On-site Determination of Grape Soluble Solid Content (SSC) Based on Spectroscopic Technology
基于光谱技术的葡萄活体可溶性固体含量在线检测研究

The fast detection of inner quality of living fruit is of importance to the selection of optimal harvest time and to the information management of an orchard. The trellised grapes in the southern part of our country are used as the research object. The soluble solid content (SSC) of four kinds of grapes in growth is detected by using a visible and near infrared spectrophotometer on site. The SSC correction models are established by using Partial Least Square regression (PLS) , Latent Variable and Artificial Neural Network (LV-ANN) and Latent Variable and Support Vector Machine (LV-SVM) respectively. The prediction performance of these models is evaluated by using a validation set. Compared with the PLS and LV-ANN models, the LV-SVM model has the best prediction performance. The experimental result shows that the combination of spectroscopy with the LV-SVM modeling is suitable for the nondestructive SSC detection of living grapes in an orchard.