Mass Spectral Library Research Articles

AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence.

The unbounded permutations of biological molecules, including proteins and their constituent peptides, present a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides.

Discovery of N-Acyl Amino Acids and Novel Related N-, O-Acyl Lipids by Integrating Molecular Networking and an Extended In Silico Spectral Library.

Research on novel bioactive lipids has garnered increasing interest. Although lipids can be identified by searching mass spectral libraries, the discovery of novel lipids remains challenging as the query spectra of such lipids are not included in libraries. In this study, we propose a strategy to discover novel carboxylic acid-containing acyl lipids by integrating molecular networking with an extended in silico spectral library. Derivatization was performed to improve the response of this method. The tandem mass spectrometry spectra enriched by derivatization facilitated the formation of molecular networking and 244 nodes were annotated. We constructed consensus spectra for these annotations based on molecular networking and developed an extended in silico spectral library based on these consensus spectra. The spectral library included 6879 in silico molecules covering 12,179 spectra. Using this integration strategy, 653 acyl lipids were discovered. Among these, O-acyl lactic acids and N-lactoyl amino acid-conjugated lipids were annotated as novel acyl lipids. Compared with conventional methods, our proposed method allows for the discovery of novel acyl lipids, and extended in silico libraries significantly increase the size of the spectral library.

Identification and Field Bioassay of Rusicada privata (Lepidoptera: Erebidae) Sex Pheromone

Rusicada privata (Lepidoptera: Erebidae) is a major pest insect on Hibiscus syriacus L. (Malvaceae: Malvales), which is usually planted in urban landscapes. Insecticidal control of R. privata is not ideal for urban landscaping due to its harmful effects and risk to human health. Therefore, nonchemical and ecofriendly alternatives are needed. To identify the sex pheromone of R. privata, abdominal tip extracts of male and female R. privata were analyzed by gas chromatography-mass spectrometry. The abundance of 7-methylheptadecane (7Me-17Hy) in female R. privata abdominal tip extracts led us to hypothesize that it is the major sex pheromone. The compound was tentatively identified by a mass spectral library and confirmed by matching retention times and mass spectra of the female-produced compound with those of a synthetic standard. The compounds elicited electroantennographic (EAG) activity. In a field trapping experiment, only synthetic lures containing 7Me-17Hy attracted R. privata males. The EAG activity and field trapping results confirmed that 7Me-17Hy is the sex pheromone of female R. privata. The results will aid in developing sex pheromone-based R. privata control techniques, such as mating disruption.

NIST Mass Spectrometry Data Center standard reference libraries and software tools: Application to seized drug analysis.

The standard reference libraries and associated custom software provided by the National Institute of Standards and Technology's Mass Spectrometry Data Center (NIST MSDC) are described with a focus on assisting the seized drug analyst with the identification of fentanyl-related substances (FRS). These tools are particularly useful when encountering novel substances when no certified sample is available. The MSDC provides three standard reference mass spectral libraries, as well as six software packages for mass spectral analysis, reference library searching, data interpretation, and measurement uncertainty estimation. Each of these libraries and software packages are described with references to the original publications provided. Examples of fentanyl identification by gas chromatography-mass spectrometry (GC-MS) and by direct analysis in real-time (DART) mass spectrometry are given. A link to online tutorials is provided.

Nontargeted screening based on EI-MS spectra using statistical methods: An investigative study of synthetic indole/indazole cannabinoids.

Mass spectrometry has evolved into a highly powerful tool for qualitative and quantitative chemical analyses. However, the identification of trace amounts of previously unknown structures in complex chemical matrix environments remains challenging. The rapid emergence of new synthetic cannabinoid substances is a typical example of this. Existing laboratory techniques are mostly based on methods used for lists of known illegal compounds. This situation poses a challenge to traditional data analysis and the risk of missing the compounds. Therefore, we propose to develop and validate a statistical model to classify newly emerging synthetic cannabinoid substances into a structural class or subclass. We obtained 70 electrospray ionization spectra of indole/indazole synthetic cannabinoids from both the actual standard analysis and the SWGDRUG mass spectral library (version 3.10). Each sample consisted of 330 m/z variables and corresponding relative intensities. We first cleared the variables with a variance below 0.1. Principal component analysis (PCA) was performed on the variance-filtered data, and the two principal components were retained to generate new data for hierarchical clustering. After hierarchical clustering, we used the receiver operating characteristic method in this cluster. Seventy synthetic indole/indazole cannabinoids were classified into four clusters. The side chain of cluster 1 is mainly fluorobenzyl, cluster 2 is pentyl, cluster 3 includes compounds from several structures, and cluster 4 is mainly fluoropentyl. The most relevant characteristic ions are m/z 109, m/z 252, and m/z 253 for cluster 1; m/z 144 and m/z 214 for cluster 2; and m/z 232 and m/z 233 for cluster 4. This study provides a more objective and less time-consuming solution for characterizing synthetic cannabinoids. And this work validates the ability of PCA to extract characteristic fragment ions.

