Screening method for rapid classification of psychoactive substances in illicit tablets using mid infrared spectroscopy and PLS-DA

Gaussian process regression coupled with mRMR to predict adulterant concentration in cocaine

Street cocaine is often mixed with various substances that intensify its harmful effects. This paper proposes a framework to identify attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) intervals that best predict the concentration of adulterants in cocaine samples. Wavelengths are ranked according to their relevance through ReliefF and mRMR feature selection approaches, and an iterative process removes less relevant wavelengths based on the ranking suggested by each approach. Gaussian Process (GP) regression models are constructed after each wavelength removal and the prediction performance is evaluated using RMSE. The subset balancing a low RMSE value and a small percentage of retained wavelengths is chosen. The proposed framework was validated using a dataset consisting of 345 samples of cocaine with different amounts of levamisole, caffeine, phenacetin, and lidocaine. Averaged over the four adulterants, the GP regression coupled with the mRMR retained 1.07 % of the 662 original wavelengths, outperforming PLS and SVR regarding prediction performance.

A compact Fourier-transform near-infrared spectrophotometer and chemometrics for characterizing a comprehensive set of seized ecstasy samples

A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.

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.

A simple, quick and non-destructive approach for sampling drugs of abuse in tablets and blotter for qualitative analysis by paper spray mass spectrometry

This study presents the development of a simple, fast, and inexpensive approach for the direct analysis of new psychoactive substances (NPS) in seized tablets and blotter paper, with improved sample preservation and increased analytical frequency. Paper triangles were gently rubbed against the surface of the samples containing synthetic drugs and then subjected to analysis by paper spray ionization mass spectrometry (PS-MS). Seized samples containing lysergic acid diethylamide (LSD) and several other substances from the classes of amphetamines, N-benzyl-substituted phenethylamines, synthetic cathinones, and synthetic cannabinoids, were analysed. Three types of paper were tested (filter paper, blotter paper, and synthetic paper) and several combinations of spray solvents were studied for the optimization. All samples were weighed and photographed before and after sequences of analysis in order to attest to the sample preservation. The results revealed that the approach is excellent for sample preservation, with less than 5% of mass loss even after 27 consecutive analyses. Moreover, no significant signal decreases were observed in mass spectrometry (MS) even after the experiments. It was possible to unequivocally identify illicit substances from seized samples (pills and blotter paper). By overcoming the solubilization and wet extraction process used for sample preparation, the waste was restricted to a volume of only 10 μL of solvent for the PS-MS analysis. The main advantage of our approach over existing methods is the sample preparation, which is simple and quick since the samples are just rubbed against the PS paper. This brings enormous benefits in terms of analytical frequency, economy of time and low consumption of solvents. Another important point is that the sample can remain intact for further analysis, which is crucial in forensic 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.

Using HRMS fingerprinting to explore micropollutant contamination in soil and vegetables caused by swine wastewater irrigation

Livestock wastewater has been reused for agricultural irrigation to save water and fertilise the soil. However, micropollutants excreted by livestock animals may contaminate the soil and crops through livestock wastewater irrigation. This study employed high-resolution mass spectrometry (HRMS) to facilitate broad-scope suspect screening of soil and vegetables and identify changes in micropollutant fingerprints caused by swine wastewater irrigation. Field trials were performed to simulate the practical cultivation of small leafy vegetables. Soil and pak choi were irrigated with groundwater, a reasonable amount of swine wastewater, and excessive swine wastewater (three times the reasonable amount) and were sampled at three time points. The samples were extracted using organic solvents and analysed with a liquid chromatography-quadrupole-time-of-flight HRMS system. The molecular features were compared to over 3000 micropollutants in commercial libraries. The relative concentrations of suspect micropollutants among the irrigation groups were compared using multivariate and univariate analyses. The marker micropollutants that increased with swine wastewater irrigation were rigorously identified based on the MS/MS spectra. Fifty-three micropollutants were frequently found in the soil (n = 54) and 36 in the pak choi (n = 53). Partial least squares discriminant analysis (PLS-DA) models revealed significant differences in the micropollutant fingerprints in the soil among the three irrigation groups, but not in the pak choi. Eight micropollutants with variable importance in projection scores above 1.0 in the PLS-DA model and significantly higher relative concentrations (p < 0.05) in the soil irrigated with swine wastewater were confirmed as markers. Besides veterinary drugs and their metabolites, cinnamic acid and phenylalanine were the markers relevant to swine feed that were not previously reported. Nevertheless, accumulations of micropollutants in the soil or contamination of the pak choi due to swine wastewater irrigation were not found under the trial conditions.

