Rapid classification of simulated street drug mixtures using Raman spectroscopy and principal component analysis

Superhydrophobic Surface Modification of Polymer Microneedles Enables Fabrication of Multimodal Surface-Enhanced Raman Spectroscopy and Mass Spectrometry Substrates for Synthetic Drug Detection in Blood Plasma

Microneedles are widely used substrates for various chemical and biological sensing applications utilizing surface-enhanced Raman spectroscopy (SERS), which is indeed a highly sensitive and specific analytical approach. This article reports the fabrication of a nanoparticle (NP)-decorated microneedle substrate that is both a SERS substrate and a substrate-supported electrospray ionization (ssESI) mass spectrometry (MS) sample ionization platform. Polymeric ligand-functionalized gold nanorods (Au NRs) are adsorbed onto superhydrophobic surface-modified polydimethylsiloxane (PDMS) microneedles through the control of various interfacial interactions. We show that the chain length of the polymer ligands dictates the NR adsorption process. Importantly, assembling Au NRs onto the micrometer-diameter needle tips allows the formation of highly concentrated electromagnetic hot spots, which provide the SERS enhancement factor as high as 1.0 × 106. The micrometer-sized area of the microneedle top and high electromagnetic field enhancement of our system can be loosely compared with tip-enhanced Raman spectroscopy, where the apex of a plasmonic NP-functionalized sharp probe produces high-intensity plasmonic hot spots. Utilizing our NR-decorated microneedle substrates, the synthetic drugs fentanyl and alprazolam are analyzed with a subpicomolar limit of detection. Further analysis of drug-molecule interactions on the NR surface utilizing the Langmuir adsorption model suggests that the higher polarizability of fentanyl allows for a stronger interaction with hydrophilic polymer layers on the NR surface. We further demonstrate the translational aspect of the microneedle substrate for both SERS- and ssESI-MS-based detection of these two potent drugs in 10 drug-of-abuse (DOA) patient plasma samples with minimal preanalysis sample preparation steps. Chemometric analysis for the SERS-based detection shows a very good classification between fentanyl, alprazolam, or a mixture thereof in our selected 10 samples. Most importantly, ssESI-MS analysis also successfully identifies fentanyl or alprazolam in these same 10 DOA plasma samples. We believe that our multimodal detection approach presented herein is a highly versatile detection technology that can be applicable to the detection of any analyte type without performing any complicated sample preparation.

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.

RamanNet: a lightweight convolutional neural network for bacterial identification based on Raman spectra

Raman spectroscopy combined convolutional neural network (CNN) enables rapid and accurate identification of the species of bacteria. However, the existing CNN requires a complex hyperparameters model design. Herein, we propose a new simple network architecture with less hyperparameter design and low computation cost, RamanNet, for rapid and accurate identifying of bacteria at the species level based on its Raman spectra. We verified that compared with the previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset and PKU-bacterial Raman spectral datasets, but using only about 1/45 and 1/297 network parameters, respectively. RamanNet achieved an average isolate-level accuracy of 84.7 ± 0.3%, antibiotic treatment identification accuracy of 97.1 ± 0.3%, and distinguished accuracy of 81.6 ± 0.9% for methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) on the Bacteria-ID dataset, respectively. Moreover, it achieved an average accuracy of 96.04% on the PKU-bacterial dataset. The RamanNet model benefited from fewer model parameters that can be quickly trained even using CPU. Therefore, our method has the potential to rapidly and accurately identify bacterial species based on their Raman spectra and can be easily extended to other classification tasks based on Raman spectra.

We propose a novel CNN model named RamanNet for rapid and accurate identification of bacteria at the species-level based on Raman spectra. Compared to previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset.

