Discrimination of opium from Afghanistan and Myanmar by infrared spectroscopy coupled with machine learning methods.

Afghanistan and Myanmar are two overwhelming opium production places. In this study, rapid and efficient methods for distinguishing opium from Afghanistan and Myanmar were developed using infrared spectroscopy (IR) coupled with multiple machine learning (ML) methods for the first time. A total of 146 authentic opium samples were analyzed by mid-IR (MIR) and near-IR (NIR), within them 116 were used for model training and 30 were used for model validation. Six ML methods, including partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (OPLS-DA), k-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), and artificial neural networks (ANNs) were constructed and compared to get the best classification effect. For MIR data, the average of precision, recall and f1-score for all classification models were 1.0. For NIR data, the average of precision, recall and f1-score for different classification models ranged from 0.90 to 0.94. The comparison results of six ML models for MIR and NIR data showed that MIR was more suitable for opium geography classification. Compared with traditional chromatography and mass spectrometry profiling methods, the advantages of MIR are simple, rapid, cost-effective, and environmentally friendly. The developed IR chemical profiling methodology may find wide application in classification of opium from Afghanistan and Myanmar, and also to differentiate them from opium originating from other opium producing countries. This study presented new insights into the application of IR and ML to rapid drug profiling analysis.

Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances.

Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended on the similar chemical structures and pharmacological action with known drugs. Such approaches could not face the complicated circumstance of emerging new psychoactive substances. Herein, mass spectrometry imaging and convolutional neural networks (CNN) were used for preliminary screening and pre-classification of suspected psychoactive substances. Mass spectrometry imaging was performed simultaneously on two brain slices as one was from blank group and another one was from psychoactive substance-induced group. Then, fused neurotransmitter variation mass spectrometry images (Nv-MSIs) reflecting the difference of neurotransmitters between two slices were achieved through two homemade programs. A CNN model was developed to classify the Nv-MSIs. Compared with traditional classification methods, CNN achieved better estimation accuracy and required minimal data preprocessing. Also, the specific region on Nv-MSIs and weight of each neurotransmitter that affected the classification most could be unraveled by CNN. Finally, the method was successfully applied to assist the identification of a new psychoactive substance seized recently. This sample was identified as cannabinoids, which greatly promoted the screening process.

Metabolism of four novel structural analogs of ketamine, 2-FXE [2-(ethylamino)-2-(2-fluorophenyl) cyclohexan-1-one], 2-MDCK [2-(methylamino)-2-(o-tolyl) cyclohexan-1-one], 3-DMXE [2-(ethylamino)-2-(m-tolyl) cyclohexan-1-one], and 2-DMXE [2-(ethylamino)-2-(o-tolyl) cyclohexan-1-one], in human liver microsomes based on ultra-performance liquid chromatography-high-resolution tandem mass spectrometry.

New psychoactive substances are constantly emerging, among which ketamine analogs with the core structure of 2-amino-2-phenylcyclohexanone have attracted global attention due to their continued involvement in acute intoxications. The monitoring of these substances largely relies on the acquisition of metabolic data. However, the lack of in vitro human metabolism information for these emerging structural analogs presents significant challenges to drug control efforts. To address this challenge, we investigated the first-phase metabolism patterns of four novel ketamine structural analogs of 2-FXE [2-(ethylamino)-2-(2-fluorophenyl) cyclohexan-1-one], 2-MDCK [2-(methylamino)-2-(o-tolyl) cyclohexan-1-one], 3-DMXE [2-(ethylamino)-2-(m-tolyl) cyclohexan-1-one], and 2-DMXE [2-(ethylamino)-2-(o-tolyl) cyclohexan-1-one] utilizing human liver microsomes for the first time. Metabolites were identified using ultra-performance liquid chromatography coupled with high-resolution tandem mass spectrometry. Our findings reveal that N-dealkylation and hydroxylation are the primary metabolic reactions, alongside other notable reactions, including oxidation, reduction, and dehydration. Based on our extensive research, we propose N-dealkylation and hydroxylation metabolites as appropriate analytical markers for monitoring the consumption of these substances.

Geographical classification of opium samples from Myanmar and Afghanistan by NMR profiling and chemometrics.

