AbstractIn order to judge the quality of tobacco leaf, it is necessary to conduct sensory smoke evaluations. However, these are subjective and the results are difficult to quantify. Therefore, we have attempted to establish a quantitative method for evaluating tobacco quality by comparing results of headspace analysis. Forty-seven leaf samples of different types (flue-cured, Burley, Oriental) were analyzed. The first step in this study was to have a panel of experts smoke cigarettes made from the test tobaccos and have them evaluate 10 sensory attributes. The scores were then analyzed by the technique of principal component analysis (PCA). Results showed that the score for the flavor note attribute indicated the type of tobacco and the scores of the other 9 attributes were combined as a total to indicate smoking quality. Following the sensory study, headspace vapors of the test tobaccos were analyzed with a headspace sampler, gas chromatography, mass spectroscopy system (HS-GC-MS), in which the gas sampling loop and the HS-GC transfer line were deactivated. In order to obtain conditions for good reproducibility, the heating temperature and time of the headspace vials were examined. PCA was performed for the headspace vapor (HSV) analysis results for 31 selected peaks. The first and second principal components could be used to classify tobacco types. The third principal component partially indicated differences of smoking qualities. Finally, multiple regression analysis was performed on the HSV analysis results in order to estimate the smoking quality scores. The regression model of all samples combined had a low regression coefficient. Then, we separated the results of the three tobacco types, as we considered that the headspace data might reveal information about the classifications themselves. The final outcome was a regression model that could be applied to each type with a higher accuracy. The variables that entered the models were compared.
Methylmercury determination in sub-ppb level by cold vapor analysis: facts, mechanisms and optimization
