Qiu-Ye Qian’s scientific contributions

The aim of this study is to evaluate grape wine quality more objectively by reducing the error of traditional grape-wine-quality evaluation. On combining grape wine quality and physicochemical index of grapevine, we provided a grape-wine-quality evaluation model by grapevine's physicochemical index in this study. Firstly, evaluations of the tasters are analyzed, for eliminating the disturbance caused by their individual difference. Then, relationship between grape wine and grapevines are analyzed. Inherent mechanism which affects the grape wine quality was figured out based on description of grape wine quality by physicochemical index of grapevine. Finally, we evaluated the grape wine quality by physicochemical index of grapevine. Additionally, rationality of the model is verified by statistical test while the accuracy of the results is verified by comparison with the evaluating results made by tasters.

Leaf mass, a vital parameter of forest ecology and physiology, is estimated in this study. According to the simulation result in this study, leaf shape makes little contribution to the exposure area. Moreover, a four-layer hidden-node neural network model is used in the new model, analyzing correlation between leaf mass and height of trees. Leaves of various areas are tested as sensitivity analysis for the simulation which studied the influence of leaf shape on exposure area. Finally, leaf mass is estimated by a statistical model based on the regression relationship of sapwood area and leaf mass.