Application of comprehensive two‐dimensional gas chromatography mass spectrometry and different types of data analysis for the investigation of cigarette particulate matter

Abstract

Application of comprehensive two-dimensional gas chromatography mass spectrometry and different types of data analysis for the investigation of cigarette particulate matterIn tobacco research, the comparison of different tobacco blends as well as the puffdependent behaviour of cigarettes is a matter of particular interest. For the investigation of smoke characteristics, GC6GC 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.

Qualitative and quantitative approaches in comprehensive two-dimensional gas chromatography

“…Groeger et al used partial least squares discriminate analysis (PLSDA) to determine the differences between different brands of cigarettes [54]. McGregor et al used GC × GC fingerprinting analysis and PCA score plots to compare coal tar from different former manufacturing gas plants [56].…”

“…Chemometrics uses mathematical techniques to extract useful information from datasets. As seen in Section 3.5, chemometric techniques, such as principle component analysis (PCA) and component discriminate analysis (PCDA), have been used to find similarities and differences between samples [54][55][56][57][58]. Chemometrics techniques that simultaneously deconvolute and quantitate have been reported in GC × GC such as the generalized rank annihilation method (GRAM) and parallel factor analysis (PARAFAC) [48,[59][60][61][62].…”

“…This can be accomplished by the comparison of entire chromatograms. Fingerprinting analysis has been found useful in many applications, including petrochemical [100], forensic [101], environmental [54], food and fragrance [102], and metabolomics [55]. It is important to have a high peak capacity for fingerprinting analysis since the entire chromatogram is used for the analysis and each peak can be potentially relevant.…”

Comprehensive two dimensional gas chromatography review

“…Groger et al [98] successfully employed GC6GC for compound target analysis, automated peak-based compound classification and comprehensive pixel-based data analysis for the characterization of different tobacco blends as well as the puff dependent behavior of cigarettes.…”

“…To evaluate the resulting classification of samples with A4000 components they found bubble plots to be very helpful in visualizing the results of classification where different symbols were used for different compound classes. Groger et al [127] used the previously mentioned compound classification rules to characterize cigarette smoke. Partial least squares-discriminent analysis was used to fingerprint tobacco smoke.…”