Application of High Dimensional Feature Grouping Method in Near-Infrared Spectra of Identification of Tobacco Growing Areas

Broad learning system with Takagi–Sugeno fuzzy subsystem for tobacco origin identification based on near infrared spectroscopy

“…NIR spectroscopy combined with other soft computing methods has been widely applied for tobacco origin identification. Zhu et al used high dimensional feature grouping method in NIR spectra to identify tobacco growing area [17]. Wang et al collected twelve hundred seventy six superior tobacco leaf from four producing areas, which are Yuxi, Chuxiong, Zhaotong and Dali in Yunnan province, and applied NIR spectrum projection and correlation methods for tobacco quality analysis of different producing areas [18].…”

Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors

“…A lot of research works have been conducted on the classification of tobacco cultivation regions with different algorithms using near-infrared (NIR) sensors. Zhu, Gong, Li, and Yu [ 13 ] identified the cultivation regions of tobacco with the high dimensional feature grouping method, which means they sorted all NIR spectra features according to importance scores of the features from small to large and then divided them into twelve groups, and they made the feature selection to get the optimal feature subset with different feature groups by calculating the error rate. Zhang, He, and Ye [ 14 ] proposed the least square support vector machines (LS-SVM) to determine the tobacco producing area using the NIR sensor with the wavelength range from 1101 to 2395 nm.…”