Comparative Analysis of the Chemical Profiles of 3,4‐Methylenedioxymethamphetamine Based on Comprehensive Two‐Dimensional Gas Chromatography–Time‐of‐Flight Mass Spectrometry (GC × GC‐TOFMS)*

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.

Unsupervised classification of petroleum Certified Reference Materials and other fuels by chemometric analysis of gas chromatography-mass spectrometry data

“…The spectra generated contain a wealth of data about the composition of the fuel. Multivariate analysis methods can help to extract information from the GC-MS [3-6] and GCxGC-TOFMS [7-9] results, and these methods can be used for rapid analysis of fuels [10-13]. …”

Translation of a One-Dimensional to a Comprehensive Two-Dimensional Gas Chromatography Method with Dual-Channel Detection for Volatile Organic Compound Measurement in Forensic Applications

“…Despite numerous advantages in comparison to one-dimensional (1D) gas chromatography (GC), such as increased peak capacity, higher sensitivity, and the possibility of group-type analysis, its application in the forensic sciences remains limited . Several publications have emphasized the benefits of the use of GC×GC in the fields of illicit drugs, , forensic toxicology, fire debris analysis, forensic taphonomy, − forensic environmental investigations, and chemical, biological, radioactive, nuclear, and explosive (CBRNE) agents …”

Control-Normalized Fisher Ratio Analysis of Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry Data for Enhanced Biomarker Discovery in a Metabolomic Study of Orthopedic Knee-Ligament Injury

“…Application of GC × GC–TOFMS to highly complex samples is often limited by the availability of chemometric tools to handle the challenging data analysis expectations; thus, there is a significant effort to address this challenge. ,− Many chemometric software tools rely upon identifying and quantifying analytes of interest in either a targeted or nontargeted fashion, where nontargeted implies that not all analytes of interest are known a priori. ,, Nontargeted “discovery” based methods aim to expose meaningful class-distinguishing analyte features with limited manual intervention and interpretation necessary. Nontargeted “discovery” methods are further classified as either supervised or unsupervised, depending upon if class membership is known a priori. , With unsupervised discovery, class membership information is not known a priori.…”

“…Normalization by the pooled within-class variance produces an F-ratio that is not biased toward higher intensity chromatographic peaks. Correspondingly, FRA has been successfully applied as a supervised discovery-based tool with GC × GC–TOFMS data sets for a variety of applications such as fuel, ,, forensics, ,,, and metabolomics. ,, However, retention time misalignment between chromatograms inflates the false positive rate, and features falsely identified as class-distinguishing are intermingled among true positives, which unnecessarily burdens the data analysis work flow, which includes a top-down mining approach to identify and quantify analytes following FRA. , The presence of false positives due to retention time misalignment hinders the identification and quantification of true positives that are pushed down the hit list by false positives. Also, when top-down data mining is applied, the presence of a high frequency of false positives can trigger a (premature) stopping point in the effort, and true positives may not be discovered.…”

“…41 Normalization by the pooled within-class variance produces an F-ratio that is not biased toward higher intensity chromatographic peaks. Correspondingly, FRA has been successfully applied as a supervised discovery-based tool with GC × GC−TOFMS data sets for a variety of applications such as fuel, 6,11,35 forensics, 36,40,42,43 and metabolomics. 24,26,34 However, retention time misalignment between chromatograms inflates the false positive rate, and features falsely identified as class-distinguishing are intermingled among true positives, which unnecessarily burdens the data analysis work flow, which includes a top-down mining approach to identify and quantify analytes following FRA.…”