One-class model with two decision thresholds for the rapid detection of cashew nuts adulteration by other nuts

A green screening method to determine cashew nut adulteration with Brazilian nut, pecan nut, macadamia nut and peanut was proposed. The method was based on the development of a one-class soft independent modelling of class analogy (SIMCA) model for non-adulterated cashew nuts using near-infrared (NIR) spectra obtained with portable equipment. Once the model is established, the assignment of unknown samples depends on the threshold established for the authentic class, which is a key aspect in any screening approach. The authors propose innovatively to define two thresholds: lower model distance limit and upper model distance limit. Samples with distances below the lower threshold are assigned as non-adulterated with a 100% probability; samples with distance values greater than the upper threshold are assigned as adulterated with a 100% probability; and samples with distances within these two thresholds will be considered uncertain and should be submitted to a confirmatory analysis. Thus, the possibility of error in the sample assignment significantly decreases. In the present study, when just one threshold was defined, values greater than 95% for the optimized threshold were obtained for both selectivity and specificity. When two class thresholds were defined, the percentage of samples with uncertain assignment changes according to the adulterant considered, highlighting the case of peanuts, in which 0% of uncertain samples was obtained. Considering all adulterants, the number of samples that were submitted to a confirmatory analysis was quite low, 5 of 224 adulterated samples and 3 of 56 non-adulterated samples.

Analysis of urine using electronic tongue towards non-invasive cancer diagnosis

Electronic tongues (e-tongues) have been broadly employed in monitoring the quality of food, beverage, cosmetics, and pharmaceutical products, and in diagnosis of diseases, as the e-tongues can discriminate samples of high complexity, reduce interference of the matrix, offer rapid response. Compared to other analytical approaches using expensive and complex instrumentation as well as required sample preparation, the e-tongue is non-destructive, miniaturizable and on-site method with little or no preparation of samples. Even though e-tongues are successfully commercialized, their application in cancer diagnosis from urine samples is underestimated. In this review, we would like to highlight the various analytical techniques such as Raman spectroscopy, infrared spectroscopy, fluorescence spectroscopy, and electrochemical methods (potentiometry and voltammetry) used as e-tongues for urine analysis towards non-invasive cancer diagnosis. Besides, different machine learning approaches, for instance, supervised and unsupervised learning algorithms are introduced to analyze extracted chemical data. Finally, capabilities of e-tongues in distinguishing between patients diagnosed with cancer and healthy controls are highlighted.

Discrimination of white automotive paint samples using ATR-FTIR and PLS-DA for forensic purposes

The consequences of a hit-and-run car crash are significant and may include serious injuries to the victims, health system overload and even victim's death. The vehicle and driver identification are often challenging for local law enforcement. The aim of this study was to develop a methodology to discriminate between automotive paint samples according to the make of the vehicle and its color shade. 143 white samples (collected at traffic accident scenes) were analyzed in situ by Fourier transform infrared spectroscopy with attenuated total reflectance (ATR-FTIR) and coupled microscopy. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed for data analysis. The samples were split into three groups: calibration set, validation set and external test set. The figures of merit were calculated to assess the quality of the model. Sensitivity, specificity, and efficiency rates were, respectively, 98,9%, 98.4% and 98.6%, for the calibration set. For the validation group, the classification accuracy was 100%. Correct classification rates for the internal validation set and external test set were 100% and 79.1% respectively. The technique is clean, fast, relatively low-cost, and non-destructive. Damaged regions of the samples were avoided by using the attached microscope. Limiting the age of the samples to a maximum of 10 years was enough to avoid misclassifications due to the natural degradation and weathering of the sample. Since the external test group is formed by underrepresented classes, its correct classification rate (79.1%) can be potentially improved at any time, by including and analyzing more samples.

Facing Counterfeit Medications in Sexual Medicine. A Systematic Scoping Review on Social Strategies and Technological Solutions

Introduction

The counterfeit phenomenon is a largely under-reported issue, with potentially large burden for healthcare. The market for counterfeit drugs used in sexual medicine, most notably type 5 phosphodiesterase inhibitors (PDE5i), is rapidly growing.