This study presents a new strategy to discriminate between opium samples obtained from different geographical regions. Nuclear magnetic resonance (NMR) profiling and chemometrics were applied to geographical classification of opium originating from Myanmar and Afghanistan, which are two major opium producing countries in the world. A total of 50 Myanmar and 46 Afghanistan authentic opium samples were analyzed by 1 H-NMR, and the chemical profiles were characterized. Different sample preparation procedures, data processing methods, and chemometrics were compared to obtain the best classification effect. It was found that drying and the addition of buffer solutions were unnecessary for classification purposes; thus, the gum opium samples were extracted directly with CD3 OD, which shortened sample preparation time. A full discrimination between the two geographical origins was achieved by 1 H-NMR profiling and orthogonal partial least squares discriminant analysis. All 30 opium samples were classified correctly by the developed orthogonal partial least squares discriminant analysis model. Compared with traditional chromatography and mass spectrometry profiling methods, the 1 H-NMR profiling method was faster (with instrument analysis time of less than 3 min) and reproducible. This study provides new insights into the applying of NMR profiling and chemometrics to rapid drug profiling analysis.

Qualitative and Quantitative Analysis of Five Indoles or Indazole Amide Synthetic Cannabinoids in Suspected E-Cigarette Oil by GC-MS.

OBJECTIVES: To establish the GC-MS qualitative and quantitative analysis methods for the synthetic cannabinoids, its main matrix and additives in suspicious electronic cigarette (e-cigarette) oil samples. METHODS: The e-cigarette oil samples were analyzed by GC-MS after diluted with methanol. Synthetic cannabinoids, its main matrix and additives in e-cigarette oil samples were qualitatively analyzed by the characteristic fragment ions and retention time. The synthetic cannabinoids were quantitatively analyzed by using the selective ion monitoring mode. RESULTS: The linear range of each compound in GC-MS quantitative method was 0.025-1 mg/mL, the matrix recovery rate was 94%-103%, the intra-day precision relative standard deviations (RSD) was less than 2.5%, and inter-day precision RSD was less than 4.0%. Five indoles or indazole amide synthetic cannabinoids were detected in 25 e-cigarette samples. The main matrixes of e-cigarette samples were propylene glycol and glycerol. Additives such as N,2,3-trimethyl-2-isopropyl butanamide (WS-23), glycerol triacetate and nicotine were detected in some samples. The content range of synthetic cannabinoids in 25 e-cigarette samples was 0.05%-2.74%. CONCLUSIONS: The GC-MS method for synthesizing cannabinoid, matrix and additive in e-cigarette oil samples has good selectivity, high resolution, low detection limit, and can be used for simultaneous qualitative and quantitative analysis of multiple components; The explored fragment ion fragmentation mechanism of the electron bombardment ion source of indole or indoxamide compounds helps to identify such substances or other compounds with similar structures in cases.

Preparation of poly(methacrylic acid-co-ethylene glycol dimethacrylate)-functionalized magnetic polydopamine nanoparticles for the extraction of six cannabinoids in wastewater followed by UHPLC-MS/MS.

Phytocannabinoids and their synthetic analogs (natural and synthetic cannabinoids) are illicit drugs that are widely abused worldwide. Wastewater-based epidemiology (WBE) is an objective approach for the estimation of population-level exposure to a wide range of substances, especially drugs of abuse. However, the concentrations of cannabinoids in wastewater are extremely low (frequently at the levels of nanograms per liter), and the existing pretreatment procedures for wastewater have the disadvantages of time-consumption or low extraction recoveries. This study aimed to propose a novel poly (methacrylic acid-co-ethylene glycol dimethacrylate)-functionalized polydopamine-coated Fe3O4 nanoparticle (Fe3O4@PDA@poly (MAA-co-EGDMA)) as an adsorbent, and provide a highly sensitive quantitative analytical technique for the detection of five synthetic cannabinoids (SCs: 5 F-EDMB-PINACA, FUB-APINACA, MDMB-4en-PINACA, MDMB-FUBINACA, and PB-22) and one cannabis-related human metabolite (THC-COOH) in wastewater. The magnetic adsorbents were fully characterized by transmission electron microscopy (TEM), infrared spectroscopy (IR), vibrating sample magnetometry (VSM) and X-ray photoelectron spectroscopy (XPS). Subsequently, an MSPE-UHPLC-MS/MS method was developed and validated for the determination of six trace analytes in wastewater. The validation results showed that the method has limits of quantification as low as 0.1-1.0 ng/L. Additionally, the recoveries ranged from 62.81 to 124.02%, and the relative standard deviations (RSDs) of intraday and interday precision were less than 15%. This MSPE-UHPLC-MS/MS method was successfully applied to real wastewater samples, and the whole analytical process of one sample from pretreatment to the obtained quantitative results was completed in less than 30 min. Thus, the proposed method based on Fe3O4@PDA@poly (MAA-co-EGDMA) is a convenient, rapid, sensitive and reliable method for the determination of trace psychoactive drugs in wastewater.

