Abstract :
[en] In tobacco research, the comparison of different tobacco blends as well as the puffdependent
<br />behaviour of cigarettes is a matter of particular interest. For the investigation
<br />of smoke characteristics, GC6GC offers different ways for data analysis,
<br />namely, compound target analysis, automated peak-based compound classification
<br />and comprehensive pixel-based data analysis. This study will show the application
<br />as well as the pros and cons of these types of data analysis for very complex matrices
<br />like cigarette particulate matter. In addition, new aspects about the recently discovered
<br />puff-dependent behaviour of compounds in cigarette smoke will be presented.
<br />Automated peak-based compound classification including mass spectrometric pattern
<br />recognition is used for the classification of tobacco particulate matter samples
<br />and the puff-dependent investigation of different compound classes. This compound
<br />group specific analysis is further reinforced by applying an even more comprehensive
<br />pixel-based analysis. This kind of analysis is used to generate fingerprints of
<br />different types of cigarettes. The combination of fast feature reduction methods like
<br />analysis of variance (ANOVA) and t-test with multivariate feature transformation
<br />methods like partial least squares discriminate analysis (PLSDA) for feature selection
<br />provides a powerful tool for a detailed inspection of different types of cigarettes.
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