Aims

To report the health risks associated with the use of counterfeit medications, the reasons driving their use, and the strategies enacted to contain this phenomenon.

Methods

A systematic scoping review of the literature regarding counterfeit PDE5i was carried between January and June 2021, then updated in August 2021.

Main Outcome Measure

We primarily aimed to clarify the main drivers for counterfeit PDE5i use, the health risks associated, and the currently available strategies to fight counterfeiters.

Results

One hundred thirty-one records were considered for the present scoping review. Production of fake PDE5i is highly lucrative and the lacking awareness of the potential health risks makes it a largely exploitable market by counterfeiters. Adulteration with other drugs, microbial contamination and unreliable dosages make counterfeit medications a cause of worry also outside of the sexual medicine scope. Several laboratory techniques have been devised to identify and quantify the presence of other compounds in counterfeit medications. Strategies aimed at improving awareness, providing antitampering packaging and producing non-falsifiable products, such as the orodispersible formulations, are also described.

Clinical implications

Improving our understanding of the PDE5i counterfeit phenomenon can be helpful to promote awareness of this issue and to improve patient care.

Strengths & Limitations

Despite the systematic approach, few clinical studies were retrieved, and data concerning the prevalence of counterfeit PDE5i use is not available on a global scale.

Conclusion

The counterfeit phenomenon is a steadily growing issue, with PDE5i being the most counterfeited medication with potentially large harmful effects on unaware consumers.

Sansone A, Cuzin B, and Jannini EA. Facing Counterfeit Medications in Sexual Medicine. A Systematic Scoping Review on Social Strategies and Technological Solutions. Sex Med 2021;9:100437.

Chemometrics in forensic science: approaches and applications

Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field.

Can X-Ray Powder Diffraction Be a Suitable Forensic Method for Illicit Drug Identification?

New psychoactive substances (NPSs) are associated with a significant number of intoxications. With the number of readily available forms of these drugs rising every year, there are even risks for the general public. Consequently, there is a high demand for methods sufficiently sensitive to detect NPSs in samples found at the crime scene. Infrared (IR) and Raman spectroscopies are commonly used for such detection, but they have limitations; for example, fluorescence in Raman can overlay the signal and when the sample is a mixture sometimes neither Raman nor IR is able to identify the compounds. Here, we investigate the potential of X-ray powder diffraction (XRPD) to analyse samples seized on the black market. A series of psychoactive substances (heroin, cocaine, mephedrone, ephylone, butylone, JWH-073, and naphyrone) was measured. Comparison of their diffraction patterns with those of the respective standards showed that XRPD was able to identify each of the substances. The same samples were analyzed using IR and Raman, which in both cases were not able to detect the compounds in all of the samples. These results suggest that XRPD could be a valuable addition to the range of forensic tools used to detect these compounds in illicit drug samples.

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.

Detection and discrimination of seminal fluid using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy combined with chemometrics

Semen is most frequently encountered body fluid in forensic cases apart from blood especially in sexual assault cases. The presence and absence of semen can help in conviction or exoneration of a suspect by either confirming or refuting the claims put forward by the suspect and the victim. However, in the wake of limited studies on non-destructive and rapid analysis of semen, it is fairly difficult. Therefore, it is an increasing demand to pioneer the application of available analytical methods in such manner that non-destructive, automated, rapid, and reliable identification and discrimination of body fluids can be established. In the present study, such a methodological application of attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy has been put forward as one of the initial steps towards the identification and discrimination/classification of seminal fluid from vaginal fluid and other human biological as well as non-biological look-alike semen substances using chemometric tools which are principal component analysis (PCA), partial least square regression (PLSR), and linear discriminant analysis (LDA). Effect of other simulated factors such as substrate interference, mixing with other body fluids, dilutions, and washing and chemical treatments to the samples has been studied. PCA resulted in 98.8% of accuracy for the discrimination of seminal fluid from vaginal fluid whilst 100% accuracy was obtained using LDA method. One hundred percent discrimination was achieved to discriminate semen from other biological fluids using PLSR and LDA, and from non-biological substances using PCA-LDA models. Furthermore, results of the effect of substrates, chemical treatment, mixing with vaginal secretions, and dilution have also been described.

