, China. 030801
国际会议
山东淄博
英文
888-891
2011-05-27（万方平台首次上网日期，不代表论文的发表时间）