Abstract
Alcoholization is an integral part of tobacco processing and volatile compounds are key to assessing tobacco alcoholization. In this study, a total of 154 volatiles from nine categories were determined by gas chromatography-ion mobility spectrometry (GC-IMS) from four grades of tobacco, of which 114 were better identified. And then, the dynamic trends of volatile compounds with significant changes in tobacco alcoholization were analyzed. The relevant volatiles with the alcoholization indices (AIs) (R > 0.8) were screened as indicators of tobacco alcoholization. Cinnamyl isobutyrate, linolenic acid alcohol, propanoic acid-M and propanoic acid-D in all tobacco samples were highly correlated with the AIs and tended to increase during the alcoholization process. In addition, linear discriminant analysis (LDA), back-propagation neural network (BPNN) and random forest (RF) classifiers were constructed for discrimination of tobacco AIs. Three classifiers trained with a combination of 20 volatiles achieved satisfactory results with area under the curve (AUC) of 0.95 (LDA), 0.94 (BPNN) and 0.97 (RF), respectively. The RF classifier gained optimal accuracy of 100 % and 96.1 % for the training and test sets, respectively. The study confirmed that GC-IMS can be used to characterize the changes of volatile compounds in tobacco during alcoholization and combined with machine learning to achieve the determination of AIs. The results of the study may provide a new means for the tobacco industry to monitor the alcoholization process and determine the degree of alcoholization.