ABSTRACT
The chemical profiling of illicit drugs is an important analytical tool to support the work of investigating and law enforcement authorities. In our work, comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) combined with nontargeted, pixel-based data analysis was adapted for the chemical profiling of 3,4-methylenedioxymethamphetamine (MDMA). The validity and benefit of this approach was evaluated by analyzing a well-investigated set of MDMA samples. Samples were prepared according to a harmonized extraction protocol to ensure the comparability of the chemical signatures. The nontargeted approach comprises preprocessing followed by analysis of variances as a fast filter algorithm for selection of a variable subset followed by partial least squares discriminant analysis for reduction to promising marker compounds for discrimination of the samples according to their chemical profile. Forty-seven potential marker compounds were determined, covering most of the target impurities known from the harmonized one-dimensional profiling as well as other compounds not previously elucidated.
ABSTRACT
In tobacco research, the comparison of different tobacco blends as well as the puff-dependent behaviour of cigarettes is a matter of particular interest. For the investigation of smoke characteristics, GC x GC offers different ways for data analysis, namely, compound target analysis, automated peak-based compound classification and comprehensive pixel-based data analysis. This study will show the application as well as the pros and cons of these types of data analysis for very complex matrices like cigarette particulate matter. In addition, new aspects about the recently discovered puff-dependent behaviour of compounds in cigarette smoke will be presented. Automated peak-based compound classification including mass spectrometric pattern recognition is used for the classification of tobacco particulate matter samples and the puff-dependent investigation of different compound classes. This compound group specific analysis is further reinforced by applying an even more comprehensive pixel-based analysis. This kind of analysis is used to generate fingerprints of different types of cigarettes. The combination of fast feature reduction methods like analysis of variance (ANOVA) and t-test with multivariate feature transformation methods like partial least squares discriminate analysis (PLSDA) for feature selection provides a powerful tool for a detailed inspection of different types of cigarettes.
