Call it a “nightshade”—A hierarchical classification approach to identification of hallucinogenic Solanaceae spp. using DART-HRMS-derived chemical signatures

Mass Spectrometric Interrogation of Earwax: Toward the Detection of Ménière's Disease

Many diseases remain difficult to identify because the occurrence of characteristic biomarkers within traditional matrices such as blood and urine remain unknown. Disease diagnosis could, therefore, benefit from the analysis of readily accessible, non-traditional matrices that have a high chemical content and contain distinguishing biomarkers. One such matrix is cerumen (i.e., earwax), whose chemical complexity can pose challenges when analyzed by conventional methods. A combination of cerumen chemical profiles analyzed by gas chromatography-mass spectrometry (GC-MS) and direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) were investigated to ascertain the possible presence of the rare otolaryngological disorder Ménière's disease. This illness is currently identified via "diagnosis by exclusion" in which the disease is distinguished from others with overlapping symptoms by the process of elimination. GC-MS revealed a chemical profile difference between those with and without a Ménière's disease diagnosis by a visually apparent diminution of the compounds present in the Ménière's disease samples. DART-HRMS revealed that the two classes could be differentiated using three fatty acids: cis-9-hexadecenoic acid, cis-10-heptadecenoic acid, and cis-9-octadecenoic acid. These compounds were subsequently quantified by GC-MS and overall, the amounts of these fatty acids were decreased in Ménière's disease patients. The average levels for non-Ménière's disease samples were 7.89 μg/mg for cis-9-hexadecenoic acid, 0.87 μg/mg for cis-10-heptadecenoic acid, and 4.94 μg/mg for cis-9-octadecenoic acid. The average levels for Ménière's disease samples were 1.70 μg/mg for cis-9-hexadecenoic acid, 0.13 μg/mg for cis-10-heptadecenoic acid, and 2.07 μg/mg for cis-9-octadecenoic acid. The confidence levels for cis-9-hexadecenoic acid, cis-10-heptadecenoic acid, and cis-9-octadecenoic acid were 98.7%, 99.9%, and 95.4%, respectively. The results suggest that assessment of the concentrations of these fatty acids could be a useful clinical tool for the more rapid and accurate detection of Ménière's disease.

Combined ambient ionization mass spectrometric and chemometric approach for the differentiation of hemp and marijuana varieties of Cannabis sativa

BACKGROUND

Hemp and marijuana are the two major varieties of Cannabis sativa. While both contain Δ⁹-tetrahydrocannabinol (THC), the primary psychoactive component of C. sativa, they differ in the amount of THC that they contain. Presently, U.S. federal laws stipulate that C. sativa containing greater than 0.3% THC is classified as marijuana, while plant material that contains less than or equal to 0.3% THC is hemp. Current methods to determine THC content are chromatography-based, which requires extensive sample preparation to render the materials into extracts suitable for sample injection, for complete separation and differentiation of THC from all other analytes present. This can create problems for forensic laboratories due to the increased workload associated with the need to analyze and quantify THC in all C. sativa materials.

METHOD

The work presented herein combines direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) and advanced chemometrics to differentiate hemp and marijuana plant materials. Samples were obtained from several sources (e.g., commercial vendors, DEA-registered suppliers, and the recreational Cannabis market). DART-HRMS enabled the interrogation of plant materials with no sample pretreatment. Advanced multivariate data analysis approaches, including random forest and principal component analysis (PCA), were used to optimally differentiate these two varieties with a high level of accuracy.

RESULTS

When PCA was applied to the hemp and marijuana data, distinct clustering that enabled their differentiation was observed. Furthermore, within the marijuana class, subclusters between recreational and DEA-supplied marijuana samples were observed. A separate investigation using the silhouette width index to determine the optimal number of clusters for the marijuana and hemp data revealed this number to be two. Internal validation of the model using random forest demonstrated an accuracy of 98%, while external validation samples were classified with 100% accuracy.

