摘要
A molecular structural characterization （MSC） method called reduced molecular electronegativity-distance vector （MEDVR） was used to describe the molecular structures of 55 components of meconopsis integrifolia flowers. By use of stepwise multiple regression （SMR） and partial least square （PLS） methods, a model with the correlation coefficient （R1） of 0.987 and the standard deviation （SD1） of 1.377 could be obtained. Then through multiple linear regression （MLR）, another model with the correlation coefficient （R2） of 0.989 and standard deviation （SD2） of 1.395 could be constructed. Furthermore, in virtue of variable screening by the stepwise multiple regression technique （SMR）, 8 vectors were selected to build up another model with its correlation coefficient （R3） and standard deviation （SD3） of 0.989 and 1.366, respectively. Then all the three models were evaluated by performing cross-validation with the leave-one-out （LOO） procedure, and the correlation coefficients （QCV） were 0.981, 0.976 and 0.979, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.
A molecular structural characterization （MSC） method called reduced molecular electronegativity-distance vector （MEDVR） was used to describe the molecular structures of 55 components of meconopsis integrifolia flowers. By use of stepwise multiple regression （SMR） and partial least square （PLS） methods, a model with the correlation coefficient （R1） of 0.987 and the standard deviation （SD1） of 1.377 could be obtained. Then through multiple linear regression （MLR）, another model with the correlation coefficient （R2） of 0.989 and standard deviation （SD2） of 1.395 could be constructed. Furthermore, in virtue of variable screening by the stepwise multiple regression technique （SMR）, 8 vectors were selected to build up another model with its correlation coefficient （R3） and standard deviation （SD3） of 0.989 and 1.366, respectively. Then all the three models were evaluated by performing cross-validation with the leave-one-out （LOO） procedure, and the correlation coefficients （QCV） were 0.981, 0.976 and 0.979, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.
基金
supported by the Foundation of Education Bureau, Sichuan Province (09ZB036)
Technology Bureau, Sichuan Province (2006j13-141)