Regions of Interest Multivariate Curve Resolution Liquid Chromatography with Data-Independent Acquisition Tandem Mass Spectrometry

New data-independent acquisition (DIA) modes coupled to chromatographic separations are opening new perspectives in the processing of massive mass spectrometric (MS) data using chemometric methods. In this work, the application of the regions of interest multivariate curve resolution (ROIMCR) method is shown for the simultaneous analysis of MS1 and MS2 DIA raw data obtained by liquid chromatography coupled to quadrupole-time-of-flight MS analysis. The ROIMCR method proposed in this work relies on the intrinsic bilinear structure of the MS1 and MS2 experimental data which allows us for the fast direct resolution of the elution and spectral profiles of all sample constituents giving measurable MS signals, without needing any further data pretreatment such as peak matching, alignment, or modeling. Compound annotation and identification can be achieved directly by the comparison of the ROIMCR-resolved MS1 and MS2 spectra with those from standards or from mass spectral libraries. ROIMCR elution profiles of the resolved components can be used to build calibration curves for the prediction of their concentrations in complex unknown samples. The application of the proposed procedure is shown for the analysis of mixtures of per- and polyfluoroalkyl substances in standard mixtures, spiked hen eggs, and gull egg samples, where these compounds tend to accumulate.

Posttranslational-modifications of human-serum-albumin analysis by a top-down approach validated by a comprehensive bottom-up analysis

The posttranslational modifications (PTM) of human serum albumin (HSA) can result in the development of isoforms that have been identified as potential biomarkers for advanced hepatic diseases. However, previous approaches using top-down (TD) analysis to identify isoforms based on molecular weight may have resulted in misidentifications. The nature of the identified isoforms has never been confirmed in previous works. Here, we aimed to critically evaluate TD for the characterization and determination of HSA isoforms in patients and make an inventory of HSA-PTM.Serum samples from control subjects and patients with liver dysfunctions were analyzed using both top-down (TD) and bottom-up (BU) approaches. TD analysis involved using a LC-TOF-MS system to obtain a multicharged spectrum of HSA, which was deconvoluted to identify isoforms. Spectra were then used for relative quantitation analysis of albumin isoform abundances based on trapezoidal integration. For BU analysis, serums were reduced +/- alkylated, digested with trypsin and analyzed in the Q-TOF, data-dependent acquisition (DDA) mode to generate a SWATH-MS high-resolution mass spectral library of all HSA peptides. Tryptic digests of another set of serum samples were then analyzed using data-independent acquisition (DIA) mode to confirm the presence of HSA isoforms and their modification sites.TD detected 15 isoforms corresponding to various modifications, including glycation, cysteinylation, nitrosylation, and oxidation (di- and tri-). In BU, the spectral library containing 127 peptides allowed for the characterization of the important isoforms with their modified sites, including some modifications that were only characterized in BU (carbamylation, deamidation, and amino-acid substitution). The method used for determining isoforms offered acceptable reproducibility (intra-/inter-assay CVs < 15%) for all isoforms present at relative abundances higher than 2%.Overall, the study found that several isoforms could be missed or misidentified by TD. However, all HSA isoforms identified by TD and reported to be relevant in liver dysfunctions were confirmed by BU. This critical evaluation of TD approach helped design an adequate and reliable method for the characterization of HSA isoforms in patients and offers the possibility to estimate isoform abundances within 3 min. These findings have significant implications for the diagnosis and treatment of liver dysfunctions.