New psychoactive substances (NPS) prevalence over LSD in blotter seized in State of Santa Catarina, Brazil: A six-year retrospective study

Designer drugs or new psychoactive substances (NPS) are a heterogeneous group of substances obtained through the modification of chemical structure of some natural products or drugs. NPS illegally commercialized in blotter papers mimicking the most common form of LSD consumption, with a great variability of colours and symbols, have largely increased worldwide, including in Brazil, becoming an important emerging public health issue. In this study, we have evaluated the presence and profile of NPS in blotters seized in the State of Santa Catarina, Brazil, over the period of 2011 to 2017. The state government criminal forensics staff has performed gas chromatography-mass spectrometer (GC-MS) analyses in order to determine the chemical composition of the blotters. During the evaluated period, there was a considerable increase in the seizing of blotters events, from 87 in 2011, to 301 in 2016 and reaching 277 in 2017. There was also an increase in the number of blotters seized per event. Interestingly, while in 2011, 100% of blotters contained LSD, this number decreased to 0,1% in 2014, and achieved 17,6% in 2017, when up to 25 different substances were detected in blotters seized. Drugs such as DOx, NBOMe, fentanyl, mescaline derivatives, triptamines, cathinones, and synthetic cannabinoids were detected and became the major substances found in blotters. In some cases, more than one substance was found in the same blotter, characterizing a new mixture scenario. The presence of several new psychoactive substances in blotters is a reality in forensic toxicology. In Brazil, it might be related to the fact that most of these substances were not considered illegal by Brazilian legislation by the time they emerged.

Simultaneous determination of metabolic and elemental markers in methamphetamine-induced hepatic injury to rats using LC-MS/MS and ICP-MS

Methamphetamine (METH) is one of the most highly addictive illicit drugs abused all over the world. Much evidence indicates that METH abuse leads to major toxicity, medical consequences, and even severe public health consequences. Existing studies usually focus on the pathomechanism of METH-induced toxicity; therefore, data on metabolites and elements correlating with particular toxicity remain scarce. The objective of the present study is to develop appropriate analytical procedures to identify the differential metabolic and elemental biomarkers on METH-induced hepatic injury to rats. The rats were administrated with METH (15 mg/mL/kg, two times per day) via intraperitoneal (i.p.) injections for four consecutive days. The alanine aminotransferase and aspartate aminotransferase activity levels of in the rat serum of the METH group increase significantly compared with those of the control group, suggesting obvious hepatic injury. The results are further confirmed by the histopathological microscopic observation. A total of 18 small molecular metabolites and 19 elements are selected to perform the simultaneous quantification based on the combination of liquid chromatography coupled with tandem mass spectrometry and inductively coupled plasma mass spectrometry. Sample preparation was optimized to cover all the analytes. Both methods are optimized and validated according to developed guidelines such as limits of detection, limits of quantification, linearity, precision, and recovery. All the obtained data are within the satisfactory range. The normalized data were processed according to the partial least squares discrimination analysis (PLS-DA) model. Five differential metabolic and six elemental markers are identified in rat plasma based on the variable importance in projection (VIP) (> 1) and t test results. Overall, the results obtained in this study demonstrate the developed methods are suitable for simultaneous determination of metabolic and elemental markers in the hepatic injury to rats induced by METH. Graphical abstract.

"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.

MS-Based Molecular Networking of Designer Drugs as an Approach for the Detection of Unknown Derivatives for Forensic and Doping Applications: A Case of NBOMe Derivatives

The NBOMe family is a group of new psychoactive substances (NPSs). In this study, the fragmentation patterns of NBOMe derivatives were analyzed using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). The MS/MS spectral data was used to establish a molecular networking map for NBOMe derivatives. The fragmentation patterns of nine NBOMe derivatives were interpreted on the basis of their product ion spectral data. NBOMe derivatives generally showed similar product ion spectral patterns; among them, the halogen-substituted methoxybenzyl ethanamine type derivatives showed a characteristic product ion of a radical cation. Molecular network analysis of the MS/MS data revealed that all NBOMe derivatives formed one integrated networking cluster that discriminated them from other types of NPSs. NBOMe derivatives were spiked into human urine and identified by connection to the NBOMe database network. Furthermore, the NBOMe compounds that were not registered in the database were also recognized as an NBOMe-related substance by molecular networking. These results demonstrate the potential of using molecular networking-based screening methods for designer drugs, and the proposed method would be useful in forensic or doping analysis.