Application of Supporting Vector Regression Model on Simultaneous Determination of Benzoic Acid and Salicylic Acid
Support vector machine (SVM) is based on the principle of structural risk minimization, which makes SVM has better generalization ability than other traditional learning machines that are based on the learning principle of empirical risk minimization. Research on the application of Support vector regression (SVR) model in spectrophotometry was done to determine the content of benzoic acid and salicylic acid simultaneously. The predicted result was found highly correlated with the time when the data was collected to build the model. The closer of the dates between collecting data for modeling and for predicting, the better the predicted results. SVR model with significantly improved robustness was resulted by using all the collected data over time, which, when applied to the determination of benzoic acid and salicylic acid simultaneously, led to satisfactory result, with recoveries being 97％-102％.
Support vector regression Simultaneous analysis Uniform design Structural risk minimization
Jiefang Liu
Department of Mathematics, Henan Institute of Science and Technology,Xinxiang 453003, China
国际会议
2010 International Conference on Advanced Mechanical Engineering(2010年先进机械工程国际学术会议 AME 2010)
洛阳
英文
326-329
2010-09-04（万方平台首次上网日期，不代表论文的发表时间）