ATR-FTIR combined with machine learning for the fast non-targeted screening of new psychoactive substances.

Due to the diversity and fast evolution of new psychoactive substances (NPS), both public health and safety are threatened around the world. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), which serves as a simple and rapid technique for targeted NPS screening, is challenging with the rapid structural modifications of NPS. To achieve the fast non-targeted screening of NPS, six machine learning (ML) models were constructed to classify eight categories of NPS, including synthetic cannabinoids, synthetic cathinones, phenethylamines, fentanyl analogues, tryptamines, phencyclidine types, benzodiazepines, and "other substances" based on the 1099 IR spectra data items of 362 types of NPS collected by one desktop ATR-FTIR and two portable FTIR spectrometers. All these six ML classification models, including k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), extra trees (ET), voting, and artificial neural networks (ANNs) were trained through cross validation, and f1-scores of 0.87-1.00 were achieved. In addition, hierarchical cluster analysis (HCA) was performed on 100 synthetic cannabinoids with the most complex structural variation to investigate the structure-spectral property relationship, which leads to a summary of eight synthetic cannabinoid sub-categories with different "linked groups". ML models were also constructed to classify eight synthetic cannabinoid sub-categories. For the first time, this study developed six ML models, which were suitable for both desktop and portable spectrometers, to classify eight categories of NPS and eight synthetic cannabinoids sub-categories. These models can be applied for the fast, accurate, cost-effective, and on-site non-targeted screening of newly emerging NPS with no reference data available.

A comprehensive analytical strategy based on characteristic fragments to detect synthetic cannabinoid analogs in seized products and hair samples.

Synthetic cannabinoids, one of the most widely abused new psychoactive substances (NPS), are now placed under national control generally in China. Due to continuous modification of synthetic cannabinoid structure, an ongoing dilemma in the forensic laboratory is that newly emerging substances cannot be detected by established methods. Thus, the screening methods for simultaneous detection of known or unknown substances have become research hotspots. In this study, the ultra high performance liquid chromatography tandem triple quadrupole mass spectrometry (UPLC-QqQ-MS) with precursor ion scan (PIS) as acquisition mode was used for prescreening purposes of all possible synthetic cannabinoids-related substances. In detail, four common characteristic fragments, m/z of 144.0, 145.0, 135.1, and 109.0 corresponding to acylium-indole, acylium-indazole, adamantyl, and fluorobenzyl cation respectively, were selected for PIS mode, and their collision energies were optimized by 97 available synthetic cannabinoids standards with relevant structures. Those suspicious signals observed in the screening experiment were confirmed by ultra high performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) via high-resolution MS and MS2 data obtained by full scan (TOF MS) and product ion scan mode. After methodological validation, the integrated strategy established above was applied to the screening and identification of the seized e-liquids, herbal blends and hair samples, confirming the presence of multiple synthetic cannabinoids in these samples. In particular, a novel synthetic cannabinoid was identified as 4 F-ABUTINACA, for which no relevant high-resolution mass spectrometry (HRMS) data has been retrieved until now, making this study the first to report the cleavage pattern of this compound in electrospray ionization (ESI) mass spectrometry. In addition, four other suspected by-products of the synthetic cannabinoids were found in the herbal blends and e-liquids, and their possible structures were also deduced via the information from high-resolution mass spectra.

Cucurbituril-protected dual-readout gold nanoclusters for sensitive fentanyl detection.

A large number of cases showed that fentanyl (FEN) has become the main cause of death from illegal drug overdose owing to its potent effect on respiratory depression, which has emerged as a grave threat to public health and safety. However, traditional analytical methods require cost-prohibitive equipment, complex pretreatment procedures, and technically trained experts, thus highlighting the urgent need to develop a cost-effective, straightforward, and highly sensitive method to detect FEN. This work demonstrated a dual-readout sensor FGGC-AuNCs@Q7 for FEN detection, which is based on the molecular recognition and self-assembly between the macrocycle cucurbit[7]uril (Q7) and FEN, accompanying spontaneous visual Tyndall effect and fluorescence optical responses of the gold nanoclusters within seconds. A detection limit of 1 ng mL-1 and a linear range of 9 to 148 000 ng mL-1 were achieved for fluorescence detection on FEN, with favorable selectivity in the presence of other illicit drugs or common interferents. The proposed method has been proved by its satisfactory application for the analysis of human urine.

