Evaluation of an electrochemical sensor and comparison with spectroscopic approaches as used today in practice for harm reduction in a festival setting-A case study: Analysis of 3,4-methylenedioxymethamphetamine samples.

More and more countries and organisations emphasise the value of harm reduction measures in the context of illicit drug use and abuse. One of these measures is drug checking, a preventive action that can represent a quick win by tailored consultation on the risks of substance use upon analytical screening of a submitted sample. Unlike drop-in centres that operate within a fixed setting, enabling drug checking in a harm reduction context at events requires portable, easy to use analytical approaches, operated by personnel with limited knowledge of analytical chemistry. In this case study, four different approaches were compared for the characterisation of 3,4-methylenedioxymethamphetamine samples and this in the way the approaches would be applied today in an event context. The four approaches are mid-infrared (MIR), near-infrared, and Raman spectroscopy, which are today used in drug checking context in Belgium, as well as an electrochemical sensor approach initially developed in the context of law enforcement at ports. The MIR and the electrochemical approach came out best, with the latter allowing for a direct straightforward analysis of the percentage 3,4-methylenedioxymethamphetamine (as base equivalent) in the samples. However, MIR has the advantage that, in a broader drug checking context, it allows to screen for several molecules and so is able to identify unexpected active components or at least the group to which such components belong. The latter is also an important advantage in the context of the growing emergence of new psychotropic substances.

Characterization of CBD oils, seized on the Belgian market, using infrared spectroscopy: Matrix identification and CBD determination, a proof of concept.

The availability of cannabidiol (CBD) oil products has increased in recent years. No analytical controls are mandatory for these products leading to uncertainties about composition and quality. In this paper, a methodology was developed to identify the oil matrix and to estimate the CBD content in such samples, using mid-infrared and near-infrared spectroscopy. Different oils were selected based on the information labeled on products and were bought in food stores in order to create a sample set with a variety of matrices. These oils were spiked with CBD to obtain samples with CBD levels from 0% to 20%. The first part of the study was focused on the qualitative analysis of the oil matrix. A classification model, based on Soft Independent Modeling of Class Analogy, was build using the spiked oils to distinguish between the different oil matrices. For both spectroscopic techniques, the sensitivity, the specificity, the accuracy and the precision were equal to 100%. These models were applied to determine the oil matrix of seized samples. The second part of the study was focused on the quantitative estimation of CBD. After determination of CBD in seized samples using gas chromatography-tandem mass spectrometry, partial least square regression (PLS-R) models were built, one for each matrix in the sample set. Both techniques were able to classify unknown oily samples according to their matrix, and although only few samples were available to evaluate the PLS-R models, the approach clearly showed promising results for the estimation of the CBD content in oil samples.

Evaluation of Multivariate Filters on Vibrational Spectroscopic Fingerprints for the PLS-DA and SIMCA Classification of Argan Oils from Four Moroccan Regions.

This study aimed to develop an analytical method to determine the geographical origin of Moroccan Argan oil through near-infrared (NIR) or mid-infrared (MIR) spectroscopic fingerprints. However, the classification may be problematic due to the spectral similarity of the components in the samples. Therefore, unsupervised and supervised classification methods-including principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA)-were evaluated to distinguish between Argan oils from four regions. The spectra of 93 samples were acquired and preprocessed using both standard preprocessing methods and multivariate filters, such as External Parameter Orthogonalization, Generalized Least Squares Weighting and Orthogonal Signal Correction, to improve the models. Their accuracy, precision, sensitivity, and selectivity were used to evaluate the performance of the models. SIMCA and PLS-DA models generated after standard preprocessing failed to correctly classify all samples. However, successful models were produced after using multivariate filters. The NIR and MIR classification models show an equivalent accuracy. The PLS-DA models outperformed the SIMCA with 100% accuracy, specificity, sensitivity and precision. In conclusion, the studied multivariate filters are applicable on the spectroscopic fingerprints to geographically identify the Argan oils in routine monitoring, significantly reducing analysis costs and time.

Quality Control and Authentication of Argan Oils: Application of Advanced Analytical Techniques.

In addition to the nutritional and therapeutic benefits, Argan oil is praised for its unique bio-ecological and botanic interest. It has been used for centuries to treat cardiovascular issues, diabetes, and skin infections, as well as for its anti-inflammatory and antiproliferative properties. Argan oil is widely commercialized as a result of these characteristics. However, falsifiers deliberately blend Argan oil with cheaper vegetable oils to make economic profits. This reduces the quality and might result in health issues for consumers. Analytical techniques that are rapid, precise, and accurate are employed to monitor its quality, safety, and authenticity. This review provides a comprehensive overview of studies on the quality assessment of Moroccan Argan oil using both untargeted and targeted approaches. To extract relevant information on quality and adulteration, the analytical data are coupled with chemometric techniques.

