Mass Spectral Library Research Articles

Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification.

Compound identification in small-molecule research, such as untargeted metabolomics or exposome research, relies on matching tandem mass spectrometry (MS/MS) spectra against experimental or in silico mass spectral libraries. Most software programs use dot product similarity scores. Here we introduce the concept of MS/MS spectral entropy to improve scoring results in MS/MS similarity searches via library matching. Entropy similarity outperformed 42 alternative similarity algorithms, including dot product similarity, when searching 434,287 spectra against the high-quality NIST20 library. Entropy similarity scores proved to be highly robust even when we added different levels of noise ions. When we applied entropy levels to 37,299 experimental spectra of natural products, false discovery rates of less than 10% were observed at entropy similarity score 0.75. Experimental human gut metabolome data were used to confirm that entropy similarity largely improved the accuracy of MS-based annotations in small-molecule research to false discovery rates below 10%, annotated new compounds and provided the basis to automatically flag poor-quality, noisy spectra.

Mass Spectrometry Adduct Calculator.

We describe the Mass Spectrometry Adduct Calculator (MSAC), an automated Python tool to calculate the adduct ion masses of a parent molecule. Here, adduct refers to a version of a parent molecule [M] that is charged due to addition or loss of atoms and electrons resulting in a charged ion, for example, [M + H]+. MSAC includes a database of 147 potential adducts and adduct/neutral loss combinations and their mass-to-charge ratios (m/z) as extracted from the NIST/EPA/NIH Mass Spectral Library (NIST17), Global Natural Products Social Molecular Networking Public Spectral Libraries (GNPS), and MassBank of North America (MoNA). The calculator relies on user-selected subsets of the combined database to calculate expected m/z for adducts of molecules supplied as formulas. This tool is intended to help researchers create identification libraries to collect evidence for the presence of molecules in mass spectrometry data. While the included adduct database focuses on adducts typically detected during liquid chromatography-mass spectrometry analyses, users may supply their own lists of adducts and charge states for calculating expected m/z. We also analyzed statistics on adducts from spectra contained in the three selected mass spectral libraries. MSAC is freely available at https://github.com/pnnl/MSAC.

Machine learning model for detecting fentanyl analogs from mass spectra

In recent years, fentanyl and its analogs have been increasingly abused, leading to tragic outcomes. One way to tackle this problem is to rapidly detect fentanyl analog compounds and prevent them from reaching people who could be harmed by them. However, the emergence of novel fentanyl analogs that evade detection by classic mass spectral library matching has exacerbated the problem. We propose supervised machine learning classification models as a complementary approach to library matching for detecting fentanyl analogs from mass spectra. To develop and apply such models, we extract two dozen peak-based and similarity-based input features from each spectrum of interest. Using techniques such as random forests, neural networks, and logistic regression, we identify patterns within these features’ values, resulting in strong detection performance. Within a cross-validation framework, we achieve 99% probability of detection and 1% probability of false alarm on a representative set of several thousand mass spectra. These results suggest that machine learning models may offer a robust complement to library matching. Practitioners from diverse fields, including border security, law enforcement, and military may benefit from this capability to detect drugs of abuse.

Untargeted analysis of contaminants in river water samples: Comparison between two different sorbents for solid-phase extraction followed by liquid chromatography-high-resolution mass spectrometry determination

In this work, an untargeted approach based on ultra-high performance liquid chromatography coupled to high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for the analysis of contaminants in surface water samples, possibly deriving from an ineffective removal by wastewater treatment plants or illicit release. First, solid-phase extraction (SPE) conditions for 30 target compounds were optimized for two different sorbents, namely the commercial Oasis HLB and graphitized carbon black (GCB), by Box-Behnken designs of experiments. Upon identification of optimal conditions, the two SPE materials yielded comparable extraction recoveries, ranging between 52 and 110% and 55–100% for Oasis HLB and GCB, respectively. Thereafter, river water samples collected in five locations near Rome (Italy) were extracted using both sorbents and then analyzed by UHPLC-HRMS/MS in both positive and negative electrospray (ESI) ion polarities. The retrieved raw data files were submitted to Compound Discoverer software and compound identification was tentatively assigned by matching their tandem mass spectra with the mzCloud mass spectral library and manual validation, resulting in a total of 241 identified compounds (211 and 40 in positive and negative ESI mode, respectively), mainly belonging to pharmaceuticals.

Structural characterization of Chemical Weapons Convention-related phosphonoselenoates by electron ionization and electrospray ionization mass spectrometry.

