Design of smart nanoparticles for the electrochemical detection of 3,4-methylenedioxymethamphetamine to allow in field screening by law enforcement officers.

A portable and highly sensitive sensor was designed for the specific detection of 3,4-methyl-enedioxy-methamphetamine (MDMA), in a range of field-testing situations. The sensor can detect MDMA in street samples, even when other controlled substances drugs, or adulterants are present. In this work, we report for the first time a sensor using electroactive molecularly imprinted polymer nanoparticles computationally designed to recognize MDMA and then produced using solid phase synthesis. A composite comprising chitosan, reduced graphene oxide, and molecularly imprinted polymer nanoparticles synthesized for MDMA for the first time was immobilized on screen-printed carbon electrodes. The sensors displayed a satisfactory sensitivity (106.8 nA × µM-1 ), limit of detection (1.6 nM; 0.31 ng/mL), and recoveries (92-99%). The accuracy of the results was confirmed through validation using Ultra-High Performance Liquid Chromatography coupled with tandem Mass Spectrometry (UPLC-MS/MS). This technology could be used in forensic analysis and make it possible to selectively detect MDMA in street samples.

Cannabis profiling of seized samples: An intra-location variability study using gas chromatography-mass spectrometry profiles and multivariate data analysis.

Yearly, cannabis belongs to the most seized drugs worldwide. During judicial investigations, illicit cannabis profiling can be performed to compare seized herbal material. However, comparison is challenging because of the natural heterogeneity of the psychoactive crop. Gas chromatography-mass spectrometry (GC-MS) profiles, consisting of eight cannabinoids, were used to study the intra-location (within) and inter-location (between) variabilities. Decision thresholds were derived from the 95% and 99% confidence limits, applying Pearson correlation coefficients for the intra-location samples. The false negatives and false positives (FPs) determined the discriminative power of different pretreatments applied to obtain the lowest FP error rate possible. Initially, a 97 samples data set was used and with log transformation as pretreatment, a decrease in FPs from 38% and 45% FPs to 17% and 22%, for both confidence limits, respectively, was seen relative to internal standard normalization that was used as reference. An additional intra-plantation variability study with 130 samples verified whether the initial model contained sufficient within-location information, but this was not the case. Hence, a combined data matrix was constructed with all seized samples. Log transformation provided the best FP results for both limits, that is, an improvement from 58% and 64% to 21% and 26%, respectively, was seen. The representativeness of these 'linked' thresholds was demonstrated using both cross-validation and an external set, for which similar FP results as for the calibration set were obtained. By applying data pretreatment, a significant improvement was observed to distinguish seized samples. However, the FP rate is still not at an acceptable level to defend in court.

Electrochemical Rapid Detection of Methamphetamine from Confiscated Samples Using a Graphene-Based Printed Platform.

Methamphetamine (MAP) is a highly addictive and illegal stimulant drug that has a significant impact on the central nervous system. Its detection in biological and street samples is crucial for various organizations involved in forensic medicine, anti-drug efforts, and clinical diagnosis. In recent years, nanotechnology and nanomaterials have played a significant role in the development of analytical sensors for MAP detection. In this study, a fast, simple, and cost-effective electrochemical sensor is presented that is used for the sensitive detection of MAP in confiscated street samples with a complex matrix. The optimized screen-printed sensor based on a carbon working electrode modified with graphene demonstrated an excellent limit of detection, good sensitivity, and a wide dynamic range (1-500 µM) for the target illicit drug both for standard solutions and real samples (seized samples, tap water, and wastewater samples). It can detect MAP at concentrations as low as 300 nM in real samples. This limit of detection is suitable for the rapid preliminary screening of suspicious samples in customs, ports, airports, and on the street. Furthermore, the sensor exhibits a good recovery rate, indicating its reliability and repeatability. This quality is crucial for ensuring consistent and accurate results during screening processes.

Investigating the electrochemical profile of methamphetamine to enable fast on-site detection in forensic analysis.