Discrepancies between validated GC-FID and UHPLC-DAD methods for the analysis of Δ-9-THC and CBD in dried hemp flowers.

Herbal products for smoking containing cannabidiol (CBD) are available as "low-tetrahydrocannabinol cannabis products" in most EU countries. In Belgium, Δ9-tetrahydrocannabinol (THC) content of these products must be less than 0.2% w/w, which is also the limit for agricultural hemp. For agricultural hemp, the official and only valid method for European regulators is gas-chromatography coupled to flame ionization detector (GC-FID). There is no such method, for smoking products. Many of these herbal for smoking products are analyzed as part of their quality control and have certificate of analysis. During surveillance by official labs, discrepancies were seen between the official results and the certificate of analysis. In this study, a GC-FID method based on the European method and an ultra-high-performance liquid chromatography coupled to diode array detection (UHPLC-DAD) method were validated and applied for samples analysis in order to investigate these discrepancies. The GC-FID method shows better results for the validation parameters; notably, it has ß-expectation tolerance limits within 10% with a ß value of 95% while the validated UHPLC-DAD method has ß-expectation tolerance limits within 15% with a ß value of 90%. Furthermore, the other parameters evaluated are generally better with the GC-FID method. The statistic t test shows that the difference between both methods was significantly different for total-THC, but not significantly different for the total-CBD. The authors state that, as for agricultural hemp, the GC-FID method is to be preferred for the analysis of THC and CBD in products for smoking.

Relative response factors and multiple regression models in liquid chromatography to quantify low-dosed components using alternative standards-proof of concept: total Δ9-THC content in cannabis flowers using CBD as reference.

A classical quantitative analysis in liquid chromatography is performed using either a one-point calibration or a calibration line, prepared using a reference standard of the compound(s) of interest. However, in some cases, adequate reference standards may be very expensive, rare to obtain, or have limited shelf-life properties. Also, in herbal matrices, multiple compounds could be necessary to be quantified, needing a whole series of different (related) reference standards. In these cases, the use of relative response (sometimes called relative correction factors) factors (RRFs) towards reference standards, different of the compound to be quantified, gained attraction. This study performed a comparison of the use of RRFs and linear relative response factor models (LRRFM) for the quantification of targeted low-dosed compounds using an alternative standard, since it is known that classical RRFs often fail in lower concentration ranges. For this purpose, the determination of the total Δ9-tetrahydrocannabinol (Δ9-THC + Δ9-THC-A) content in dried cannabis flowers, using UHPLC-DAD, was used as a case study. A chromatographic method was implemented and validated, and the use of classical calibration lines, classical RRF, and the LRRFM was applied and compared, with special focus on the concentration around 0.2% (w/w) total Δ9-THC, the legal limit (in most European countries) in these products. Results showed that the newly presented and validated LRRFM approach outperformed the classical RRFs, especially in the low-concentration ranges and that concentrations obtained with the LRRFM were in accordance with the interpolation results obtained with a calibration line.

CBD oils on the Belgian market: A validated MRM GC-MS/MS method for routine quality control using QuEChERS sample clean up.

Quality control of CBD oils on the Belgium market showed that the CBD content not always corresponds to the label claim. There is a pressing need to develop new analytical methods specifically developed to the assay of such oily samples. Analytical issues are, however, encountered for routine analyses due to the matrix complexity, high cost of cannabinoid standards and low Δ9-THC concentrations. An oily matrix could cause technical damages to analytical instruments and reduce the lifetime of the chromatographic columns. This paper proposes a procedure combining a sample cleanup by QuEChERS, removing the oily matrix, followed by a validated MRM GC-MS/MS method for the routine analysis of CBD oil samples. Eighteen CBD samples were selected on the Belgium market for analysis. This method allows the quantification of CBD, the legality check for the Δ9-THC content by a CBN standard and the screening of seven other cannabinoids namely CBN, CBDV, CBT, CBC, Δ8-THC, THCV and CBG. The method was validated at three concentration levels (0.5-1-2% (w/v)) for CBD and (0.05-0.1-0.2% (w/v)) for CBN. The detection limits for CBT, CBD, CBC, Δ8-THC, CBN and for the other cannabinoids of interest, were 10 and 14 ng/mL respectively. The accuracy profile values for CBD and CBN showed that the ß-expectation tolerance intervals did not exceed the acceptance limits of ± 20%, meaning that 90% of future measurements will be included within this error range.

