Direct classification of new psychoactive substances in seized blotter papers by ATR-FTIR and multivariate discriminant analysis

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.

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.

Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses

Contemporary criminal investigations are based on the statements made by the victim and the eyewitnesses. They also rely on the physical evidences found in the crime scene. These evidences, and more particularly biological ones, have a great judicial value in the courtroom. They are usually used to revoke the suspect’s allegations and confirm or refute the statements made by the victim and the witnesses. Stains of body fluids are biological evidences highly sought by forensic investigators. In many criminal cases, the success of the investigation relies on the correct identification and classification of these stains. Therefore, the adoption of reliable and accurate forensic analytical methods seems to be of vital importance to attain this objective. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is a modern and universal analytical technique capable of fingerprint recognition of the analyte using minimal amount of the test sample. The current systematic review aims to through light on the fundamentals of this technique and to illustrate its wide range of applications in forensic investigations. ATR-FTIR is a nondestructive technique which has demonstrated an exceptional efficiency in detecting, identifying and discriminating between stains of various types of body fluids usually encountered in crime scenes. The ATR-FTIR spectral data generated from bloodstains can be used to deduce a wealth of information related to the donor species, age, gender, and race. These data can also be exploited to discriminate between stains of different types of bloods including menstrual and peripheral bloods. In addition, ATR-FTIR has a great utility in the postmortem investigations. More particularly, in estimating the postmortem interval and diagnosing death caused by extreme weather conditions. It is also useful in diagnosing some ambiguous death causes such as fatal anaphylactic shock and diabetic ketoacidosis.

A fast and effective approach for the discrimination of garlic origin using wooden-tip electrospray ionization mass spectrometry and multivariate classification

This paper presents the combination of wooden-tip electrospray ionization mass spectrometry (WTESI-MS) and multivariate pattern recognition methods (principal component analysis, PCA and partial least squares discriminant analysis, PLS-DA) for the rapid and reliable discrimination, via chemical fingerprints, of garlic origin. A total of 312 garlic samples grown in different countries (Brazil, China, Argentina, Spain, and Chile) were studied. The methodology was based on a direct sampling approach, which relies on loading the sample by penetrating the garlic cloves with a pre-wetted wooden tip, followed by direct prompt analysis by WTESI-MS. Thus, no sample preparation is needed, which prevents the degradation of important metabolites and increases the analytical throughput. Parameters that affects the WTESI were optimized and the best performance in terms of signal stability and intensity was achieved using the positive ion mode. Most of the ions in WTESI mass spectra were assigned to amino acids, sugars, organosulfur compounds, and lipids. The discriminative model showed good performance (accuracy rates between 81.9% and 98.6%) and enabled identifying diagnostic ions for garlic samples from different origins. The differentiation and classification of garlic origin is of major importance as this food flavoring product is widely consumed, with worldwide trade representing billions of dollars every year, and is very often the subject of fraud.

Chemical and statistical analyses of blotter paper matrix drugs seized in the State of Rio de Janeiro

Drugs of abuse are psychoactive substances illicitly distributed and used worldwide. In Rio de Janeiro, Brazil, they represent a public health issue and are directly related to several social problems. The recent increase in appearances of new psychoactive substances (NPS), derived from structural modifications of existing psychoactive substances, poses a threat to public health and forensic laboratories worldwide, as little is known about these substances. This study aimed to chemically and geographically map drugs of abuse from blotter papers seized by the Civil Police of Rio de Janeiro State (PCERJ) between 2006 and 2019. High-performance analytical techniques, such as gas chromatography-mass spectrometry (GC-MS) and Orbitrap mass spectrometry (Orbitrap-MS), combined with statistical analyses, were employed to characterize the seized samples. The most common chemical compounds in NPS found in this study were synthetic phenethylamines, i.e., molecules from the 25I-NBOH (2-(((4-iodo-2,5-dimethoxyphenethyl)amino)methyl)phenol) and 25I-NBOMe (2-(4-iodo-2,5-dimethoxyphenyl)-N-(2-methoxybenzyl)ethanamine) families. Prior to 2014, the majority of seized blotter papers contained lysergic acid diethylamide (LSD) and were concentrated in the Metropolitan region. An upsurge in blotter paper seizures was observed from 2014 to 2017; the most common substances during this time were from the NBOMe family. NBOH compounds emerged in 2016 in coastal regions with high tourism, reaching over 1300 items only in 2017. Only one synthetic cannabinoid was found among the blotter papers seized in Rio de Janeiro between 2006 and 2019. The assembled chemical data and statistical analyses allowed the mapping and monitoring of the chemical profiles of the seized blotter papers, providing a strong foundation for the understanding of the origins and movement of these drugs around the RJ State.

Two newly-emerging substituted phenethylamines MAL and BOD induce differential psychopharmacological effects in rodents

BACKGROUND

Recently, the recreational use of substituted phenethylamines has grown rapidly. Among these are 2-(3,5-dimethoxy-4-((2-methylallyl)oxy)phenyl)ethanamine (MAL) and 2-(2,5-dimethoxy-4-methylphenyl)-2-methoxyethan-1-amine (BOD). However, studies characterizing their abuse potential are still lacking.

