Benefits of derivatization in GC–MS-based identification of new psychoactive substances

Kranenburg, Ruben F.; Verduin, Joshka; Stuyver, Laura I.; Ridder, Renee de; Beek, Annique van; Colmsee, Erik; Asten, Arian C. van

doi:10.1016/j.forc.2020.100273

Cited by 39 publications

(21 citation statements)

References 34 publications

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“…GC-MS has been considered a powerful analytical instrument to identify the chemical. However, time-consuming, complex sample pretreatment, and a significant constraint in the distinguishing of isomeric molecules, particularly ring-isomeric compounds ( Kranenburg et al, 2020 ) limit its application for the plant-based products screening. GC-O-MS is a powerful tool for extracting aroma-active compounds from the complex mixtures, because of repetitive time-consuming labor, this method is not ideal for the rapid detection of volatile organic compounds (VOCs) in the plant-based products ( Wang et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis and Development of a Flavor Fingerprint for Volatile Compounds of Vegetable Soybean Seeds Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry

Yuan

et al. 2021

Front. Plant Sci.

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Evaluating the volatile compounds and characteristic fingerprints of the core cultivars of vegetable soybean would provide useful data for improving their aroma in the breeding programs. The present study used headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to evaluate the volatile compounds of vegetable soybean seeds at a specific growth stage. In total, 93 signal peaks were identified, 63 compounds qualitatively, with 14 volatile flavor compounds providing multiple signals. The 63 volatile compounds consisted of 15 esters, 15 aldehydes, 13 alcohols, 15 ketones, one acid, and four other compounds. The peak intensity of most of the volatile compounds varied greatly between the core cultivars. The alcohols and aldehydes determined the basic volatile flavor of the vegetable soybean seeds. Volatile flavors were determined by their respective esters, ketones, or other components. Characteristic fingerprints were found in some core vegetable soybean cultivars. Four cultivars (Xiangdou, ZHE1754, Zhexian 65018-33, and Qvxian No. 1) had pleasant aromas, because of their higher content of 2-acetyl-1-pyrroline (2-AP). A principal component analysis (PCA) was used to distinguish the samples based on the signal intensity of their volatile components. The results showed that the composition and concentration of volatile compounds differed greatly between the core cultivars, with the volatile flavor compounds of soybeans being determined by the ecotype of the cultivar, the direction of breeding selection, and their geographical origin. Characteristic fingerprints of the cultivars were established by HS-GC-IMS, enabling them to be used to describe and distinguish cultivars and their offspring in future breeding studies.

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Section: Discussionmentioning

confidence: 99%

Comparative Analysis and Development of a Flavor Fingerprint for Volatile Compounds of Vegetable Soybean Seeds Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry

Yuan

et al. 2021

Front. Plant Sci.

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“…GC-MS results used for comparison were obtained from sample solutions in dichloromethane analyzed in full-scan MS mode on a single quadrupole instrument using the laboratory's validated methods described elsewhere. 32,33 Fourier transform infrared (FTIR) analyses were performed on a benchtop Spectrum Two spectrometer with ATR option from PerkinElmer (Waltham, MA) using a scan range of 400-1,400 cm −1 .…”

Section: Reference Analysismentioning

confidence: 99%

Performance evaluation of handheld Raman spectroscopy for cocaine detection in forensic case samples

Kranenburg

Verduin

Ridder³

et al. 2021

Drug Testing and Analysis

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Handheld Raman spectroscopy is an emerging technique for rapid on-site detection of drugs of abuse. Most devices are developed for on-scene operation with a user interface that only shows whether cocaine has been detected. Extensive validation studies are unavailable, and so are typically the insight in raw spectral data and the identification criteria. This work evaluates the performance of a commercial handheld Raman spectrometer for cocaine detection based on (i) its performance on 0-100 wt% binary cocaine mixtures, (ii) retrospective comparison of 3,168 case samples from 2015 to 2020 analyzed by both gas chromatography-mass spectrometry (GC-MS) and Raman, (iii) assessment of spectral selectivity, and (iv) comparison of the instrument's on-screen results with combined partial least square regression (PLS-R) and discriminant analysis (PLS-DA) models. The limit of detection was dependent on sample composition and varied between 10 wt% and 40 wt% cocaine. Because the average cocaine content in street samples is well above this limit, a 97.5% true positive rate was observed in case samples. No cocaine false positives were reported, although 12.5% of the negative samples were initially reported as inconclusive by the built-in software. The spectral assessment showed high selectivity for Raman peaks at 1,712 (cocaine base) and 1,716 cm −1 (cocaine HCl). Combined PLS-R and PLS-DA models using these features confirmed and further improved instrument performance. This study scientifically assessed the performance of a commercial Raman spectrometer, providing useful insight on its applicability for both presumptive detection and legally valid evidence of cocaine presence for law enforcement.

