Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer

Trace Detection of Adulterants in Illicit Opioid Samples Using Surface-Enhanced Raman Scattering and Random Forest Classification

The detection of trace adulterants in opioid samples is an important aspect of drug checking, a harm reduction measure that is required as a result of the variability and unpredictability of the illicit drug supply. While many analytical methods are suitable for such analysis, community-based approaches require techniques that are amenable to point-of-care applications with minimal sample preparation and automated analysis. We demonstrate that surface-enhanced Raman spectroscopy (SERS), combined with a random forest classifier, is able to detect the presence of two common sedatives, bromazolam (0.32-36% w/w) and xylazine (0.15-15% w/w), found in street opioid samples collected as a part of a community drug checking service. The Raman predictions, benchmarked against mass spectrometry results, exhibited high specificity (88% for bromazolam, 96% for xylazine) and sensitivity (88% for bromazolam, 92% for xylazine) for the compounds of interest. We additionally provide evidence that this exceeds the performance of a more conventional approach using infrared spectral data acquired on the same samples. This demonstrates the feasibility of SERS for point-of-care analysis of challenging multicomponent samples containing trace adulterants.

Combining surface-enhanced Raman spectroscopy (SERS) and paper spray mass spectrometry (PS-MS) for illicit drug detection

There is an ongoing effort in the US illicit drug market to make new psychoactive compounds more potent and addictive. Due to continuous chemical modifications, many fentanyl analogs are developed and mixed with more traditional illicit drugs, such as cocaine and heroin. Detecting fentanyl and fentanyl analogs in these illicit drug mixtures has become more crucial because of the increased potency and associated health risks. Most confirmatory procedures require time-consuming and expensive, highly sophisticated laboratory equipment and experimental procedures, which can delay critical information that might save a victim or find a suspect. In this study, we propose miniaturizing and accelerating this process by combining surface-enhanced Raman spectroscopy (SERS) analysis and paper spray mass spectrometry (PS-MS). For this aim, dual-purposed paper substrates were developed through soaking in Au/Ag nanostars suspensions. These novel, in-house prepared paper SERS substrates showed stability for up to four weeks with and without the presence of drug compounds. Fentanyl analogs with similar SERS spectra were differentiated by coupling with PS-MS. The limit of detection (LOD) for fentanyl on the paper substrates is 34 μg/mL and 0.32 μg/mL for SERS and PS-MS, respectively. Fentanyl and fentanyl analogs show selective SERS enhancement that helped to detect trace amounts of these opioids in heroin and cocaine street samples. In short, we propose the combination of SERS/PS-MS by using modified paper substrates to develop cost-effective, sensitive, rapid, portable, reliable, and reproducible methods to detect illicit drugs, especially trace amounts of fentanyl and fentanyl analogs in illicit drug mixtures. The combination of these two category A techniques allows for the identification of illicit drugs according to the SWGDRUG guidelines.

Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors

The opioid overdose crisis is a global health challenge. Fentanyl, an exceedingly potent synthetic opioid, has emerged as a leading contributor to the surge in opioid-related overdose deaths. The surge in overdose fatalities, particularly due to illicitly manufactured fentanyl and its contamination of street drugs, emphasizes the urgency for drug-testing technologies that can quickly and accurately identify fentanyl from other drugs and quantify trace amounts of fentanyl. In this paper, gold nanoparticle (AuNP)-decorated single-walled carbon nanotube (SWCNT)-based field-effect transistors (FETs) are utilized for machine learning-assisted identification of fentanyl from codeine, hydrocodone, and morphine. The unique sensing performance of fentanyl led to use machine learning approaches for accurate identification of fentanyl. Employing linear discriminant analysis (LDA) with a leave-one-out cross-validation approach, a validation accuracy of 91.2% is achieved. Meanwhile, density functional theory (DFT) calculations reveal the factors that contributed to the enhanced sensitivity of the Au-SWCNT FET sensor toward fentanyl as well as the underlying sensing mechanism. Finally, fentanyl antibodies are introduced to the Au-SWCNT FET sensor as specific receptors, expanding the linear range of the sensor in the lower concentration range, and enabling ultrasensitive detection of fentanyl with a limit of detection at 10.8 fg mL-1.

Differentiating Structurally Similar Fentanyl Analogs by Comparing Density Functional Theory (DFT) Calculations and Surface-Enhanced Raman Spectroscopy (SERS) Results

Fentanyl and fentanyl analogs are the main cause of recent overdose deaths in the United States. The presence of fentanyl analogs in illicit drugs makes it difficult to estimate their potencies. This makes the detection and differentiation of fentanyl analogs critically significant. Surface-enhanced Raman spectroscopy (SERS) can differentiate structurally similar fentanyl analogs by yielding spectroscopic fingerprints for the detected molecules. In previous years, five fentanyl analogs, carfentanil, furanyl fentanyl, acetyl fentanyl, 4-fluoroisobutyryl fentanyl (4-FIBF), and cyclopropyl fentanyl (CPrF), gained popularity and were found in 76.4% of the fentanyl analogs trafficked. In this study, we focused on 4-FIBF, CPrF, and structurally similar fentanyl analogs. We developed methods to differentiate these fentanyl analogs using theoretical and experimental methods. To do this, a set of fentanyl analogs were examined using density functional theory (DFT) calculations. The DFT results obtained in this project permitted the assignment of spectral bands. These results were then compared with normal Raman and SERS techniques. Structurally similar fentanyl analogs show important differences in their spectra, and they have been visually differentiated from each other both theoretically and experimentally. Additional results using principal component analysis and soft independent modeling of class analogy show they can be distinguished using this technique. The limit of detection values for FIBF and CPrF were determined to be 0.35 ng/mL and 4.4 ng/mL, respectively, using SERS. Experimental results obtained in this project can be readily implemented in field applications and smaller laboratories, where inexpensive portable Raman spectrometers are often present and used in drug analysis.

