Distinguishing Infested Flour from Uninfested Flour through Chemometric Processing of DART-HRMS Data─Revealing the Presence of Tribolium castaneum, the Red Flour Beetle.

Insect infestation of agricultural stored products is a significant challenge to food security across the globe. One common pest is Tribolium castaneum (red flour beetle). In a new approach to addressing the threat of these beetles, Direct Analysis in Real Time-High-Resolution Mass Spectrometry was used to examine infested and uninfested flour samples. These samples were then distinguished through statistical analysis techniques, including EDR-MCR, in order to highlight the important m/z values contributing to the differences in the flour profiles. A subset of these values responsible for the identification of infested flour (nominal m/z 135, 136, 137, 163, 211, 279, 280, 283, 295, 297, and 338) were further investigated, and compounds responsible for these masses included 2-(2-ethoxyethoxy)ethanol, 2-ethyl-1,4-benzoquinone, palmitic acid, linolenic acid and oleic acid. These results have the potential to lead to a rapid technique by which flour and other grains can be tested for insect infestation.

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.

Introducing "DoPP": A Graphical User-Friendly Application for the Rapid Species Identification of Psychoactive Plant Materials and Quantification of Psychoactive Small Molecules Using DART-MS Data.

The widespread abuse of "legal high" psychoactive plants continues to be of global concern because of their negative impacts on public health and safety. In forensic science, a major challenge in controlling these substances is the paucity of methods to rapidly identify them. We report the development of the Database of Psychoactive Plants (DoPP), a new user-friendly tool featuring an architecture for the identification of plant unknowns, and the necessary regression statistics for the development and validation of psychoactive compound quantification. The application relies on the knowledge that terrestrial plants exhibit species-specific chemical signatures that can be revealed by direct analysis in real time─high-resolution mass spectrometry (DART-HRMS). Subsequent automated machine learning processing of libraries of these spectra enables rapid discrimination and species identification. The chemical signature database includes 57 available plant species. The rapid acquisition of mass spectra and the ability to sample the materials in their native form enabled the generation of the vast amounts of spectral replicates required for database construction. For the identification of sample unknowns, a data analysis workflow was developed and implemented using the DoPP tool. It utilizes a hierarchical classification tree that integrates three machine learning methods, namely, random forest, k-nearest neighbors, and support vector machine, all of which were fused using posterior probabilities. The results show accuracies of 98 and 99% for 10-fold cross-validation and external validation, respectively, which make the classification model suitable for identity prediction of real samples.

Revealing the presence of tryptamine new psychoactive substances using fused "neutral loss" spectra derived from DART high-resolution mass spectra.

A data fusion approach for the rapid extraction of core scaffold information that can be used to facilitate structure determination for new psychoactive substance (NPS) tryptamines is described. The method involves the screening of DART-HRMS data of new tryptamines against a partial least squares-discriminant analysis (PLS-DA) model that predicts the core tryptamine structure class into which the compound can be grouped. The PLS-DA prediction model was created and trained using neutral loss spectra derived from collision-induced dissociation (CID) DART mass spectral analysis of 50 tryptamine structures acquired at 60 V and 90 V, in which the sample groups were revealed by hierarchical clustering analysis (HCA). HCA of the fused neutral loss data clustered the 50 tryptamines into 10 groups based on the identities of the neutral fragments lost during fragmentation. "Leave-one-structure-out" validation of the PLS-DA model gave 100% accuracy, precision, sensitivity, and specificity. For external validation, the ability of the model to classify four compounds that were unfamiliar to it was tested, and the model was found to correctly predict the skeletal framework in each case. The results show proof of concept for how this approach can aide in the identification of new emerging psychoactive compounds.

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.

Distinguishing tissue origin of bovine gelatin in processed products using LC/MS technique in combination with chemometrics tools.

Gelatin, as a by-product of the meat industry, is extracted from bone and skin of mainly bovine and porcine origins. It is used widely in the food, drug, and cosmetic industries. Authenticity testing methods can be used to confirm whether labelled ingredients are present in the product. Generally, studies on gelatin are concerned mainly with determining species, but detecting tissue origin is also important from religious, health, and commercial perspectives. In the present study, for the first time, liquid chromatography/mass spectrometry (HPLC/MS) was used to differentiate bovine bone gelatin from gelatin derived from bovine skin. Tryptic-digested gelatins were measured using HPLC/MS and, subsequently, two powerful chemometrics approaches (i.e., PCA and PLS-DA) were used to classify samples as either skin or bone gelatins. Origin of bovine gelatins in different test samples were predicted accurately using this method. The results showed both the stability and reliability of the proposed procedure.

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.

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.

