Characterization of Tobacco Leaves by Near-Infrared Reflectance Spectroscopy and Electronic Nose with Support Vector Machine

Abstract

ABSTRACTThe determination of different regions of tobacco leaves is vital in the tobacco industry. Different parts of tobacco leaves produce varying flavors due to the different chemical compositions. Here, near infrared spectroscopy and electronic nose were combined with support vector machine to predict the parts of tobacco leaves. Comparing to the single data model as near infrared spectroscopy with support vector machine or electronic nose with support vector machine, near infrared spectroscopy and electronic nose with support vector machine model show higher accuracy. The accuracy of near infrared spectroscopy and electronic nose with support vector machine model is 95.31%, while the accuracy of leave-one-out cross-validation is 79.69%. The optimal model was then applied to 60 unknown tobacco samples from different parts of tobacco leaves to test its accuracy, which is 81.67%.