Instrument-independent chemometric models for rapid, calibration-free NPS isomer differentiation from mass spectral GC-MS data

Chemometric analysis of mass spectral data for the purpose of differentiating positional isomers of novel psychoactive substances has seen a substantial increase in popularity in recent years. However, the process of generating a large and robust dataset for chemometric isomer identification is time consuming and impractical for forensic laboratories. To begin to address this problem, three sets of ortho/meta/para positional ring isomers (fluoroamphetamine (FA), fluoromethamphetamine (FMA), and methylmethcathinone (MMC)) were analyzed using multiple GC-MS instruments at three distinct laboratories. A diverse assortment of instrument manufacturers, model types, and parameters was utilized in order to incorporate substantial instrumental variation. The dataset was randomly split into 70% training and 30% validation sets, stratified by instrument. Following an approach based on Design of Experiments, the validation set was used to optimize the preprocessing steps performed prior to Linear Discriminant Analysis. Using the optimized model, a minimum m/z fragment threshold was determined to allow analysts to assess whether an unknown spectrum is of sufficient abundance and quality to be compared to the model. To assess the robustness of the models, a test set was developed utilizing two instruments from a fourth laboratory that was not involved in the generation of the primary dataset in addition to spectra from widely used mass spectral libraries. Of the spectra that reached the threshold, the classification accuracy was 100% for all three isomer types. Only two of the test and validation spectra that did not reach the threshold were misclassified. The results indicate that forensic illicit drug experts world-wide can use these models for robust NPS isomer identification on the basis of preprocessed mass spectral data without the need for acquiring reference drug standards and creating instrument specific GC-MS reference datasets. The continued robustness of the models could be ensured through international collaboration to collect data that captures all potential GC-MS instrumental variation encountered in forensic illicit drug analysis laboratories. This would allow every forensic institute to confidently assign isomeric structures without the need for additional chemical analysis.

Automated identification of unknown doping agents in confiscation samples by flow-injection mass spectrometry and mass spectral library searches.

Rapid and accurate identification of unknown compounds within suspicious samples confiscated for sports doping control and law enforcement drug testing is critical, but such analyses are often conducted manually and can be time-consuming. Here, we report a methodology for automated identification of unknown substances in confiscation samples by rapid automatic flow-injection analysis on a liquid chromatography coupled to high-resolution mass spectrometry system and identifying unknown compounds with Compound Discoverer software. The developed methodology was validated by comparing the automated identification results with those obtained from manual syringe-infusion experiments and manual tandem mass spectral library searches. The automated methodology resulted in far higher throughput and remarkably shorter turnaround time for analysis when compared with manual procedures and, in most cases, yielded more compounds. As this is the first such report to the authors' knowledge, this methodology may potentially transform analysis of confiscated samples in sports doping control and law enforcement drug testing.

Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization-Mass Spectrometry.

Mass spectrometry is a vital tool in the analytical chemist's toolkit, commonly used to identify the presence of known compounds and elucidate unknown chemical structures. All of these applications rely on having previously measured spectra for known substances. Computational methods for predicting mass spectra from chemical structures can be used to augment existing spectral databases with predicted spectra from previously unmeasured molecules. In this paper, we present a method for prediction of electron ionization-mass spectra (EI-MS) of small molecules that combines physically plausible substructure enumeration and deep learning, which we term rapid approximate subset-based spectra prediction (RASSP). The first of our two models, FormulaNet, produces a probability distribution over chemical subformulae to achieve a state-of-the-art forward prediction accuracy of 92.9% weighted (Stein) dot product and database lookup recall (within top 10 ranked spectra) of 98.0% when evaluated against the NIST 2017 Mass Spectral Library. The second model, SubsetNet, produces a probability distribution over vertex subsets of the original molecule graph to achieve similar forward prediction accuracy and superior generalization in the high-resolution, low-data regime. Spectra predicted by our best model improve upon the previous state-of-the-art spectral database lookup error rate by a factor of 2.9×, reducing the lookup error (top 10) from 5.7 to 2.0%. Both models can train on and predict spectral data at arbitrary resolution. Source code and predicted EI-MS spectra for 73.2M small molecules from PubChem will be made freely accessible online.

Fentanyl analogs classification via Siamese network and mass spectral library searching

Fentanyl and its analogs, as emerging psychotropic drugs, have led to a sharp increasing fatality due to their abuse in recent years. It is difficult to identify their differences due to the diversified molecular structures and small sample characteristics. This paper proposed a novel deep classification model based on Siamese network and mass spectral library searching to classify fentanyl analogs accurately. After embedding the query mass spectrum and reference spectrum into a low-dimensional space, the best matched spectrum is obtained by calculating their similarity, so as to determine the category of the query analogue. Three experiments were performed to verify the classification performance of the proposed model on two open datasets of fentanyl analogs. Compared with two spectral library searching methods of simple match factor (sMF) and hybrid match factor (hMF), four machine learning methods of linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and Adaboost, and two deep learning methods of deep clustering and contrastive learning, the proposed model can achieve the highest classification accuracy of 96.13%, 95.83% and 94.17%, respectively.