Organophosphorus compounds with phosphorus atom bonded to one methyl, ethyl or propyl (normal or iso) group are listed in Schedule 2 of the Chemical Weapons Convention (CWC). Selenophosphorus compounds, listed in Schedule 2.B.4, have very limited representation in mass spectral libraries and the open literature. Members of a new category of selenophosphorus compounds were prepared via microsynthetic protocols and their fragmentation pathways were investigated using electron ionization (EI) and positive electrospray ionization (ESI) mass spectrometry. The EI and ESI fragmentation pathways were suggested and supported by acquired fragment ions of deuterated analogs and density functional theory calculations. Mass spectrometric investigations showed some interesting fragmentation pathways, such as McLafferty-type, selenono-selenolo rearrangements, intramolecular electrophilic aromatic substitution reaction and α-cleavage. Efficient microsynthesis was conducted, and EI-MS spectra and ESI-MS/MS spectra of a series of selenophosphorus compounds were collected and studied with the purpose of identifying CWC-related chemicals during on-site inspection and/or off-site analysis.

A Fast and Automated Strategy for the Identification and Risk Assessment of Unknown Substances (IAS/NIAS) in Plastic Food Contact Materials by GC-Q-Orbitrap HRMS: Recycled LDPE as a Proof-of-Concept.

A fast and automated approach has been developed for the tentative identification and risk assessment of unknown substances in plastic food contact materials (FCM) by GC-Q-Orbitrap HRMS. The proposed approach combines GC-HRMS full scan data acquisition coupled to Compound Discoverer™ 3.2 software for automated data processing and compound identification. To perform the tentative identification of the detected features, a restrictive set of identification criteria was used, including matching with the NIST Mass Spectral Library, exact mass of annotated fragments, and retention index calculation. After the tentative identification, a risk assessment of the identified substances was performed by using the threshold of toxicological concern (TTC) approach. This strategy has been applied to recycled low-density polyethylene (LDPE), which could be used as FCM, as a proof-of-concept demonstration. In the analyzed sample, 374 features were detected, of which 83 were tentatively identified after examination of the identification criteria. Most of these were additives, such as plasticizers, used in a wide variety of plastic applications, oligomers of LDPE, and substances with chemical, industrial, or cosmetic applications. The risk assessment was performed and, according to the TTC approach, the obtained results showed that there was no risk associated with the release of the identified substances. However, complementary studies related to the toxicity of the unidentified substances and the potential mixture toxicity (cocktail effects) should be conducted in parallel using bioassays.

Automated high confidence compound identification of electron ionization mass spectra for nontargeted analysis

Nontargeted analysis based on mass spectrometry is a rising practice in environmental monitoring for identifying contaminants of emerging concern. Nontargeted analysis performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) generates large numbers of possible analytes. Moreover, the default spectral library similarity score-based search algorithm used by LECO® ChromaTOF® does not ensure that high similarity scores result in correct library matches. Therefore, an additional manual screening is necessary, but leads to human errors especially when dealing with large amounts of data. To improve the speed and accuracy of the chemical identification, we developed CINeMA.py (Classification Is Never Manual Again). This programming suite automates GC×GC/TOF-MS data interpretation by determining the confidence of a match between the observed analyte mass spectrum and the LECO® ChromaTOF® software generated library hit from the NIST Electron Ionization Mass Spectral (NIST EI-MS) library. Our script allows the user to evaluate the confidence of the match using an algorithmic method that mimics the manual curation process and two different machine learning approaches (neural networks and random forest). The script allows the user to adjust various parameters (e.g., similarity threshold) and study their effects on prediction accuracy. To test CINeMA.py, we used data from two different environmental contaminant studies: an EPA study on household dust and a study on stormwater runoff. Using a reference set based on the analysis performed by highly trained users of the ChromaTOF and GC×GC/TOF-MS systems, the random forest model had the highest prediction accuracies of 86% and 83% on the EPA and Stormwater data sets, respectively. The algorithmic approach had the second-best prediction accuracy (82% and 79%), while the neural network accuracy had the lowest (63% and 67%). All the approaches required less than 1 min to classify 986 observed analytes, whereas manual data analysis required hours or days to complete. Our methods were also able to detect high confidence matches missed during the manual review. Overall, CINeMA.py provides users with a powerful suite of tools that should significantly speed-up data analysis while reducing the possibilities of manual errors and discrepancies among users, and can be applicable to other GC/EI-MS instrument based nontargeted analysis.

Non-Targeted Analysis Using Gas Chromatography-Mass Spectrometry for Evaluation of Chemical Composition of E-Vapor Products.

