Prediction Model of Nicotine and Glycerol in Reconstituted Tobacco Leaves Based on Support Vector Machine Algorithm

Abstract

Nicotine and glycerol are the two important indexes of reconstituted tobacco and they determine the quality of the reconstituted tobacco. A hand-held near infrared spectrometer was used to collect the spectral data of reconstituted tobacco leaves, and three algorithms of principal component regression, partial least squares and support vector machine were used to build the prediction model of the nicotine and glycerol content in reconstituted tobacco leaves. The experimental results showed that the support vector machine algorithm could achieve the best prediction results compared with principal component regression and partial least squares algorithms. The proposed method can rapidly determine the nicotine and glycerol content of the reconstituted tobacco leaves and it provides a new technical reference for improving the quality of new tobacco.