Methamphetamine (MA) is a synthetic psychoactive drug which is consumed both licitly and illicitly. In some countries it is prescribed for attention-deficit and hyperactivity disorder, and short-term treatment of obesity. More often though, it is abused for its psychostimulant properties. Unfortunately, the spread and abuse of this synthetic drug have increased globally, being reported as the most widely consumed synthetic psychoactive drug in the world in 2019. Attempting to overcome the shortcomings of the currently used on-site methods for MA detection in suspected cargos, the present study explores the potential of electrochemical identification of MA by means of square wave voltammetry on disposable graphite screen-printed electrodes. Hence, the analytical characterization of the method was evaluated under optimal conditions exhibiting a linear range between 50 µM and 2.5 mM MA, a LOD of 16.7 µM, a LOQ of 50.0 µM and a sensitivity of 5.3 µA mM-1. Interestingly, two zones in the potential window were identified for the detection of MA, depending on its concentration in solution. Furthermore, the oxidative pathway of MA was elucidated employing liquid chromatography - mass spectrometry to understand the change in the electrochemical profile. Thereafter, the selectivity of the method towards MA in mixtures with other drugs of abuse as well as common adulterants/cutting agents was evaluated. Finally, the described method was employed for the analysis of MA in confiscated samples and compared with forensic methods, displaying its potential as a fast and easy-to-use method for on-site analysis.

Gas Chromatographic Fingerprint Analysis for the Comparison of Seized Cannabis Samples.

Cannabis sativa L. is widely used as recreational illegal drugs. Illicit Cannabis profiling, comparing seized samples, is challenging due to natural Cannabis heterogeneity. The aim of this study was to use GC-FID and GC-MS herbal fingerprints for intra (within)- and inter (between)-location variability evaluation. This study focused on finding an acceptable threshold to link seized samples. Through Pearson correlation-coefficient calculations between intra-location samples, 'linked' thresholds were derived using 95% and 99% confidence limits. False negative (FN) and false positive (FP) error rate calculations, aiming at obtaining the lowest possible FP value, were performed for different data pre-treatments. Fingerprint-alignment parameters were optimized using Automated Correlation-Optimized Warping (ACOW) or Design of Experiments (DoE), which presented similar results. Hence, ACOW data, as reference, showed 54% and 65% FP values (95 and 99% confidence, respectively). An additional fourth root normalization pre-treatment provided the best results for both the GC-FID and GC-MS datasets. For GC-FID, which showed the best improved FP error rate, 54 and 65% FP for the reference data decreased to 24 and 32%, respectively, after fourth root transformation. Cross-validation showed FP values similar as the entire calibration set, indicating the representativeness of the thresholds. A noteworthy improvement in discrimination between seized Cannabis samples could be concluded.

Feasibility study on exhaled-breath analysis by untargeted Selected-Ion Flow-Tube Mass Spectrometry in children with cystic fibrosis, asthma, and healthy controls: Comparison of data pretreatment and classification techniques.

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H3O+, NO+ and O2+. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples.

Investigation of the effect of column temperature and back-pressure in achiral supercritical fluid chromatography within the context of drug impurity profiling.

Twenty commercially available stationary phases were characterized in supercritical fluid chromatography (SFC) using a diverse set of pharmaceutical compounds. Six dissimilar phases were selected, and a benzodiazepine and a trimethoprim impurity mixture were screened on these phases. Two stationary phases were then selected for each mixture to study the effect of temperature and back-pressure on retention, separation and chromatographic efficiency using a response surface design approach. The maximal feasible domain for each phase was examined and column performance was monitored for stability during the duration of the study. Chromatographic responses of the individual mixture components, such as retention time, peak width and apparent plate count, were modelled as a function of temperature and back-pressure. The use of high temperatures led to improved separations and higher efficiencies while high back-pressures resulted in reduced retention. For the two mixtures, the response surface plots of the resolution of the worst-separated peak pair over the experimental domain allowed the identification of the temperature and back-pressure leading to the maximal resolution for the worst-separated peak pair. For the mixtures investigated, the use of high temperatures led to improved separations and high efficiencies, while high back-pressures resulted in reduced retention. These factors are fine-tuning parameters in SFC, and similarly to the modifier composition, they lead to local, rather than global selectivity differences.