An infrared spectroscopic approach to characterise white powders, easily applicable in the context of drug checking, drug prevention and on-site analysis.

More and more events, such as the summer music festivals, are considering the possibilities for implementing on-site testing of psychoactive drugs in the context of prevention and harm reduction. Although the on-site identification is already implemented by plenty of drug checking services, the required rapid quantitative dosing of the composition of illicit substances is still a missing aspect for a successful harm reduction strategy at events. In this paper, an approach is presented to identify white powders as amphetamine, cocaine, ketamine or others and to estimate the purity of the amphetamine, cocaine and ketamine samples using spectroscopic techniques hyphenated with partial least squares (PLS) modelling. For identification purposes, it was observed that mid-infrared spectroscopy hyphenated with PLS-discriminant analysis allowed the distinction between amphetamine, cocaine, ketamine and other samples and this with a correct classification rate of 93.1% for an external test set. For quantitative estimation, near-infrared spectroscopy was more performant and allowed the estimation of the dosage/purity of the amphetamine, cocaine and ketamine samples with an error of more or less 10% w/w. An easily applicable, practical and cost-effective approach for on-site characterisation of the majority of the psychoactive samples encountered in Belgian nightlife settings based on IR spectroscopy was proposed.

Spectroscopic techniques combined with chemometrics for fast on-site characterization of suspected illegal antimicrobials.

The threats of substandard and falsified (SF) antimicrobials, posed to public health, include serious adverse drug effects, treatment failures and even development of antimicrobial resistance. Next to these issues, it has no doubt that efficient methods for on-site screening are required to avoid that SF antimicrobials reach the patient or even infiltrate the legal supply chain. This study aims to develop a fast on-site screening method for SF antimicrobials using spectroscopic techniques (mid infrared, benchtop near infrared, portable near infrared and Raman spectroscopy) combined with chemometrics. 58 real-life illegal antimicrobials (claiming 18 different antimicrobials and one beta-lactamase inhibitor) confiscated by the Belgian Federal Agency for Medicines and Health Products (FAMHP) and 14 genuine antimicrobials were analyzed and used to build and validate models. Two types of models were developed and validated using supervised chemometric tools. One was used for the identification of the active pharmaceutical ingredients (APIs) by applying partial least squares-discriminant analysis (PLS-DA) and another one was used for the detection of non-compliant (overdosed or underdosed) samples by applying PLS-DA, k-nearest neighbors (k-NN) and soft independent modelling by class analogy (SIMCA). The best model capable of identifying amoxicillin and clavulanic acid (co-amoxiclav), azithromycin, co-trimoxazole and amoxicillin was based on the mid-infrared spectra with a correct classification rate (ccr) of 100%. The optimal model capable of detecting non-compliant samples within the combined group of amoxicillin and co-amoxiclav via SIMCA showed a ccr for the test set of 88% (7/8) using mid infrared or benchtop near infrared spectroscopy. The best model for detecting non-compliant samples within the group of amoxicillin via SIMCA was obtained using mid-infrared or Raman spectra, resulting in a ccr of 80% for the test set (4/5) and a ccr for calibration of 100%. For the group of co-amoxiclav, the optimal models showed a ccr of 100% for the detection of non-compliant samples by applying mid-infrared, benchtop near infrared or portable near infrared spectroscopy. Taken together, the obtained models, hyphenating spectroscopic techniques and chemometrics, enable to easily identify suspected SF antimicrobials and to differentiate non-compliant samples from compliant ones.

Discrimination of legal and illegal Cannabis spp. according to European legislation using near infrared spectroscopy and chemometrics.

Aerial parts containing cannabidiol can be purchased in a legal way but cannabis used as recreational drug is illegal in most European countries. Δ9-tetrahydrocannabinol is one of the main cannabinoids responsible for the psychotropic effect. European Union countries and Switzerland authorize a concentration of THC of 0.2 % and 1.0 % w/w, respectively, for smoking products and industrial hemp. Public health inspectors and law enforcement officers need to check the legality of samples. Therefore there is a need for innovative approaches, allowing quality control of these products in an easy way and preferably on site. In many countries, cultivation of industrial hemp is permitted if the THC content does not exceed 0.2 % w/w. A portable equipment could be a useful measuring tool for farmers to check for the THC content at regular time. In this work, 189 samples were analysed with a benchtop and a handheld NIR device in order to create two classification methods according to European and Swiss laws. All samples were also analysed by GC-FID to determine their THC concentration. Supervised analysis was applied in order to establish the best model. For the first classification, the accuracy was 91% for the test set with the benchtop data and 93 % for the test set with the handheld data. For the second classification, the accuracies were respectively 91 % and 95 %. The obtained models, hyphenating spectroscopic techniques and chemometrics, enable to discriminate legal and illegal cannabis samples according to European and Swiss laws.