Optimization of electromagnetic hot spots in surface-enhanced Raman scattering substrates for an ultrasensitive drug assay of emergency department patients' plasma

Herein we report the programmable preparation of ultrasensitive surface-enhanced Raman scattering (SERS)-based nanoplasmonic superlattice substrates to assay fentanyl and cocaine (detection and quantification) from 10 μL aliquots of emergency department patient plasma without the need for purification steps. Highly homogeneous three-dimensional (3D) nanoplasmonic superlattices are generated through the droplet evaporation-based self-assembly process of chemically-synthesized, polyethylene glycol thiolate-coated gold triangular nanoprisms (Au TNPs). Close-packed, solid-state 3D superlattice substrates produce electromagnetic hot spots due to near-field plasmonic coupling of Au TNPs, which display unique localized surface plasmonic resonance properties. These uniquely prepared superlattice substrates enable strong SERS enhancement to achieve a parts-per-quadrillion limit of detection using the label-free SERS-based technique. Our reported limit of detection is at least 100-fold better than any known SERS substrates for the drug assay. Importantly, our density functional theory calculations show that a specific electronic interaction between the drug molecule and novel nanoplasmonic superlattice substrates plays a critical role that may trigger achieving this unprecedentedly high sensitivity. Additionally, we show high selectivity of the superlattice substrate in the SERS-based detection of analytes from different patient samples, which do and do not contain target analytes (i.e., fentanyl and/or cocaine). The demonstrated sensitivity and selectivity of 3D superlattice substrates for SERS-based drug analysis in real toxicological samples are expected to advance the field of measurement science, and forensic and clinical toxicology by obviating the need for complicated sample processing steps, long assay times, and the low sensitivity of existing "gold standard" analytical techniques including gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry and enzyme-linked immunosorbent assays. Taken together, we believe that this entirely new and reproducible superlattice substrate for the SERS analysis will aid scientific, forensic, and healthcare communities to battle the drug overdose epidemic in the United States.

Intended and unintended use of cathinone mixtures

INTRODUCTION

Cathinones are one of the most popular categories of new psychoactive substances (NPS) consumed. Cathinones have different pharmacological activities and receptor selectivity for monoamine transporters based on their chemical structures. They are incorporated into NPS mixtures and used with other NPS or 'traditional' drugs. Cathinone use represents significant health risks to individuals and is a public health burden.

METHODS

Evidence of poly-NPS use with cathinones, seizure information, and literature analyses results on NPS mixtures was systematically gathered from online database sources, including Google Scholar, Scopus, Bluelight, and Drugs-Forum.

RESULTS AND DISCUSSION

Results highlight the prevalence of NPS with low purity, incorporation of cathinones into NPS mixtures since 2008, and multiple members of the cathinone family being present in individual UK-seized samples. Cathinones were identified as adulterants in NPS marketed as being pure NPS, drugs of abuse, branded products, herbal blends, and products labelled "not for human consumption." Toxicity resulting from cathinone mixtures is unpredictable because key attributes remain largely unknown. Symptoms of intoxication include neuro-psychological, psychiatric, and metabolic symptoms. Proposed treatment includes holistic approaches involving psychosocial, psychiatric and pharmacological interventions.

CONCLUSION

Raising awareness of NPS, education, and training of health care professionals are paramount in reducing harms related to cathinone use.

Differentiation of aged fibers by Raman spectroscopy and multivariate data analysis

Raman spectroscopy followed by multivariate data analysis was used to analyze cotton fibers dyed using similar formulations and submitted to different aging conditions. Spectra were collected on a commercial instrument using a near-infrared laser with a 780nm light source. Discriminant analysis allowed to correctly classify the aged fibers 100% of the time. The prediction ability of the calculated model was estimated to be 100% by the "leave-one-out" cross-validation for 3 out of the 4 series under investigation. Finally, reliability of the developed approach for the discrimination of aged vs new fibers was confirmed by the analysis of commercial polyamide and polyester textiles submitted to the same aging process.

Drug stability analysis by Raman spectroscopy

Pharmaceutical drugs are available to astronauts to help them overcome the deleterious effects of weightlessness, sickness and injuries. Unfortunately, recent studies have shown that some of the drugs currently used may degrade more rapidly in space, losing their potency before their expiration dates. To complicate matters, the degradation products of some drugs can be toxic. Here, we present a preliminary investigation of the ability of Raman spectroscopy to quantify mixtures of four drugs; acetaminophen, azithromycin, epinephrine, and lidocaine, with their primary degradation products. The Raman spectra for the mixtures were replicated by adding the pure spectra of the drug and its degradant to determine the relative percent contributions using classical least squares. This multivariate approach allowed determining concentrations in ~10 min with a limit of detection of ~4% of the degradant. These results suggest that a Raman analyzer could be used to assess drug potency, nondestructively, at the time of use to ensure crewmember safety.