Machine Learning-Assisted Rapid Screening of Four Types of New Psychoactive Substances in Drug Seizures.

Over the past few years, new psychoactive substances (NPS) have become a global health and social problem because of their wide variety, constant structural renewal, vague legal definitions, and rapid adaptation to legal restrictions. The rapid structural modifications of NPS have posed significant challenges for the screening and identification of these new substances using traditional mass spectrometric techniques based on reference substances or a mass spectral database. Here, we propose supervised machine learning (ML) classification models such as k-nearest neighbors, support vector machine, random forest, and multigrained cascade forest for the rapid screening of NPS using mass spectrometric data. This approach utilizes ML methods to learn the statistical probability distributions of mass spectral data for NPS and non-NPS. Four classification ML models were generated and evaluated using a data set comprising 567 LC-MS and 732 GC-MS spectra. Through cross validation, we achieved an F1 score of 0.35-0.97. These algorithms were applied in conjunction with mass spectrometry techniques for the detection of six seizures including electronic cigarette oil and suspected powdered substances netted in drug trafficking cases. The models provided warning signals for synthetic cannabinoids, synthetic cathinones, and fentanyl. Thus, an early warning system was successfully established, which provided a useful method for reliable and effective identifications of unknown NPS.

New trends of new psychoactive substances (NPS)-infused chocolate: Identification and quantification of trace level of NPS in complex matrix by GC-MS and NMR.

For the first time, the identification and quantification of trace level of new psychoactive substances (NPS) in a complex chocolate matrix have been reported. Since the beginning of 2022, suspected NPS-infused chocolate samples confiscated in inbound packages have been continuously sent to our laboratory for analysis. The qualitative gas chromatography-mass spectrometry (GC-MS) results were verified by 1H nuclear magnetic resonance (1H NMR) and 19F NMR to distinguish between potential aromatic isomers. A total of 11 NPS including deoxymethoxetamine, 3-OH-PCP, 6-APB, 4-APB, 4-OH-MiPT, 3-FEA, 2-FEA, 3-MMC, bromazolam, 2-FDCK, and ADB-BUTINACA were detected in 65 seized chocolate samples. A general 1H quantitative NMR (1H qNMR) method for quantification of 297 types of NPS in complex chocolate matrixes was devised for the first time after rigorous analysis of various critical features of merit, including suitable deuterated solvent, internal standard, quantitative peaks, and instrument acquisition parameters. Validation of the method using six different types of NPS afforded limits of detection of 0.05-0.1 mg/mL, limits of quantification of 0.01-0.03 mg/mL, repeatability and reproducibility lower than 0.5% and 3.6%, recoveries of 91.7%∼104.4%, and absence of matrix effect. The quantitative analysis of 65 seized chocolate samples by 1H qNMR and 19F qNMR showed that the content of NPS was in the range of 0.5 mg/g∼44.1 mg/g. Generally, the developed qNMR method was simple, fast, precise, and can be performed without reference materials of NPS. Since the type and content of NPS are relatively random, chocolate consumers will face huge health risks. Therefore, this new trend of NPS-infused chocolate deserves and requires more attention from national NPS monitoring departments as well as forensic laboratories.

Identification and analytical characterization of N-propyl norbutylone, N-butyl norbutylone, N-benzyl norheptedrone, and N-pyrrolidinyl-3,4-DMA.