Establishment of non-targeted screening database and confirmation method for 18 mycotoxins in grains using ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry

A mass spectral library of 18 mycotoxins was developed based on ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF/MS), which was used to establish a non-targeted screening method for mycotoxins in rice and wheat matrices. Eighteen mycotoxin standards were separated on an HSS T3 column, and data were collected for both positive and negative ionization under the MSE mode of the UPLC-Q-TOF/MS. Details including formulas, retention times, theoretical exact masses, measured exact masses of the adduct and fragment ions, and ion abundance ratios were recorded to establish the mass spectral library of the 18 mycotoxins in UNIFI Software. Analyte detection was based on a retention time deviation of 0.3 min, and the exact mass deviation of the adduct ions and fragment ions was set to 5×10-6. The screening detection limit (SDL) was used as the main threshold for verifying the screening method. In the validation process, 18 mycotoxins were classified into two types: with maximum levels (MLs) and without MLs. The results showed that the mycotoxins with MLs could be accurately screened at their limited level, and the mycotoxins without MLs had a range of SDL concentration from 2 to 800 μg/kg. The matrix effect results showed that 14 mycotoxins in rice and 11 in wheat had moderate matrix effects. Finally, 25 batches of rice and wheat were purified using QuEChERS and HLB columns after acetonitrile extraction and screening were performed by employing the established method. The results revealed that aflatoxin G1 (AFG1), aflatoxin G2 (AFG2), fumonisins B1 (FB1), and sterigmatocystin (ST) were detected in one batch of rice, FB1 and ST were detected in another batch of rice, FB1 and ochratoxin A (OTA) were detected in two batches of wheat, and no other mycotoxins were detected. This method is characterized by high throughput, simplicity, rapidity, accuracy, and can be applied to accurately screen mycotoxins with concentrations higher than the SDLs and qualitatively screen various mycotoxins in rice and wheat without standards.

Adding open spectral data to MassBank and PubChem using open source tools to support non-targeted exposomics of mixtures.

The term "exposome" is defined as a comprehensive study of life-course environmental exposures and the associated biological responses. Humans are exposed to many different chemicals, which can pose a major threat to the well-being of humanity. Targeted or non-targeted mass spectrometry techniques are widely used to identify and characterize various environmental stressors when linking exposures to human health. However, identification remains challenging due to the huge chemical space applicable to exposomics, combined with the lack of sufficient relevant entries in spectral libraries. Addressing these challenges requires cheminformatics tools and database resources to share curated open spectral data on chemicals to improve the identification of chemicals in exposomics studies. This article describes efforts to contribute spectra relevant for exposomics to the open mass spectral library MassBank (https://www.massbank.eu) using various open source software efforts, including the R packages RMassBank and Shinyscreen. The experimental spectra were obtained from ten mixtures containing toxicologically relevant chemicals from the US Environmental Protection Agency (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT). Following processing and curation, 5582 spectra from 783 of the 1268 ENTACT compounds were added to MassBank, and through this to other open spectral libraries (e.g., MoNA, GNPS) for community benefit. Additionally, an automated deposition and annotation workflow was developed with PubChem to enable the display of all MassBank mass spectra in PubChem, which is rerun with each MassBank release. The new spectral records have already been used in several studies to increase the confidence in identification in non-target small molecule identification workflows applied to environmental and exposomics research.

An analytical platform for the screening and identification of pyrrolizidine alkaloids in food matrices with high risk of contamination

An analytical platform for the detection of pyrrolizidine alkaloids (PAs) in honey, pollen, teas, herbal infusions, and dietary supplements is proposed; it includes a wide-scope suspect screening method, based on a diagnostic product ion filtering strategy for the characterization of PAs, and a target screening and identification method for the high-throughput detection of 118 PAs of a high-resolution mass spectral library. Salting-out assisted liquid–liquid extraction of aqueous extracts combined to ultra-high performance liquid chromatography–high-resolution tandem mass spectrometry was employed. The limit of identification (0.6–30 µg kg−1) of 28 standards were fit-for-purpose in PA-monitoring applications, with a false negative rate <1.3 % at 4 µg L–1. The wide-scope suspect screening method allowed the tentative identification of 88 compounds. The screening of 282 commercial samples revealed a broad contamination of the studied matrices, demonstrating the effectiveness of the platform in detecting and identifying both target and untarget PAs.