A new potential anti-cancer beta-carboline derivative decreases the expression levels of key proteins involved in glioma aggressiveness: A proteomic investigation.

Gliomas remain highly fatal due to their high resistance to current therapies. Deregulation of protein synthesis contributes to cancer onset and progression and is a source of rising interest for new drugs. CM16, a harmine derivative with predicted high blood-brain barrier penetration, exerts antiproliferative effects partly through translation inhibition. We evaluated herein how CM16 alters the proteome of glioma cells. The analysis of the gel-free LC/MS and auto-MS/MS data showed that CM16 induces time- and concentration-dependent significant changes in the total ion current chromatograms. In addition, we observed spontaneous clustering of the samples according to their treatment condition and their proper classification by unsupervised and supervised analyses, respectively. A two-dimensional gel-based approach analysis allowed us to identify that treatment with CM16 may downregulate four key proteins involved in glioma aggressiveness and associated with poor patient survival (HspB1, BTF3, PGAM1, and cofilin), while it may upregulate galectin-1 and Ebp1. Consistently with the protein synthesis inhibition properties of CM16, HspB1, Ebp1, and BTF3 exert known roles in protein synthesis. In conclusion, the downregulation of HspB1, BTF3, PGAM1 and cofilin bring new insights in CM16 antiproliferative effects, further supporting CM16 as an interesting protein synthesis inhibitor to combat glioma.

Determination of Three Main Chlorogenic Acids in Water Extracts of Coffee Leaves by Liquid Chromatography Coupled to an Electrochemical Detector.

Coffee is a beverage widely consumed in the world. The coffee species most commercialized worldwide are Arabica (Coffea arabica) and Robusta (Coffea canephora). Roasted coffee beans are the most used, but coffee leaves are also consumed as infusion in several countries for traditional medicinal purposes. They contain several interesting phenolic antioxidant compounds mainly belonging to chlorogenic acids (CGAs). In the present work, a liquid chromatography-electrochemical detection (LC-EC) method was developed for the determination of three main chlorogenic acid isomers, namely 3-, 4-, and 5-caffeoylquinic acids (CQA), in coffee leaves aqueous extracts. Samples from eight coffee species, namely; Coffea arabica, Coffea canephora, Coffea liberica, Coffea humilis, Coffea mannii, Coffea charrieriana, Coffea anthonyi, and Coffea liberica var. liberica, were grown and collected in tropical greenhouses. Linearity of the calibration graphs was observed in the range from the limit of quantification to 1.0 × 10-5 M, with R² equal to 99.9% in all cases. High sensitivity was achieved with a limit of detection of 1.0 × 10-8 M for 3-CQA and 5-CQA (i.e., 3.5 µg/L) and 2.0 × 10-8 M for 4-CQA (i.e., 7.1 µg/L). The chromatographic profile of the samples harvested for each Coffea species was studied comparatively. Obtained raw data were pretreated for baseline variations and shifts in retention times between the chromatographic profiles. Principal Component Analysis (PCA) was applied to the pretreated data. According to the results, three clusters of Coffea species were found. In the water sample extracts, 5-CQA appeared to be the major isomer, and some species contained a very low amount of CQAs. Fluctuations were observed depending on the Coffea species and harvesting period. Significant differences between January and July were noticed regarding CQAs content. The species with the best CQAs/caffeine ratio was identified. The LC-EC data were validated by liquid chromatography-high resolution mass spectrometry (LC-HRMS).

Identification of coffee leaves using FT-NIR spectroscopy and SIMCA.

Abundant literature has been devoted to coffee beans (green or roasted) chemical description but relatively few studies have been devoted to coffee leaves. Given the fact that coffee leaves are used for food and medicinal consumption, it was of interest to develop a rapid screening method in order to identify coffee leaves taxa. Investigation by Fourier - Transform near infrared spectroscopy (FT-NIRS) was performed on nine Coffea taxa leaves harvested over one year in a tropical greenhouse of the Botanic Garden Meise (Belgium). The only process after leaves harvesting was an effective drying and a homogeneous leaves grinding. FT-NIRS with SIMCA analysis allowed to discriminate the spectral profiles across taxon, aging stage (mature and senescence coffee leaves) and harvest period. This study showed that it was possible (i) to classify the different taxa, (ii) to identify their aging stage and (iii) to identify the harvest period for the mature stage with a correct classification rate of 99%, 100% and 90%, respectively.