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“… 9 Since GC-MS is so commonly utilized in the testing of novel psychoactive substances in forensic laboratories, the analysis of these data has been improved using chemometric analysis as a means of enhancing the technique’s isomeric differentiation capabilities. 5 , 10 − 19 Some of these studies used a univariate approach where each m / z fragment is analyzed separately with an unequal variance t test. 10 − 12 Other studies have seen success with a multivariate approach such as principal component analysis (PCA) followed by linear discriminant analysis (LDA), 5 , 13 LDA with different variable selection techniques, 14 hierarchal clustering analysis, 15 and canonical discriminant analysis.…”

Section: Introductionmentioning

confidence: 99%

“… 16 , 17 Another study used PCA solely as a means to highlight which m / z ions to further investigate for differentiation. 18 Despite the success of these currently available techniques, many require significant analysis time, either through additional sample preparation such as derivatization 19 or simply the more substantial time required for a typical GC analysis, particularly since a blank, sample, and standard would each require a separate injection. In contrast, the minimal sample preparation required for Direct Analysis in Real Time–Time-of-Flight mass spectrometry (DART-ToF) in combination with its rapid analysis time make it an underutilized resource for tackling the NPS challenge.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Machine Learning for the Differentiation of Positional NPS Isomers with Direct Analysis in Real Time Mass Spectrometry

Bonetti

Samanipour

Asten³

2022

Anal. Chem.

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The differentiation of positional isomers is a well established analytical challenge for forensic laboratories. As more novel psychoactive substances (NPSs) are introduced to the illicit drug market, robust yet efficient methods of isomer identification are needed. Although current literature suggests that Direct Analysis in Real Time–Time-of-Flight mass spectrometry (DART-ToF) with in-source collision induced dissociation (is-CID) can be used to differentiate positional isomers, it is currently unclear whether this capability extends to positional isomers whose only structural difference is the precise location of a single substitution on an aromatic ring. The aim of this work was to determine whether chemometric analysis of DART-ToF data could offer forensic laboratories an alternative rapid and robust method of differentiating NPS positional ring isomers. To test the feasibility of this technique, three positional isomer sets (fluoroamphetamine, fluoromethamphetamine, and methylmethcathinone) were analyzed. Using a linear rail for consistent sample introduction, the three isomers of each type were analyzed 96 times over an eight-week timespan. The classification methods investigated included a univariate approach, the Welch t test at each included ion; a multivariate approach, linear discriminant analysis; and a machine learning approach, the Random Forest classifier. For each method, multiple validation techniques were used including restricting the classifier to data that was only generated on one day. Of these classification methods, the Random Forest algorithm was ultimately the most accurate and robust, consistently achieving out-of-bag error rates below 5%. At an inconclusive rate of approximately 5%, a success rate of 100% was obtained for isomer identification when applied to a randomly selected test set. The model was further tested with data acquired as a part of a different batch. The highest classification success rate was 93.9%, and error rates under 5% were consistently achieved.

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Benefits of derivatization in GC–MS-based identification of new psychoactive substances

Cited by 39 publications

References 34 publications

Comparative Analysis and Development of a Flavor Fingerprint for Volatile Compounds of Vegetable Soybean Seeds Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry

Comparative Analysis and Development of a Flavor Fingerprint for Volatile Compounds of Vegetable Soybean Seeds Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry

Performance evaluation of handheld Raman spectroscopy for cocaine detection in forensic case samples

Utilization of Machine Learning for the Differentiation of Positional NPS Isomers with Direct Analysis in Real Time Mass Spectrometry

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