Full Characterisation of Heroin Samples Using Infrared Spectroscopy and Multivariate Calibration

The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed.

Identification of liquor adulteration by Raman spectroscopy method based on ICNAFS

The health of consumers can be impacted by the additives placed into the liquor. To address the issues of poor accuracy, low reliability, and complex operational procedures in identifying adulteration in existing liquor, an improved convex non-negative matrix factorization (ICNAFS) with an adaptive graph constraint for unsupervised feature extraction is proposed in this paper, with the goal of achieving rapid identification of adulteration in liquor by Raman spectroscopy through dimensionality reduction. For the sake to streamline the calculation process for effective feature extraction and increase the accuracy of the analyzed model, the proposed ICNAFS method incorporates two fundamental models, such as ridge regression and convex non-negative matrix factorization (NMF). In particular, dimensionality reduction of the original spectrum is initially conducted using Principal Component Analysis (PCA), Sequential Projection Algorithm (SPA), Convex Non-Negative Matrix Factorization with an Adaptive Graph Constraint (CNAFS), and ICNAFS respectively. k-means is subsequently employed to merge the four models for clustering analysis. The results suggest that the accuracy of the presented ICNAFS-assisted k-means model is higher than the other techniques, with a clustering accuracy of 98.67%, exhibiting a 4% improvement over the existing CNAFS, through examination of 150 sets of tainted liquor data from five categories of samples. This demonstrates the potency of the proposed ICNAFS-assisted k-means clustering model in conjunction with Raman spectroscopy as a method for detecting tainted liquor.

Quantitative Raman Cross-Sections and Band Assignments for Fentanyl and Fentanyl Analogs

Raman cross sections and spectra were measured for five synthetic opioid fentanyl analogs: fentanyl citrate, sufentanil citrate, alfentanil HCl, carfentanil oxalate, and remifentanil HCl. The measurements were performed with excitation wavelengths in the visible (532 nm) and near infrared (785 nm). In addition, density functional theory (DFT) calculations were employed to generate simulated spectra of the compounds and aid in identification of the observed spectral modes. These cross-section measurements and calculations were also used to assess results from a series of measurements of fentanyls cut with other powdered materials. These measurements are valuable for assessment of field-deployable Raman chemical sensors for detection of fentanyl and fentanyl analogs, including when mixed with other materials.

Point-of-care community drug checking technologies: an insider look at the scientific principles and practical considerations

Drug checking is increasingly being explored outside of festivals and events to be an ongoing service within communities, frequently integrated within responses to illicit drug overdose. The choice of instrumentation is a common question, and the demands on these chemical analytical instruments can be challenging as illicit substances may be more complex and include highly potent ingredients at trace levels. The answer remains nuanced as the instruments themselves are not directly comparable nor are the local demands on the service, meaning implementation factors heavily influence the assessment and effectiveness of instruments. In this perspective, we provide a technical but accessible introduction to the background of a few common drug checking methods aimed at current and potential drug checking service providers. We discuss the following tools that have been used as part of the Vancouver Island Drug Checking Project in Victoria, Canada: immunoassay test strips, attenuated total reflection IR-absorption spectroscopy, Raman spectroscopy from powder samples, surface-enhanced Raman scattering in a solution of colloidal gold nanoparticles, and gas chromatography-mass spectrometry. Using four different drug mixtures received and tested at the service, we illustrate the strengths, limitations, and capabilities of such instruments, and expose the scientific theory to give further insight into their analytical results. Each case study provides a walk-through-style analysis for a practical comparison between data from several different instruments acquired on the same sample. Ideally, a single instrument would be able to achieve all of the objectives of drug checking. However, there is no clear instrument that ticks every box; low cost, portable, rapid, easy-to-use and provides highly sensitive identification and accurate quantification. Multi-instrument approaches to drug checking may be required to effectively respond to increasingly complex and highly potent substances demanding trace level detection and the potential for quantification.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Surface-enhanced Raman spectroscopy for drug discovery: peptide-RNA binding

The ever-growing demand for new drugs highlights the need to develop novel cost- and time-effective techniques for drug discovery. Surface-enhanced Raman spectroscopy (SERS) is an emerging ultrasensitive and label-free technique that allows for the efficient detection and characterization of molecular interactions. We have recently developed a SERS platform for detecting a single protein molecule linked to a gold substrate (Almehmadi et al. Scientific Reports 2019). In this study, we extended the approach to probe the binding of potential drugs to RNA targets. To demonstrate the proof of concept, two 16-amino acid residue peptides with close primary structures and different binding affinities to the RNA CUG repeat related to myotonic dystrophy were tested. Three-microliter solutions of the RNA repeat with these peptides at nanomolar concentrations were probed using the developed approach, and the binding of only one peptide was demonstrated. The SER spectra exhibited significant fluctuations along with a sudden strong enhancement as spectra were collected consecutively from individual spots. Principal component analysis (PCA) of the SER spectral datasets indicated that free RNA repeats could be differentiated from those complexed with a peptide with 100% accuracy. The developed SERS platform provides a novel opportunity for label-free screening of RNA-binding peptides for drug discovery. Schematic representation of the SERS platform for drug discovery developed in this study.