In this study, the analytical characterization of three cathinones and one N-pyrrolidinyl-substituted amphetamine derivative is described: 1-([3,4-methylenedioxyphenyl])-2-(propylamino)butan-1-one (N-propyl norbutylone 1), 1-([3,4-methylenedioxyphenyl])-2-(butylamino)butan-1-one (N-butyl norbutylone 2), 2-(benzylamino)-1-phenylheptan-1-one (N-benzyl norheptedrone 3), and 1-(1-[3,4-dimethoxyphenyl]propan-2-yl)pyrrolidine (N-pyrrolidinyl-3,4-DMA 4). The identification was based on ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UHPLC-QTOF-MS), gas chromatography-orbitrap MS (GC-Orbitrap-MS), nuclear magnetic resonance spectroscopy (NMR), and Fourier transform infrared (FT-IR). GC-Orbitrap-MS, with higher mass accuracy, benefit more on the accurate structure elucidation of product ions compared with the low-resolution GC-MS. The collision-induced dissociation (CID) and electron ionization (EI) pathways of these compounds were examined to assist forensic laboratories in elucidating the structure of new psychoactive substances (NPS) with similar structure in their case work. In addition, electron activated dissociation (EAD) was applied to analyze N-benzyl norheptedrone, which showed only one product ion in the CID mode. The result showed that for compound with limited product ions in the CID mode, the EAD mode can give more complementary information for structure elucidation. In addition, quantitative NMR (qNMR) was applied for the quantification of four powdered/crystal and two herbal blend seized samples. To our knowledge, no analytical data about the compounds 3 and 4 have appeared until now, making this the first report on these compounds.

The application of 19F NMR spectroscopy for the analysis of fluorinated new psychoactive substances (NPS).

In this study, fluorine-19 nuclear magnetic resonance spectroscopy (19F NMR) served as a highly specific tool for identification of fluorinated new psychoactive substances (NPS) as well as a suitable analytical method for the accurate quantification of fluorinated NPS in different seized samples. In the first part of the study, 19F NMR spectroscopy of a number of different fluorinated NPS, including 51 synthetic cannabinoids, 8 synthetic cathinones, 7 phenethylamines, 8 fentanyl analogues, and 9 other types of compounds was conducted. The chemical shifts and multiplet of the primary fluorides (RCH2F), fluorobenzenes (ortho-ArF, meta-ArF, and para-ArF), and trifluoromethylbenzenes (ArCF3) were discussed in detail to illustrate the role of 19F signals as special fingerprints in assisting the structure identification of fluorine-containing NPS. To the best of our knowledge, this study is the largest evaluation of fluorinated NPS compounds by 19F NMR. The second part of this study dealt with the problems encountered in the 19F quantification procedure and the criteria to be considered for successful quantification by 19F NMR. General high field (HF)- and low field (LF)- 19F qNMR methods for the quantification of fluorinated NPS were established after the thorough discussion of NMR spectrum acquisition and processing parameters such as: transmitter frequency offset (O1P), spin-lattice relaxation time (T1), and different baseline correction methods. The limit of quantifications (LOQs) for HF-19F qNMR varied between 0.1 mg/mL and 0.2 mg/mL, and for LF-19F qNMR varied between 1.0 mg/mL and 2.0 mg/mL. The limit of detections (LODs) for HF-19F qNMR varied between 0.03 mg/mL and 0.06 mg/mL, and for LF-19F qNMR varied between 0.3 mg/mL and 0.6 mg/mL. Finally, the developed methods were applied for the quantification of fluorinated-NPS in seventeen herbal blends, e-liquid, tablet, and powder NPS seizures.

Characterization of 17 unknown ketamine manufacturing by-product impurities by UHPLC-QTOF-MS.

This study initially reported the selection and characterization of 17 unknown impurities attributed to the manufacture process of ketamine. A total of 150 seized ketamine samples were investigated through ultra-high-performance liquid chromatography-quadrupole-time of flight (UHPLC-Q-TOF). Seventeen characteristic impurities were selected in accordance with four criteria: The compound was detected in over 10% of all 150 seized ketamine samples, the compound had at least one nitrogen, the unsaturation of the compound was more than 5, and the compound was stable in the dilution solvent solution for 48 h. The accurate masses of the protonated molecules and product ions of the target impurities were obtained based on the full scan mode and the product ion mode of Q-TOF, respectively. Lastly, the possible structures of the above impurities were tentatively elucidated in accordance with the synthetic route of ketamine, protonated molecules, and MS2 product ions. All 17 impurities had the same skeleton of deschloroketamine (DCK), but were substituted with additional chlorine, hydroxyl, methyl, cyclohexane, and o-chlorophenyl cyclopentyl ketone substituents. Under the electrospray ionization (ESI), the above impurities showed similar characteristic fragment ions through the dissociation of the CH3 NH2, C2 H6 NH, H2 O, CO, C2 H4 O, C4 H6 , and C2 H2 moieties. The above impurities have been routinely used for the profiling analysis of seized ketamine samples in the National Narcotics Laboratory of China and employed to establish the tactical intelligence for law enforcement agencies.

