Identification of MDA in seized ecstasy-like samples using atmospheric solids analysis probe mass spectrometry and machine learning
[Display omitted] •Data from seized ecstasy-like samples were employed, which were analyzed using a ASAP-MS.•Classification models were developed using LDA with variable selection through the GA, SW and SPA.•SPA emerged as a valuable tool, not only for classification but also for variable selection....
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Veröffentlicht in: | Microchemical journal 2024-10, Vol.205, p.111287, Article 111287 |
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Sprache: | eng |
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•Data from seized ecstasy-like samples were employed, which were analyzed using a ASAP-MS.•Classification models were developed using LDA with variable selection through the GA, SW and SPA.•SPA emerged as a valuable tool, not only for classification but also for variable selection.•ASAP-MS associated a chemometrics technique is a viable method in the analysis of ecstasy tablets.
In this study, a total of 64 samples of ecstasy tablets seized during operations conducted within the State of Rio Grande do Sul – Brazil in 2021–2022 were analyzed. The instrumental analysis was performed using the Radian ASAP MS instrument with a single quadrupole mass spectrometer and an Atmospheric Solids Analysis Probe (ASAP) interface. Data preprocessing was conducted using MATLAB to improve data quality for subsequent chemometric analyses. Classification models seeking to identify samples containing MDA (3,4-methylenedioxyamphetamine) were developed using Linear Discriminant Analysis (LDA) with variable selection through the genetic algorithm (GA), stepwise (SW), and successive projections algorithm (SPA). Additionally, Partial Least Squares Discriminant Analysis (PLS-DA) models were explored. The GA-LDA model achieved an accuracy of approximately 95% for both training and testing sets, demonstrating its effectiveness in distinguishing between sample classes. It selected only five variables during model building. The SW-LDA model displayed exceptional performance in the training set, achieving 100% for all metrics. However, its accuracy for the testing set was slightly lower at 84.7%, suggesting potential overfitting due to the inclusion of numerous variables. The SPA-LDA model, while not the best performer in the testing set, selected variables that were more easily attributed to specific ions/fragments, aiding in the interpretation of results. Interpretation of selected variables revealed key m/z values associated with different compounds found in the samples, such as MDMA, cocaine, and amphetamine-related fragments. Notably, the SPA method selected six variables that played a crucial role in distinguishing between sample groups. These findings contribute to the advancement of forensic drug analysis and quality control processes. |
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ISSN: | 0026-265X |
DOI: | 10.1016/j.microc.2024.111287 |