Abstract
The quality of tobacco products is directly related to the sensory contributions of related compounds in the mainstream smoke during tobacco combustion. Currently, there is no clear and perfect quantitative calculation method for tobacco quality assessment and prediction. In this paper, samples of Qujing K326 and Dali Hongda were used as raw materials to simulate an actual tobacco smoke release environment. The mainstream smoke products were captured, separated and analyzed by gas chromatography-mass spectrometry (GC-MS). The relative percentage of each compound in mainstream tobacco smoke was obtained. Therefore, through relevant quantitative calculation, this study quantified the sensory contributions of compounds through flue gas separation. Finally, the evaluation quality of tobacco samples could be predicted accurately by neural network model. The methods used in this paper can provide important technical support for tobacco quality control.
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