Identification of AD-18, 5F-MDA-19, and pentyl MDA-19 in seized materials after the class-wide ban of synthetic cannabinoids in China.

To curb the manufacturing, trafficking, and abuse of synthetic cannabinoids, China implemented a class-wide regulation on synthetic cannabinoids in July 2021. Recently, three different types of synthetic cannabinoid analogs that were not covered by the generic definitions were detected in seized powdered and e-liquid materials. These derivatives included 2-(2-(1-(4-fluorobenzyl)-1H-indol-3-yl)acetamido)-3,3-dimethylbutanamide (AD-18), N'-(1-(5-fluoropentyl)-2-oxoindolin-3-ylidene)benzohydrazide (5F-MDA-19), and N'-(2-oxo-1-pentylindolin-3-ylidene)benzohydrazide (pentyl MDA-19). Identification was based on ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UHPLC-QTOF-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance spectroscopy (NMR), and Fourier transform infrared spectroscopy (FT-IR). AD-18 is a methylene analog of ADB-FUBICA. No chemical or pharmacological data about AD-18 and 5F-MDA-19 have appeared until now, making this the first report on these two compounds. Pentyl MDA-19 has previously been reported to have high affinity for cannabinoid CB1 and CB2 receptors, but this is the first report of its presence in the recreational drug market. Moreover, the collision-induced dissociation (CID) and electron ionization (EI) characteristic fragmentation routes of AD-18 and the other two MDA-19 derivatives were also discussed to facilitate forensic laboratories in their identification of other substances with a similar structure in their case work.

Structure Analysis of the Interfering Substance N-methyl-2-phenylpropan-1-amine of Methamphetamine in Wastewater.

OBJECTIVES: To analyze the chemical structure of the interfering substance that affects the result of methamphetamine analysis in wastewater. METHODS: A combination of GC-MS and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) was used to analyze the mass spectrum characteristics of the interfering substance that affects the result of methamphetamine analysis and to infer its possible structure. Liquid chromatography-triple quadrupole-mass spectrometry (LC-TQ-MS) was used to confirm the control material. RESULTS: Using LC-QTOF-MS in positive electrospray ionization (ESI+) mode, the mass-to-charge ratio (m/z) of quasi-molecular ion in the MS1 mass spectrometry of interfering substance was identical to that of methamphetamine, indicating that the interfering substance was probably an isomer of methamphetamine. The MS2 mass spectra obtained at three collision energies of 15 V, 30 V and 45 V were highly similar to methamphetamine, suggesting that the interfering substance contained methylamino and benzyl groups. Further analysis using GC-MS in electron impact (EI) ionization mode showed that the base peak in the mass spectrum of the interfering substance was at m/z 44. The interfering substance was confirmed to be N-methyl-2-phenylpropan-1-amine by compared with the standard reference. CONCLUSIONS: The chemical structure of N-methyl-2-phenylpropan-1-amine is highly similar to methamphetamine, which is easy to cause interference for the detection of trace amounts of methamphetamine in wastewater using LC-TQ-MS. Therefore, in the actual analysis, the chromatographic retention time can be used to distinguish between N-methyl-2-phenylpropan-1-amine and methamphetamine.

Identification and quantification of 10 indole/indazole carboxamide synthetic cannabinoids in 36 herbal blends by gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy.

Herbal blends containing synthetic cannabinoids have become popular alternatives to marijuana. The number of synthetic cannabinoids and speed of their emergence enable this group of compounds particularly challenging in terms of detection, monitoring, and responding. In this work, both gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR) methods were developed for the identification and quantification of synthetic cannabinoids in herbal blends. Ten types of indole/indazole carboxamide synthetic cannabinoids, which showed different types of substitutions connected to nitrogen of the indole/indazole carboxamide, were detected in 36 herbal blends. The GC-MS fragmentation routes of indole/indazole carboxamide synthetic cannabinoids were discussed in detail for structure identification purpose. The concentration range of synthetic cannabinoid in 36 herbal blends was 1.9-50.6 mg/g using GC-MS method, while 1.5-49.0 mg/g by NMR method. Nicotine in herbal blends was quantified by NMR method without using reference material, and showed a variation of 5.3-44.7 mg/g. For quantitative analysis, NMR method showed great advantage in the absence of reference material, while GC-MS method showed great merit for multiple-compound analysis when reference material was available. Therefore, for the quantitative analysis of new emerged synthetic cannabinoid in herbal blends, different methods could be chosen by considering whether reference material is available, as well as the number and types of synthetic cannabinoids detected in a single sample.