Remote identification of the invasive tunicate Didemnum vexillum using reflectance spectroscopy

Benthic coverage of the invasive tunicate Didemnum vexillum on Georges Bank is largely unknown. Monitoring of D. vexillum coverage is vital to understanding the impact this invasive species will have on the productive fishing grounds of Georges Bank. Here we investigate using reflectance spectroscopy as a method for remote identification of D. vexillum. Using two different systems, a NightSea Dive-Spec and a combination of LED light sources with a hyperspectral radiometer, we collected in-situ measurements of reflectance from D. vexillum colonies. In comparison to reflectance spectra of other common benthic substrates, D. vexillum appears to have a unique spectral signature between 500 and 600 nm. Measuring the slope of the spectrum between these wavelengths appears to be the most robust method for spectral identification. Using derivative analysis or principal component analysis, the reflectance spectra of D. vexillum can be identified among numerous other spectra of common benthic substrates. An optical system consisting of a radiometer, light source, and camera was deployed on a remotely operated vehicle to test the feasibility of using reflectance to assess D. vexillum coverage. Preliminary results, analyzed here, prove the method to be successful for the areas we surveyed and open the way for its use on large-scale surveys.

Raman Spectroscopy for Homeland Security Applications

Raman spectroscopy is an analytical technique with vast applications in the homeland security and defense arenas. The Raman effect is defined by the inelastic interaction of the incident laser with the analyte molecule’s vibrational modes, which can be exploited to detect and identify chemicals in various environments and for the detection of hazards in the field, at checkpoints, or in a forensic laboratory with no contact with the substance. A major source of error that overwhelms the Raman signal is fluorescence caused by the background and the sample matrix. Novel methods are being developed to enhance the Raman signal’s sensitivity and to reduce the effects of fluorescence by altering how the hazard material interacts with its environment and the incident laser. Basic Raman techniques applicable to homeland security applications include conventional (off-resonance) Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), resonance Raman spectroscopy, and spatially or temporally offset Raman spectroscopy (SORS and TORS). Additional emerging Raman techniques, including remote Raman detection, Raman imaging, and Heterodyne imaging, are being developed to further enhance the Raman signal, mitigate fluorescence effects, and monitor hazards at a distance for use in homeland security and defense applications.

Noninvasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis

In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for noninvasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform noninvasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.

Qualitative analysis and the answer box: a perspective on portable Raman spectroscopy

Miniaturization of Raman instruments has created a new genre of devices for qualitative analysis of materials. These new devices are introducing Raman spectroscopy to a diverse range of applications.

Pill characterization data streams for reducing exposure to inadequately identified anti-malarial medication in developing countries

Background

A large fraction of anti-malaria medicines (and indeed many other medicines classes) used in developing countries are inadequately identified. Framing this problem as one of misidentification rather than the more common framing of criminal misrepresentation leads to new solutions sets not currently being considered.

Method

That reframing led to consideration and analysis of 4 new problems that informed design of a digital platform technology for delivering a distributed medicine characterization system: 1) problematic interests associated with a focus on preventing counterfeiting, 2) the complexity of the many ways that medicines can deviate from expected identities, 3) the challenge of choosing amongst a diversity of attribute characterization technologies, and 4) the need for a flexible and distributed data aggregation mechanism.

Results

Analysis of those new problems confirmed an initial insight that a previously described digital technology for tracking malaria tests results in infrastructure limited regions could be adapted for characterizing pill attributes. Feasibility is illustrated by describing how the platform design can be implemented using open-source software and commodity computational and communication technology readily available and supportable in developing countries.

Discussion

A system of this type would allow users to answer several questions. Is this medicine what it is supposed to be? Can it be used to treat locally encountered malaria? What has been the experience of others who have used pills having the same identity? Ubiquitous access to global digital telecommunication infrastructure allows the system to generate data streams from these distributed medicine characterization transactions that can be used to map global patterns of use of specifically identified medicines. This can provide feedback necessary to guide efforts to reduce the burden of malaria.