CWT-SVR model and its application in NIR analysis of corn

Near-infrared spectroscopy(NIR) analytical technique is simple,fast and low cost,making neither pollution nor damage to the samples,and can determine many components simultaneously.Support vector machine(SVM) is based on the principle of structural risk minimization,which makes SVM a better generalization ability than other traditional learning machines that are based on the learning principle of empirical risk minimization.It is successful in many fields.In this paper,continuous wave transform(CWT) combined with SVM is used in NIR analysis.Compared with Partial Least Squares(PLS) and support vector regression(SVR),it shows that the CWT-SVR model has better forecast accuracy.