Quantification of Cathinone Analogues without Reference Standard Using 1H Quantitative NMR.

Synthetic cathinones are a type of new psychoactive substances (NPS) that have been seriously abused. Owing to the rapid variation in their structures, the absence of reference standards poses a challenge in quantitative investigations. In this study, a 1H quantitative nuclear magnetic resonance (1H qNMR) method was established using maleic acid as the internal standard and the shared signal (i.e., the methylidyne hydrogen) on the parent synthetic cathinones structure as the quantitative peak. Taking 3-methoxy-2-(methylamino)-1-(4-methylphenyl)propan-1-one (mexedrone) as an example, this study optimized the acquisition parameters and conducted method validation, including an evaluation of the specificity, linearity, accuracy, precision, and robustness. Using this 1H qNMR method, the contents of mexedrone and its analogues, including 1-(3-chlorophenyl)-2-(ethylamino)-propan-1-one (3-CEC), 4-chloro-α-pyrroli-dinopropiophenone (4-Cl-α-PVP), 1-(3,4-methylenedioxy-phenyl)-2-propylamino-propan-1-one (propylone), and methcathinone, were obtained. The obtained results showed that the method was accurate, rapid, versatile, and can be used to address the qualitative and quantitative issues related to similar substances.

Automatic analytical approach for the determination of 12 illicit drugs and nicotine metabolites in wastewater using on-line SPE-UHPLC-MS/MS.

In this study, we developed a novel on-line solid phase extraction (SPE)-ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS)-based analytical method for simultaneously quantifying 12 illicit drugs and metabolites (methamphetamine, amphetamine, morphine, codeine, 6-monoacetylmorphine, benzoylecgonine, 3,4-methylenedioxymethamphetamine, 3,4-methylenedioxyamphetamine, cocaine, ketamine, norketamine, and methcathinone) and cotinine (COT) in wastewater samples. The analysis was performed by loading 2 mL of the sample onto an Oasis hydrophilic-lipophilic balance cartridge and using a cleanup step (5% methanol) to eliminate interference with a total run time of 13 min. The isotope-labeled internal standard method was used to quantify the target substances and correct for unavoidable losses and matrix effects during the on-line SPE process. Typical analytical characteristics used for method validation were sensitivity, linearity, precision, repeatability, recovery, and matrix effects. The limit of detection (LOD) and limit of quantification (LOQ) of each target were set at 0.20 ng/L and 0.50 ng/L, respectively. The linearity was between 0.5 ng/L and 250 ng/L, except for that of COT. The intra- and inter-day precisions were <10.45% and 25.64%, respectively, and the relative recovery ranged from 83.74% to 162.26%. The method was used to analyze various wastewater samples from 33 cities in China, and the results were compared with the experimental results of identical samples analyzed using off-line SPE. The difference rate was between 19.91% and -20.44%, and the error range could be considered acceptable. These findings showed that on-line SPE is a suitable alternative to off-line SPE for the analysis of illicit drugs in samples.

Discrimination of phenethylamine regioisomers and structural analogues by Raman spectroscopy.

In this study, the Raman spectra of 21 phenethylamines were obtained using far-red excitation (785 nm). The distinguishing ability of Raman for phenethylamines, especially for phenethylamine regioisomers and structural analogues, was investigated. Here, the evaluation of a cross section of Raman spectra demonstrated that all types of phenethylamines were distinguishable, even for certain structural analogues with high spectrum similarity. Raman exhibited high distinguishing ability for phenethylamine regioisomers that differ in the substitution position of halogen, methoxy, alkyl, or other substituted groups; as well as for structural analogues containing different groups, such as furanyl, 2,3-dihydrofuranyl, halogen, and alkyl substituted at the same position. The Raman spectra for homologues with differences in only a methyl group were found to be highly similar; however, their spectra demonstrated small but detectable differences. Four analogue mixtures and 59 seized samples were also analyzed to study the practical use of the Raman method in forensic field. 95% of the seized samples were correctly identified, which significantly validated the ability of Raman method in identifying the correct isomers. Accordingly, this study provides a non-destructive, high-throughput and minimal sample preparation technique for the discrimination of phenethylamines.