Characterizing the degradation of cannabidiol in an e-liquid formulation

The reported characteristics of cannabidiol (CBD) have encouraged significant growth in commercial CBD products. There is limited information on the stability of CBD and some researchers have noted significant reductions of CBD in products. In this study, the chemical profiles of plant-based and chemically synthesized CBD in a prototype e-liquid formulation were assessed during 4 weeks of storage under varying conditions. Samples were analysed on days 1, 8, 15, 22, and 29 by untargeted analysis using ultra-high performance liquid chromatography—trapped ion mobility–time-of-flight mass spectrometry (UHPLC-TIMS-TOF-MS). On day 1, analysis of plant-based and synthetic CBD formulations showed small differences in their composition, with plant-based CBD e-liquid containing trace levels of a higher number of phytocannabinoid-related impurities. Storage for 4 weeks under stress (40 °C, 75% relative humidity, dark) and ambient (25 °C, 60% relative humidity, daylight) conditions led to increases in the number and abundance of cannabinoid-related degradation products, including cannabielsoin (CBE) and CBD-hydroxyquinone (HU-331), which are products of the oxidation of CBD, and other unidentified cannabinoid-related compounds. The unidentified cannabinoid-related compounds were probed by accurate mass measurement and MS2 fragmentation but could not be matched using a mass spectral library derived from 39 commercially available cannabinoid reference standards. Based on elemental composition and MS2 fragmentation patterns, the unidentified cannabinoid-related compounds were classified as hydroxy-CBE, hydroxy-CBD, and dihydroxy-CBD. The analysis of e-liquid formulations protected from light and stored at 4 °C for 4 weeks indicated only very small increases in CBD oxidation products. The results indicate that CBD degrades in e-liquid solution at ambient temperature in dark and light to form potentially undesirable products, including cannabielsoin and cannabidiol hydroxyquinone.

Unsupervised Pharmaceutical Polymorph Identification and Multicomponent Particle Mapping of ToF-SIMS Data by Non-Negative Matrix Factorization.

Crystal polymorphism of pharmaceutical compounds directly impacts resulting physicochemical characteristics, a critical aspect in active pharmaceutical ingredient (API) production. Tools to characterize and chemically map these polymorphs at the single particle scale remain important to advancing directed manufacture of targeted polymorphs. Here, time-of-flight secondary ion mass spectrometry (ToF-SIMS) was employed for chemically imaging inkjet printed acetaminophen samples. ToF-SIMS generates large data sets of high spatial resolution images. Extracting relevant data and peaks of interest can be laborious for, and biased by, users. Advances in machine learning approaches have introduced many supervised and unsupervised methods for data analysis. In this study, we apply non-negative matrix factorization (NMF) for the unsupervised analysis of ToF-SIMS chemical image data. More specifically, an expanded variant of NMF, NMFk, was employed to determine the data set's latent dimensionality. NMFk combines the spectral unmixing of traditional NMF with k-means clustering of the resulting factors and an optimization of the reconstruction and clustering. The method was used to identify the number of polymorph phases-and their representative mass spectra-generated from inkjet printed acetaminophen samples. Amorphous, crystalline form I, and crystalline form II polymorphs were observed. The learned polymorph mass spectra were then used to map the learned polymorphs onto subsequent particle samples of acetaminophen. Finally, NMFk also enabled the decomposition of mixed particle samples (i.e., migraine medicine), learning the number of compounds and their composition. The extracted constituent phase mass spectra-representing single compounds-were searched against mass spectral libraries for identification.

Cross-Platform Identification and Classification of Fentanyl Analogs by Tandem Mass Spectrometry (MS/MS)

The abuse of fentanyl and its analogs has caused a large number of deaths worldwide. Due to the rapid development of new fentanyl analogs, the standard mass spectral library is generally incomplete. The spectra obtained by different mass spectrometers by electrospray ionization (ESI) cannot be used across platforms, resulting in the hysteretic detection of novel fentanyl analogs. This paper reports a machine learning classification model based upon a hybrid similarity search. For the purpose of the identification of fentanyl analogs with different mass spectrometers, a mass spectral library based on the conserved fragmentation behavior (CFB) of fentanyl analogs was established. The results show that the identification accuracy of fentanyl analogs by two mass spectrometers is 100%, and the accuracy of classification model is 97.85%. Furthermore, the model may be applied to linear ion trap mass spectrometry (LIT-MS) and quadrupole time-of-flight mass spectrometry (Q-TOF-MS) with classification accuracies of 100 and 98.17%, respectively. This study provides promising technical support for the real-time and cloud computing detection of unknown fentanyl analogs.