DISCUSSION

The results show that the developed approach would significantly aid in the analysis and differentiation of C. sativa plant materials prior to launching painstaking confirmatory testing using chromatography. However, to maintain and/or enhance the accuracy of the prediction model and keep it from becoming outdated, it will be necessary to continue to expand it to include mass spectral data representative of emerging hemp and marijuana strains/cultivars.

Plasma-based ambient mass spectrometry: Recent progress and applications

Ambient mass spectrometry (AMS) has grown as a group of advanced analytical techniques that allow for the direct sampling and ionization of the analytes in different statuses from their native environment without or with minimum sample pretreatments. As a significant category of AMS, plasma-based AMS has gained a lot of attention due to its features that allow rapid, real-time, high-throughput, in vivo, and in situ analysis in various fields, including bioanalysis, pharmaceuticals, forensics, food safety, and mass spectrometry imaging. Tens of new methods have been developed since the introduction of the first plasma-based AMS technique direct analysis in real-time. This review first provides a comprehensive overview of the established plasma-based AMS techniques from their ion source configurations, mechanisms, and developments. Then, the progress of the representative applications in various scientific fields in the past 4 years (January 2017 to January 2021) has been summarized. Finally, we discuss the current challenges and propose the future directions of plasma-based AMS from our perspective.

Application of direct analysis in real-time mass spectrometry (DART-MS) in forensic science: a comprehensive review

Background

As the rate of crime is constantly increasing, the workload on the forensic analyst also piles up. The availability of a limited number of seized samples makes it crucial to directly analyze the sample, thereby preventing wastage in the prior steps of sample preparation. Due to such needs, the forensic community is consistently working on broadening the usage of direct analysis in real-time mass spectrometry (DART-MS). DART-MS is a relatively new technique for rapid mass spectral analysis. Its use for chemical analysis credits its ability to analyze the sample at atmospheric pressure.

Main body

This article gives insight into the ionization mechanisms, data analysis tools, and the use of hyphenated techniques like thermal-desorption-DART-MS, infrared-thermal-desorption-DART-MS, Joule-heating thermal-desorption-DART-MS, etc. This review summarizes the applications of DART-MS in the field of Forensic Science reported from 2005 to 2021. The applications include analysis of drugs, warfare agents, gun-shot residues, ink differentiation, and other forensically relevant samples. The paper also presents the relation between the type of DART-MS technique and the ionization mode used for a particular class of compounds.

Conclusion

The review follows that the high-resolution mass-spectrometers or low-resolution mass-spectrometers systems in the positive or negative mode were highly dependent on the type of analyte under investigation. Drugs, inks, dyes, and paints were mainly analyzed using the positive ionization mode in the HRMS technique. The examinations of fire accelerants predominantly used the positive ionization mode in the LRMS technique. Moreover, the limit of detection values obtained from the qualitative screening of street drugs were of ppb level, indicating high sensitivity of DART-MS. Considering the work done in the past years, there are potential future research needs of this technology, especially in forensic science.

Graphical Abstract

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.

Workflow for the Supervised Learning of Chemical Data: Efficient Data Reduction-Multivariate Curve Resolution (EDR-MCR)

A new method termed efficient data reduction-multivariate curve resolution (EDR-MCR) has been devised for classification of high-dimensional data. The method introduces the coupling of EDR and MCR as a new strategy for data splitting, variable selection, and supervised classification of high dimensionality data. The method reduces data dimensionality and selects the training set using principal component analysis (PCA) and convex geometry prior to data classification. Then, the reduced data are categorized using an MCR model, in which numerical constraints are imposed to resolve the data into classes and readily interpretable pure component signal weights. The performance of the EDR and supervised MCR methods were tested for their ability to enable discrimination between the constituents of two benchmark and two high-dimensional data sets. The results were compared with the output of the application of different data splitting methods including iterative random selection (IRS), Kennard-Stone (KS), and discrimination methods including partial least-squares-discriminant analysis (PLS-DA) and the ensemble-learning frameworks of linear discriminant analysis (LDA), k-nearest neighbors (KNN), classification and regression trees (CART), and support vector machine (SVM). Overall, EDR resulted in comparable results with other data splitting methods despite the small size of the training set samples that it created. The proposed MCR approach, in comparison with other commonly used supervised techniques, has the advantages of speed in implementation, tuning of fewer parameters, flexibility in the analysis of data characterized by low sample numbers and class imbalances, improved accuracy from the inclusion of additional system information in the form of numerical constraints, and the ability to resolve pure components signal weights.

