Direct Analysis in Real Time-Mass Spectrometry and Kohonen Artificial Neural Networks for Species Identification of Larva, Pupa and Adult Life Stages of Carrion Insects

Discrimination of Diptera order insects based on their saturated cuticular hydrocarbon content using a new microextraction procedure and chromatographic analysis

The nature and proportions of hydrocarbons in the cuticle of insects are characteristic of the species and age. Chemical analysis of cuticular hydrocarbons allows species discrimination, which is of great interest in the forensic field, where insects play a crucial role in estimating the minimum post-mortem interval. The objective of this work was the differentiation of Diptera order insects through their saturated cuticular hydrocarbon compositions (SCHCs). For this, specimens fixed in 70 : 30 ethanol : water, as recommended by the European Association for Forensic Entomology, were submitted to solid-liquid extraction followed by dispersive liquid-liquid microextraction, providing preconcentration factors up to 76 for the SCHCs. The final organic extract was analysed by gas chromatography coupled with flame ionization detection (GC-FID), and GC coupled with mass spectrometry was applied to confirm the identity of the SCHCs. The analysed samples contained linear alkanes with the number of carbon atoms in the C9-C15 and C18-C36 ranges with concentrations between 0.1 and 125 ng g-1. Chrysomya albiceps (in its larval stage) showed the highest number of analytes detected, with 21 compounds, while Lucilia sericata and Calliphora vicina the lowest, with only 3 alkanes. Non-supervised principal component analysis and supervised orthogonal partial least squares discriminant analysis were performed and an optimal model to differentiate specimens according to their species was obtained. In addition, statistically significant differences were observed in the concentrations of certain SCHCs within the same species depending on the stage of development or the growth pattern of the insect.

Application of a Modified 3D-PCSI-MS Ion Source to On-Site, Trace Evidence Processing via Integrated Vacuum Collection

Trace evidence, including hair, fibers, soil/dust, and gunshot residue (GSR), can be recovered from a crime scene to help identify or associate a suspect with illegal activities via physical, chemical, and biological testing. Vacuum collection is one technique that is employed in recovering such trace evidence but is often done so in a targeted manner, leaving other complementary, chemical-specific information unexamined. Here, we describe a modified 3D-printed cone spray ionization (3D-PCSI) source with integrated vacuum collection for on-site, forensic evidence screening, allowing the processing of targeted physical traces and nontargeted chemical species alike. The reported form factor allows sample collection, onboard extraction, filtration, and spray-based ionization in a singular vessel with minimal handling of evidence by the operator. Utilizing authentic forensic evidence types and portable MS instrumentation, this new method was characterized through systematic studies that replicate CSI applications. Reliability in the form of false positive/negative response rates was determined from a modest, user-blinded data set, and other attributes, such as collection efficacy and detection limit, were examined.

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.

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.

Coral Genus Differentiation Based on Direct Analysis in Real Time-High Resolution Mass Spectrometry-Derived Chemical Fingerprints

Coral reefs are one of the most biologically diverse ecosystems, and the accurate identification of the species is essential for diversity assessment and conservation. Current genus determination approaches are time-consuming and resource-intensive and can be highly subjective. To explore the hypothesis that the small-molecule profiles of coral are genus-specific and can be used as a rapid tool to catalogue and distinguish between coral genera, the small-molecule chemical fingerprints of the species Acanthastrea echinata, Catalaphyllia jardinei, Duncanopsammia axifuga, Echinopora lamellosa, Euphyllia divisa, Euphyllia paraancora, Euphyllia paradivisa, Galaxea fascicularis, Herpolitha limax, Montipora confusa, Monitpora digitata, Montipora setosa, Pachyseris rugosa, Pavona cactus, Plerogyra sinuosa, Pocillopora acuta, Seriatopora hystrix, Sinularia dura, Turbinaria peltata, Turbinaria reniformis, Xenia elongata, and Xenia umbellata were generated using direct analysis in real time-high resolution mass spectrometry (DART-HRMS). It is demonstrated here that the mass spectrum-derived small-molecule profiles for coral of different genera are distinct. Multivariate statistical analysis processing of the DART-HRMS data enabled rapid genus-level differentiation based on the chemical composition of the coral. Coral samples were analyzed with no sample preparation required, making the approach rapid and efficient. The resulting spectra were subjected to kernel discriminant analysis (KDA), which furnished accurate genus differentiation of the coral. Leave-one-out cross-validation (LOOCV) was carried out to determine the classification accuracy of each model and confirm that this approach can be used for coral genus attribution with prediction accuracies ranging from 86.67 to 97.33%. The advantages and application of the statistical analysis to DART-HRMS-derived coral chemical signatures for genus-level differentiation are discussed.