The Premarket Tobacco Product Applications (PMTA) guidance issued by the Food and Drug Administration for electronic nicotine delivery systems (ENDSs) recommends that in addition to reporting harmful and potentially harmful constituents (HPHCs), manufacturers should evaluate these products for other chemicals that could form during use and over time. Although e-vapor product aerosols are considerably less complex than mainstream smoke from cigarettes and heated tobacco product (HTP) aerosols, there are challenges with performing a comprehensive chemical characterization. Some of these challenges include the complexity of the e-liquid chemical compositions, the variety of flavors used, and the aerosol collection efficiency of volatile and semi-volatile compounds generated from aerosols. In this study, a non-targeted analysis method was developed using gas chromatography-mass spectrometry (GC-MS) that allows evaluation of volatile and semi-volatile compounds in e-liquids and aerosols of e-vapor products. The method employed an automated data analysis workflow using Agilent MassHunter Unknowns Analysis software for mass spectral deconvolution, peak detection, and library searching and reporting. The automated process ensured data integrity and consistency of compound identification with >99% of known compounds being identified using an in-house custom mass spectral library. The custom library was created to aid in compound identifications and includes over 1,100 unique mass spectral entries, of which 600 have been confirmed from reference standard comparisons. The method validation included accuracy, precision, repeatability, limit of detection (LOD), and selectivity. The validation also demonstrated that this semi-quantitative method provides estimated concentrations with an accuracy ranging between 0.5- and 2.0-fold as compared to the actual values. The LOD threshold of 0.7 ppm was established based on instrument sensitivity and accuracy of the compounds identified. To demonstrate the application of this method, we share results from the comprehensive chemical profile of e-liquids and aerosols collected from a marketed e-vapor product. Applying the data processing workflow developed here, 46 compounds were detected in the e-liquid formulation and 55 compounds in the aerosol sample. More than 50% of compounds reported have been confirmed with reference standards. The profiling approach described in this publication is applicable to evaluating volatile and semi-volatile compounds in e-vapor products.

New psychoactive and classic substances in pooled urine samples collected at the Ultra Europe festival in Split, Croatia.

We believe that analysing pooled urine samples for recreational drugs used at mass events can provide useful information about trends in drug use. An opportunity arose with the Ultra Europe music festival, which is attended by more than 150,000 people from over 150 countries every year. We analysed 30 pooled urine samples collected from portable chemical toilets located at or close to the Ultra Europe music festival venue in Split, Croatia in 2016–2018 to detect the presence of classic and new psychoactive substances (NPS). Four urine samples collected in 2016 were from a toilet without added chemicals (otherwise used to kill the smell) while the remaining samples were collected from toilets with added chemicals. Samples were qualitatively analysed with gas chromatography-mass spectrometry (GC/MS) using the full-scan mode. Data were compared with the Wiley mass spectral library of designer drugs and our in-house library containing about 1000 compounds and metabolites. We identified forty-six different substances and metabolites, 26 of which were classic substances/metabolites, mostly from the stimulants group, while 20 were NPS. In the NPS group, most of them were phenethylamines and cathinones. The variety of substances was the highest on the first day of the festival regardless of the year, but 2018 showed a significant drop compared to the previous two years. The results of our study revealed a stable trend of classic drug consumption, while NPS trend changed from one year to another.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective.

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Tackling unknown disinfection by-products: Lessons learned

Disinfection by-products (DBPs) are important environmental contaminants that have documented human health issues from many epidemiologic studies. Unlike classical contaminants, they are not manufactured, but form during drinking water treatment. As a result, they can be difficult to identify because most are not present in the mass spectral library databases. This perspective article presents lessons learned over the years for tackling unknown DBPs, many of which can also be applied to the identification of other unknown environmental contaminants. These lessons include: the importance of high resolution mass spectrometry, confirming tentative identifications with authentic chemical standards, considering possible isomers with unknown identification, and using multiple analytical techniques to enable a more complete picture of unknowns; understanding that features are not chemical structures; recognizing when the structure you identified in a solvent extract may not be the original form of the chemical in water; how some of our best discoveries are by accident; and finally, the “so what” question—is the compound you identified a concern?

