Study on Discrimination Model of Alcoholized Cigar Tobacco Varieties Based on Near Infrared Spec-troscopy
The aim of this study was to improve the discrimination accuracy of different post-alcoholised cigar tobacco varieties,pre-processing algorithms such as multiple scattering correction were used to denoise the spectral data in order to reduce the influence of experimental,environmental and instrumental noise on the data.Support vector machines,BP neural networks and random forests were combined to establish near-infrared spectral discrimination models for different varieties.The model performance was evaluated by ac-curacy and confusion matrix.The results showed that the model built with SNV+FD preprocessing algorithm and CARS feature wave-length selection algorithm was the most effective,and showed high accuracy in both training and prediction sets,which confirmed the feasibility of the use of NIR spectroscopy to quickly discriminate different varieties of post-alcoholisation cigar tobacco leaves.In sum-mary,the use of near infrared spectroscopy could realize the non-destructive and rapid discrimination of different varieties of alco-holized cigar tobacco,and further improve the industrial availability of cigar tobacco.
国家科技期刊平台
NSTL
万方数据
维普