Rapid and practical qualitative and quantitative evaluation of non-fumigated ginger and sulfur-fumigated ginger via Fourier-transform infrared spectroscopy and chemometric methods

A strategy was developed to distinguish and quantitate nonfumigated ginger (NS-ginger) and sulfur-fumigated ginger (S-ginger), based on Fourier transform near infrared spectroscopy (FT-NIR) and chemometrics. FT-NIR provided a reliable method to qualitatively assess ginger samples and batches of S-ginger (41) and NS-ginger (39) were discriminated using principal component analysis and orthogonal partial least squares discriminant analysis of FT-NIR data. To generate quantitative methods based on partial least squares (PLS) and counter propagation artificial neural network (CP-ANN) from the FT-NIR, major gingerols were quantified using high performance liquid chromatography (HPLC) and the data used as a reference. Finally, PLS and CP-ANN were deployed to predict concentrations of target compounds in S- and NS-ginger. The results indicated that FT-NIR can provide an alternative to HPLC for prediction of active components in ginger samples and was able to work directly on solid samples.

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.

The current role of mass spectrometry in forensics and future prospects

Mass spectrometry (MS) techniques are highly prevalent in crime laboratories, particularly those coupled to chromatographic separations like gas chromatography (GC) and liquid chromatography (LC). These methods are considered "gold standard" analytical techniques for forensic analysis and have been extensively validated for producing prosecutorial evidentiary data. However, factors such as growing evidence backlogs and problematic evidence types (e.g., novel psychoactive substance (NPS) classes) have exposed limitations of these stalwart techniques. This critical review serves to delineate the current role of MS methods across the broad sub-disciplines of forensic science, providing insight on how governmental steering committees guide their implementation. Novel, developing techniques that seek to broaden applicability and enhance performance will also be highlighted, from unique modifications to traditional hyphenated MS methods to the newer "ambient" MS techniques that show promise for forensic analysis, but need further validation before incorporation into routine forensic workflows. This review also expounds on how recent improvements to MS instrumental design, scan modes, and data processing could cause a paradigm shift in how the future forensic practitioner collects and processes target evidence.

Peptide fingerprinting of Hermetia illucens and Alphitobius diaperinus: Identification of insect species-specific marker peptides for authentication in food and feed

Insects have been proposed as new source of proteins to meet the growing demand connected to the increasing world population. In the EU the inclusion of insect proteins in feed and food is strictly regulated. Hence, analytical methods able to discriminate and identify different insect species in food and feed are a necessity. In this work, a peptidomic approach was applied to determine peptide biomarkers for two edible insect species: lesser mealworm and black soldier fly. Three species specific peptide biomarkers were identified using LC-MS/MS. The two insects were mixed with fish standard feed at different concentrations, to evaluate the feasibility of their use as markers in complex matrices. The detection of marker peptides was confirmed down to 1% insect amount. The data here reported constitutes the first proof of concept for the potential application of the peptide marker approach for the identification and quantification of insect ingredients.

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.

Natural Product Discovery by Direct Analysis in Real Time Mass Spectrometry

Direct analysis in real time mass spectrometry (DART MS) is one of the first ambient ionization methods to be introduced and commercialized. Analysis by DART MS requires minimal sample preparation, produces nearly instantaneous results, and provides detection over a broad range of compounds. These advantageous features are particularly well-suited for the inherent complexity of natural product analysis. This review highlights recent applications of DART MS for species identification by chemotaxonomy, chemical profiling, genetic screening, and chemical spatial analysis from plants, insects, microbes, and metabolites from living systems.

Random Forest Processing of Direct Analysis in Real-Time Mass Spectrometric Data Enables Species Identification of Psychoactive Plants from Their Headspace Chemical Signatures

The United Nations Office on Drugs and Crime has designated several "legal highs" as "plants of concern" because of the dangers associated with their increasing recreational abuse. Routine identification of these products is hampered by the difficulty in distinguishing them from innocuous plant materials such as foods, herbs, and spices. It is demonstrated here that several of these products have unique but consistent headspace chemical profiles and that multivariate statistical analysis processing of their chemical signatures can be used to accurately identify the species of plants from which the materials are derived. For this study, the headspace volatiles of several species were analyzed by direct analysis in real-time high-resolution mass spectrometry (DART-HRMS). These species include Althaea officinalis, Calea zacatechichi, Cannabis indica, Cannabis sativa, Echinopsis pachanoi, Lactuca virosa, Leonotis leonurus, Mimosa hositlis, Mitragyna speciosa, Ocimum basilicum, Origanum vulgare, Piper methysticum, Salvia divinorum, Turnera diffusa, and Voacanga africana. The results of the DART-HRMS analysis revealed intraspecies similarities and interspecies differences. Exploratory statistical analysis of the data using principal component analysis and global t-distributed stochastic neighbor embedding showed clustering of like species and separation of different species. This led to the use of supervised random forest (RF), which resulted in a model with 99% accuracy. A conformal predictor based on the RF classifier was created and proved to be valid for a significance level of 8% with an efficiency of 0.1, an observed fuzziness of 0, and an error rate of 0. The variables used for the statistical analysis processing were ranked in terms of the ability to enable clustering and discrimination between species using principal component analysis-variable importance of projection scores and RF variable importance indices. The variables that ranked the highest were then identified as m/z values consistent with molecules previously identified in plant material. This technique therefore shows proof-of-concept for the creation of a database for the detection and identification of plant-based legal highs through headspace analysis.

Intelligence Algorithms for Protein Classification by Mass Spectrometry

Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work.