Identification of Producing Area of Tobacco Leaf Based on Spectrometric Analysis and LS-SVM

In order to investigate a fast and efficient method determining the producing area of tobacco leaf, near-infrared reflectance spectroscopy with least squares-support vector machines (LS-SVM) was applied to determine producing area of tobacco leaf. Three producing areas including Yunnan, Hubei and Henan were as the research objects. As the pretreatments of the optimal smoothing way, moving average with three segments and multiplication scatter correction (MSC) were applied to reduce the noise of the spectra. After the principle component analysis of the spectra from 1101 to 2395 nm, 4 to 12 principal components (PCs) were chosen as the inputs of LS-SVM models. Results show that the prediction performance of the LS-SVM model with 12 PCs is better than partial least square (PLS) model. Its correlation coefficient of prediction set (rp) is 0.9907, standard error of prediction (SEP) is 1.7551, and root mean square error of prediction (RMSEP) is 1.7373. It is concluded that NIR spectroscopy with LS-SVM is a feasible method to determine the producing area of tobacco leaf.