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

Plants that produce atropine and scopolamine fall under several genera within the nightshade family. Both atropine and scopolamine are used clinically, but they are also important in a forensics context because they are abused recreationally for their psychoactive properties. The accurate species attribution of these plants, which are related taxonomically, and which all contain the same characteristic biomarkers, is a challenging problem in both forensics and horticulture, as the plants are not only mind-altering, but are also important in landscaping as ornamentals. Ambient ionization mass spectrometry in combination with a hierarchical classification workflow is shown to enable species identification of these plants. The hierarchical classification simplifies the classification problem to primarily consider the subset of models that account for the hierarchy taxonomy, instead of having it be based on discrimination between species using a single flat classification model. Accordingly, the seeds of 24 nightshade plant species spanning 5 genera (i.e. Atropa, Brugmansia, Datura, Hyocyamus and Mandragora), were analyzed by direct analysis in real time-high resolution mass spectrometry (DART-HRMS) with minimal sample preparation required. During the training phase using a top-down hierarchical classification algorithm, the best set of discriminating features were selected and evaluated with a partial least square-discriminant analysis (PLS-DA) classifier to discriminate and visualize the data. The method yields species identity through a class hierarchy, and reveals the most significant markers for differentiation. The overall accuracy of the approach for species identification was 95% and 96% using 100X bootstrapping validation and test samples respectively. The method can be extended for the rapid identification of an infinite number of plant species.

A chemometric strategy for forensic analysis of condom residues: Identification and marker profiling of condom brands from direct analysis in real time-high resolution mass spectrometric chemical signatures.

The rapid and accurate identification of condom-derived lubricant traces takes on heightened importance in sexual assault cases where the assailant has used a condom in order to avoid leaving behind incriminating DNA evidence. Previous reports have demonstrated that a variety of techniques can be used to confirm that a given residue is condom-derived, based on the detection of spermicides, slip agents and/or other common additives. However, limited success has been achieved in differentiating brands from among a broad range of condom types. In this study, the utility of direct analysis in real time-high resolution mass spectrometry (DART-HRMS) combined with chemometrics, for the rapid and accurate attribution of brands to condom residues of various types, was explored and developed. A database of condom residue spectra comprised of 110 different condom types representing 16 brands was generated, with the spectra serving as representative fingerprints for each brand. The spectral fingerprints were subjected to pre-processing prior to the application of Partial Least Squares-Discriminant Analysis (PLS-DA) which was used to generate a classifier that permitted identification of condom brands with an accuracy of 97.4%. An additional criterion was imposed on the PLS-DA to provide the confidence level and credibility of each prediction. The effect of time since deposition, the presence of contaminants and the influence of residue transfer on the prediction accuracy of the model were also assessed. The results from Sparse Discriminant Analysis (SDA) and PLS-DA were followed by application of the Student's t-test to determine m/z values representative of small-molecule markers that were most important for defining brand classes. The m/z values revealed by the two methods were found to be consistent in indicating which masses were representative of markers. The SDA method also provided low-dimensional views of the discriminative directions for classification of condom residues, thereby enabling easy visualization of the relationship between the indicated m/z values and brand discrimination. The results further revealed a subset of 14 m/z values that were observed in all 110 condoms representing the 16 brands, and some of these may serve as potential universal small-molecule condom markers. Overall, the results show that the DART-HRMS database of condom residue spectra can be used to identify residues based on differences in chemical components peculiar to each brand. The database can be readily expanded to include more condoms.

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.

Species determination of the various life stages of flies (Order: Diptera) is challenging, particularly for the immature forms, because analogous life stages of different species are difficult to differentiate based on morphological features alone. It is demonstrated here that direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) combined with supervised Kohonen Self-Organizing Maps (SOM) enables accomplishment of species-level identification of larva, pupa, and adult life stages of carrion flies. DART-HRMS data for each life stage were acquired from analysis of ethanol suspensions representing Calliphoridae, Phoridae, and Sarcophagidae families, without additional sample preparation. After preprocessing, the data were subjected to a combination of minimum Redundancy Maximal Relevance (mRMR) and Sparse Discriminant Analysis (SDA) methods to select the most significant variables for creating accurate SOM models. The resulting data were divided into training and validation sets and then analyzed by the SOM method to define the proper discrimination models. The 5-fold venetian blind cross-validation misclassification error was below 7% for all life stages, and the validation samples were correctly identified in all cases. The multiclass SOM model also revealed which chemical components were the most significant markers for each species, with several of these being amino acids. The results show that processing of DART-HRMS data using artificial neural networks (ANNs) based on the Kohonen SOM approach enables rapid discrimination and identification of fly species even for the immature life stages. The ANNs can be continuously expanded to include a larger number of species and can be used to screen DART-HRMS data from unknowns to rapidly determine species identity.

A review of recent methods for the determination of ranges of feasible solutions resulting from soft modelling analyses of multivariate data.

