Application of SVR in NIR Prediction Model for Starch in Tobacco

In order to test the applicability of support vector regression (SVR) in near-infrared (NIR) spectral analysis of tobacco, NIR calibration model for starch in tobacco were developed by processing NIR spectra and chemically determined starch content data of 187 tobacco samples with SVR, partial least square regression (PLS), multiplicative linear regression (MLR) and error back propagation artificial neural network (BP-ANN). The obtained model was tested through internal validation and external validation with leave-one-out cross validation (LOOCV) and independent sample set. The results showed that the accuracy of SVR model was slightly higher than that of BP-ANN, PLS and MLR models, it implied that SVR algorithm could be applied to NIR analysis of starch in tobacco.