AIM

The purpose of this study was to investigate the abuse potential of MAL and BOD.

METHODS

The psychostimulant, reinforcing, and rewarding properties of MAL and BOD were analyzed using locomotor sensitization, self-administration, and conditioned place preference tests. Dopamine antagonists (i.e. SCH23390, haloperidol) were administered during conditioned place preference to evaluate the involvement of the mesolimbic dopamine system. Furthermore, dopamine-related protein expression in the nucleus accumbens and the ventral tegmental area was measured along with dopamine concentrations in the nucleus accumbens. Electroencephalography was conducted to determine effects of MAL and BOD on brain wave activity.

RESULTS

MAL induced psychostimulant effects and sensitization, while BOD induced locomotor depression in mice. Only MAL was self-administered by rats. Both drugs induced conditioned place preference in mice at different doses; dopamine receptor antagonists blocked MAL- and BOD-induced conditioned place preference. Both the compounds altered the expression of dopamine receptor D₁ and D₂ proteins in the nucleus accumbens and tyrosine hydroxylase (TH) and dopamine transporter in the ventral tegmental area, enhanced dopamine levels in the nucleus accumbens, and increased delta and gamma wave activities in the brain.

CONCLUSIONS

MAL may induce abuse potential via the mesolimbic dopaminergic system and possibly accompanied by alterations in brain wave activity. Moreover, the lack of rewarding and reinforcing effects in BOD suggest that this drug may have little to no capability to engender compulsive behavior, though having found to induce alterations in dopaminergic system and brain wave activities.

Development and validation of a color spot test method for the presumptive detection of 25-NBOMe compounds

The great increase of new psychoactive substances over the past decade has substantially transformed the illicit drug industry to an ever-changing dynamic market. 25-NBOMe compounds are just one of these new substance groups that pose a public health risk in many countries around the world. These highly potent, hallucinogenic phenethylamines have previously been sold as "legal highs" or "synthetic LSD" and the necessity to rapidly identify their presence is crucial. While there are many laboratory-based analytical methods capable of identifying these compounds, the lack of presumptive test methods indicates the need for a specific and timely test that could be used in the field. Herein we outline the developed chemical spot test that can selectively identify the presence of 25-NBOMe compounds and related analogs through the reaction with a substituted benzoquinone reagent under basic conditions. This test method has been comprehensively validated showing a high level of selectivity, specificity, and precision with only two other illicit substances producing similar positive results as 25-NBOMe and few false-negative results seen. The working limit of detection was determined to be 225 μg and there was no cross-reactivity from potential adulterants of significance. This test has also been shown to work directly with blotter papers containing 25-NBOMe compounds, indicating no interference from this common matrix and the ability to differentiate these compounds from LSD. This method shows a high potential to be translated to a field compatible test that is simple, rapid, and selective for 25-NBOMe compounds.

Interpol review of controlled substances 2016-2019

This review paper covers the forensic-relevant literature in controlled substances from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.

"Lab-on-Click" Detection of Illicit Drugs in Oral Fluids by MicroNIR-Chemometrics

A novel, entirely automated MicroNIR-chemometric platform was developed for the "lab-on-click" detection of illicit drugs in nonpretreated oral fluids, and a novel tool for the first-level test is proposed. Calibration of the method was achieved by collecting oral-fluid specimens from volunteers, and chemometric analysis was considered for the development of models for prediction for cocaine, amphetamine, and Δ9-tetrahydrocannabinol. In addition, a comprehensive model was optimized for the simultaneous prediction of positive-negative samples and the specific illicit drug used by abusers in a single "click". The detection ability of the method was checked for true-positive and false-positive outcomes, and results were validated by a GC-MS reference official method. The MicroNIR-chemometric platform provided the simultaneous prediction of the three most frequently abused addictive drugs with the sensitivity and accuracy of the confirmatory analyses, offering the advantages of rapidity and simplicity and demonstrating that it is a promising tool for supporting public-health surveillance.

Rapid Identification of Novel Psychoactive and Other Controlled Substances Using Low-Field 1H NMR Spectroscopy

An automated approach to the collection of ¹H NMR (nuclear magnetic resonance) spectra using a benchtop NMR spectrometer and the subsequent analysis, processing, and elucidation of components present in seized drug samples are reported. An algorithm is developed to compare spectral data to a reference library of over 300 ¹H NMR spectra, ranking matches by a correlation-based score. A threshold for identification was set at 0.838, below which identification of the component present was deemed unreliable. Using this system, 432 samples were surveyed and validated against contemporaneously acquired GC-MS (gas chromatography-mass spectrometry) data. Following removal of samples which possessed no peaks in the GC-MS trace or in both the ¹H NMR spectrum and GC-MS trace, the remaining 416 samples matched in 93% of cases. Thirteen of these samples were binary mixtures. A partial match (one component not identified) was obtained for 6% of samples surveyed whilst only 1% of samples did not match at all.