Soft modelling or multivariate curve resolution (MCR) are well-known methodologies for the analysis of multivariate data in many different application fields. Results obtained by soft modelling methods are very likely impaired by rotational and scaling ambiguities, i.e. a full range of feasible solutions can describe the data equally well while fulfilling the constraints of the system. These issues are severely limiting the applicability of these methods and therefore, they can be considered as the most challenging ones. The purpose of the current review is to describe and critically compare the available methods that attempt at determining the range of ambiguity for the case of 3-component systems. Theoretical and practical aspects are discussed, based on a collection of simulated examples containing noise-free and noisy data sets as well as an experimental example.

Fitting-free curve resolution of spectroscopic data: Chemometric and physical chemical viewpoints.

In this paper the authors have investigated spectroscopic data analysis according to a recent development, i.e. the Direct Inversion in the Spectral Subspace (DISS) procedure. DISS is a supervised curve resolution technique, consequently it can be used once the spectra of the potential pure components are known and the experimental spectrum of a chemical mixture is also presented; hence the task is to determine the composition of the unknown chemical mixture. In this paper, the original algorithm of DISS is re-examined and some further critical reasoning and essential developments are provided, including the detailed explanations of the constrained minimization task based on Lagrange multiplier regularization approach. The main conclusion is that the regularization used for DISS is needed because of the possible shifted spectra effect instead of collinearity; and this new property, i.e. treating the mild shifted spectra effect, of DISS can be considered as its main scientific advantage.

Definition and detection of data-based uniqueness in evaluating bilinear (two-way) chemical measurements.

Multivariate curve resolution methods, frequently used in analyzing bilinear data sets, result in ambiguous decomposition in general. Implementing the adequate constraints may lead to reduce the so-called rotational ambiguity drastically, and in the most favorable cases to the unique solution. However, in some special cases, non-negativity constraint as minimal information of the system is a sufficient condition to resolve profiles uniquely. Although, several studies on exploring the uniqueness of the bilinear non-negatively constrained multivariate curve resolution methods have been made in the literature, it has still remained a mysterious question. In 1995, Manne published his profile-based theorems giving the necessary and sufficient conditions of the unique resolution. In this study, a new term, i.e., data-based uniqueness is defined and investigated in details, and a general procedure is suggested for detection of uniquely recovered profile(s) on the basis of data set structure in the abstract space. Close inspection of Borgen plots of these data sets leads to realize the comprehensive information of local rank, and these argumentations furnish a basis for data-based uniqueness theorem. The reported phenomenon and its exploration is a new stage (it can be said fundament) in understanding and describing the bilinear (matrix-type) chemical data in general. Our proposed detection tool is restricted to three-component systems because of the visual limitations of the Borgen plot, but the theorem is universal for systems with more than three components. A recently published experimental four-component system is used for illustrating this theorem in the case of systems with more than three components.

Newer developments on self-modeling curve resolution implementing equality and unimodality constraints.

Analytical self-modeling curve resolution (SMCR) methods resolve data sets to a range of feasible solutions using only non-negative constraints. The Lawton-Sylvestre method was the first direct method to analyze a two-component system. It was generalized as a Borgen plot for determining the feasible regions in three-component systems. It seems that a geometrical view is required for considering curve resolution methods, because the complicated (only algebraic) conceptions caused a stop in the general study of Borgen's work for 20 years. Rajkó and István revised and elucidated the principles of existing theory in SMCR methods and subsequently introduced computational geometry tools for developing an algorithm to draw Borgen plots in three-component systems. These developments are theoretical inventions and the formulations are not always able to be given in close form or regularized formalism, especially for geometric descriptions, that is why several algorithms should have been developed and provided for even the theoretical deductions and determinations. In this study, analytical SMCR methods are revised and described using simple concepts. The details of a drawing algorithm for a developmental type of Borgen plot are given. Additionally, for the first time in the literature, equality and unimodality constraints are successfully implemented in the Lawton-Sylvestre method. To this end, a new state-of-the-art procedure is proposed to impose equality constraint in Borgen plots. Two- and three-component HPLC-DAD data set were simulated and analyzed by the new analytical curve resolution methods with and without additional constraints. Detailed descriptions and explanations are given based on the obtained abstract spaces.

Investigation of the equality constraint effect on the reduction of the rotational ambiguity in three-component system using a novel grid search method.

The obtained results by soft modeling multivariate curve resolution methods often are not unique and are questionable because of rotational ambiguity. It means a range of feasible solutions equally fit experimental data and fulfill the constraints. Regarding to chemometric literature, a survey of useful constraints for the reduction of the rotational ambiguity is a big challenge for chemometrician. It is worth to study the effects of applying constraints on the reduction of rotational ambiguity, since it can help us to choose the useful constraints in order to impose in multivariate curve resolution methods for analyzing data sets. In this work, we have investigated the effect of equality constraint on decreasing of the rotational ambiguity. For calculation of all feasible solutions corresponding with known spectrum, a novel systematic grid search method based on Species-based Particle Swarm Optimization is proposed in a three-component system.