Forensic applications of DART-MS: A review of recent literature

The need for rapid chemical analyses and new analytical tools in forensic laboratories continues to grow due to case backlogs, difficult-to-analyze cases, and identification of previously unseen materials such as new psychoactive substances. To adapt to these needs, the forensics community has been pursuing the use of ambient ionization mass spectrometry, and more specifically direct analysis in real time mass spectrometry (DART-MS), for a wide range of applications. From the inception of DART-MS forensic applications have been researched with demonstrations ranging from drugs of abuse to inorganic gunshot residue to printer inks to insect identification. This article presents a review of research demonstrating the use of DART-MS for forensically relevant samples over the past five years. To provide more context, background on the technique, sampling approaches, and data analysis methods are presented along with a discussion on the potential future and research needs of the technology.

Effect of ontogeny on the content of the hallucinogenic alkaloids atropine and scopolamine in the different organs of some Solanaceae plants

The content of atropine and scopolamine is known to vary with ontogeny and plant organs selected which makes it necessary to define the optimal stage for harvesting of each plant organ. The present study aims at investigating the effect of ontogeny on the accumulation of atropine and scopolamine in the leaves, stems, roots, flowers and fruits of the Solanaceae plants Burgmansia suaveolens Bercht. & J.Presl, Datura stramonium L., D. arborea L., D. inoxia Mill. and Hyoscyamus albus L. Results showed that the highest content of atropine and scopolamine was observed during the flowering stage of most organs. H. albus L. leaves collected during flowering stage exhibited the highest content of atropine (746.66 ug/g) followed by the pre-flowering leaves of D. stramonium L. and the flowering stage stems of H. albus L. while D. inoxia Mill. pre-flowering leaves and flowering stems had significantly higher content of scopolamine among all the tested extracts with a concentration of 555.04 ug/g and 244.26 ug/g, respectively.

Identification of the Species Constituents of Maggot Populations Feeding on Decomposing Remains-Facilitation of the Determination of Post Mortem Interval and Time Since Tissue Infestation through Application of Machine Learning and Direct Analysis in Real Time-Mass Spectrometry

The utilization of entomological specimens such as larvae (maggots) for the estimation of time since oviposition (i.e., egg laying) for post mortem interval determination, or for estimation of time since tissue infestation (in investigations of elder or child care neglect and animal abuse cases), requires accurate determination of insect species identity. Because the larvae of multiple species are visually highly similar and difficult to distinguish, it is customary for species determination of maggots to be made by rearing them to maturity so that the gross morphological features of the adult can be used to accurately identify the species. This is a time-consuming and resource-intensive process which also requires that the sample be viable. The situation is further complicated when the maggot mass being sampled is comprised of multiple species. Therefore, a method for accurate species identification, particularly for mixtures, is needed. It is demonstrated here that direct analysis in real time-high resolution mass spectrometric (DART-HRMS) analysis of ethanol suspensions containing combinations of maggots representing Calliphora vicina, Chrysomya rufifacies, Lucilia coeruleiviridis, L. sericata, Phormia regina, and Phoridae exhibit highly reproducible chemical signatures. An aggregated hierarchical conformal predictor applied to a hierarchical classification tree that was trained against the DART-HRMS data enabled, for the first time, multispecies identification of maggots in mixtures of two, three, four, five, and six species. The conformal predictor provided label specific regions with confidence limits between 80 and 99% for species identification. The study demonstrates a novel, rapid, facile, and powerful approach for identification of maggot species in field-derived samples.