Illuminating the dark metabolome of Pseudo-nitzschia-microbiome associations.

The exchange of metabolites mediates algal and bacterial interactions that maintain ecosystem function. Yet, while thousands of metabolites are produced, only a few molecules have been identified in these associations. Using the ubiquitous microalgae Pseudo-nitzschia sp., as a model, we employed an untargeted metabolomics strategy to assign structural characteristics to the metabolites that distinguished specific diatom-microbiome associations. We cultured five species of Pseudo-nitzschia, including two species that produced the toxin domoic acid, and examined their microbiomes and metabolomes. A total of 4826 molecular features were detected by tandem mass spectrometry. Only 229 of these could be annotated using available mass spectral libraries, but by applying new in silico annotation tools, characterization was expanded to 2710 features. The metabolomes of the Pseudo-nitzschia-microbiome associations were distinct and distinguished by structurally diverse nitrogen compounds, ranging from simple amines and amides to cyclic compounds such as imidazoles, pyrrolidines and lactams. By illuminating the dark metabolomes, this study expands our capacity to discover new chemical targets that facilitate microbial partnerships and uncovers the chemical diversity that underpins algae-bacteria interactions.

Chemicals of concern in personal care products used by women of color in three communities of California.

Personal care products (PCPs) may contain chemicals associated with adverse health effects. Prior studies found differences in product use by race/ethnicity and suggest some women are disproportionately exposed to chemicals of concern (CoCs). We quantified chemicals linked to cancer, reproductive or developmental harm, or endocrine disruption in PCPs used by women of color. We documented PCPs in stores frequented by Black, Latina, and Vietnamese women in their communities in California and CoCs on ingredient labels of 546 unique hair, skin, makeup, nail, deodorant/perfume, and intimate care products. Community partners chose 31 products for a combined targeted and suspect screen (National Institute of Standards and Technology mass spectral library search) two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC-TOFMS) analysis to detect chemicals not on ingredient labels. We found that 65% of labels included CoCs, and 74% of labels had undisclosed ingredients listed as "fragrance." The most prevalent chemicals were parabens, cyclosiloxanes, and formaldehyde releasers. GCxGC-TOFMS found additional CoCs, including fragrances, solvents, preservatives, ultraviolet filters, and contaminants. These findings contribute to awareness of potentially hazardous chemicals in PCPs, can help estimate disparities in chemical exposure, and complement research on health inequities due to chemical exposures from various contributors. This study is one of the first detailed assessments of chemicals of concern found in various types of PCPs used by several racial/ethnic groups. We found that over half of the 546 products selected by community partners as marketed to and/or used by them contained ingredients linked to cancer, reproductive or developmental harm, or endocrine disruption. Laboratory analysis identified additional chemicals in a subset of products, including unlabeled fragrance chemicals and contaminants. Elucidating exposures to chemicals in PCPs is important for risk assessment and health inequity research.

Combining Feature-Based Molecular Networking and Contextual Mass Spectral Libraries to Decipher Nutrimetabolomics Profiles.

Untargeted metabolomics approaches deal with complex data hindering structural information for the comprehensive analysis of unknown metabolite features. We investigated the metabolite discovery capacity and the possible extension of the annotation coverage of the Feature-Based Molecular Networking (FBMN) approach by adding two novel nutritionally-relevant (contextual) mass spectral libraries to the existing public ones, as compared to widely-used open-source annotation protocols. Two contextual mass spectral libraries in positive and negative ionization mode of ~300 reference molecules relevant for plant-based nutrikinetic studies were created and made publicly available through the GNPS platform. The postprandial urinary metabolome analysis within the intervention of Vaccinium supplements was selected as a case study. Following the FBMN approach in combination with the added contextual mass spectral libraries, 67 berry-related and human endogenous metabolites were annotated, achieving a structural annotation coverage comparable to or higher than existing non-commercial annotation workflows. To further exploit the quantitative data obtained within the FBMN environment, the postprandial behavior of the annotated metabolites was analyzed with Pearson product-moment correlation. This simple chemometric tool linked several molecular families with phase II and phase I metabolism. The proposed approach is a powerful strategy to employ in longitudinal studies since it reduces the unknown chemical space by boosting the annotation power to characterize biochemically relevant metabolites in human biofluids.