Rapid and improved identification of drinking water bacteria using the Drinking Water Library, a dedicated MALDI-TOF MS database

According to the European Directives (UE) 2020/2184 and 2009/54/EC, which establishes the sanitary criteria for water intended for human consumption in Europe, water suitable for human consumption must be free of the bacterial indicators Escherichia coli, Clostridium perfringens and Enterococcus spp. Drinking water is also monitored for heterotrophic bacteria, which are not a human health risk, but can serve as an index of bacteriological water quality. Therefore, a rapid, accurate, and cost-effective method for the identification of these colonies would improve our understanding of the culturable bacteria of drinking water and facilitate the task of water management by treatment facilities. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is potentially such a method, although most of the currently available mass spectral libraries have been developed in a clinical setting and have limited environmental applicability. In this work, a MALDI-TOF MS drinking water library (DWL) was defined and developed by targeting bacteria present in water intended for human consumption. This database, made up of 319 different bacterial strains, can contribute to the routine microbiological control of either treated drinking water or mineral bottled water carried out by water treatment and distribution operators, offering a faster identification rate compared to a clinical sample-based library. The DWL, made up of 96 bacterial genera, 44 of which are not represented in the MALDI-TOF MS bacterial Bruker Daltonics (BDAL) database, was found to significantly improve the identification of bacteria present in drinking water.

MS_Piano: A Software Tool for Annotating Peaks in CID Tandem Mass Spectra of Peptides and N-Glycopeptides.

Annotating product ion peaks in tandem mass spectra is essential for evaluating spectral quality and validating peptide identification. This task is more complex for glycopeptides and is crucial for the confident determination of glycosylation sites in glycoproteins. MS_Piano (Mass Spectrum Peptide Annotation) software was developed for reliable annotation of peaks in collision induced dissociation (CID) tandem mass spectra of peptides or N-glycopeptides for given peptide sequences, charge states, and optional modifications. The program annotates each peak in high or low resolution spectra with possible product ion(s) and the mass difference between the measured and theoretical m/z values. Spectral quality is measured by two major parameters: the ratio between the sum of unannotated vs all peak intensities in the top 20 peaks, and the intensity of the highest unannotated peak. The product ions of peptides, glycans, and glycopeptides in spectra are labeled in different class-type colors to facilitate interpretation. MS_Piano assists validating peptide and N-glycopeptide identification from database and library searches and provides quality control and optimizes search reliability in custom developed peptide mass spectral libraries. The software is freely available in .exe and .dll formats for the Windows operating system.

Suspect Screening of Fentanyl Analogs Using Matrix-Assisted Ionization and a Miniature Mass Spectrometer with a Custom Expandable Mass Spectral Library.

The reliable identification of fentanyl and its analogs is of great significance for public security. However, with the growing prevalence of fentanyl compounds, current analytical strategies cannot fully meet the need for fast and high-throughput detection. In this study, a simple, rapid, and on-site analytical protocol was developed based on a miniature mass spectrometer. A dramatically simplified workflow was implemented using matrix-assisted ionization, bypassing complex sample pretreatment and chromatographic separation. The tandem mass spectrometry (MS/MS) capability afforded by the miniature ion trap mass spectrometer facilitated the investigation of fragmentation patterns for 49 fentanyl analogs during collision-induced dissociation, revealing valuable information on marker fragment ions and characteristic neutral loss. Calculations on Laplacian bond order values further verified the mass spectrometric behavior. A computation-assisted expandable mass spectral library was constructed in-house for fentanyl compounds. Smart suspect screening was carried out based on the full-scan MS and MS/MS data. The present study demonstrates an appealing potential for forensic applications, enabling streamlined screening for the presence of illicit fentanyl compounds at the point of seizures of suspect samples.

Characterizing azobenzene disperse dyes in commercial mixtures and children's polyester clothing

Azobenzene disperse dyes are the fastest-growing class of dyestuffs, yet little is known about dye occurrences, sources, and transformations; azo dyes are also underrepresented in chemical standard catalogs, molecular databases, and mass spectral libraries. Many azo dyes are known to have sensitization, mutagenic, and carcinogenic properties. To fill these knowledge gaps, azo dyes were purified from dyestuffs by Soxhlet extraction and flash chromatography and characterized using ultra-high-performance liquid chromatography (UHPLC) coupled to a high resolution Orbitrap Fusion Lumos mass spectrometer operated in positive electrospray ionization mode, as well as by 1H and 13C NMR. Data were analyzed to identify likely chemical formulas and structures using a weight-of-evidence approach with multiple open-source, in silico computational mass spectrometry tools. Nineteen total azobenzene dyes were detected in dyestuffs via a non-targeted analysis approach; the azobenzene dyes Disperse Blue 79:1, Disperse Blue 183:1, Disperse Orange 44, Disperse Orange 73, Disperse Red 50, Disperse Red 73, and Disperse Red 354 were purified from raw dyestuffs. Samples of children's polyester clothing were then analyzed likewise. In clothing, 21 azobenzene disperse dyes were detected, 12 of which were confirmed and quantified via reference standards. Individual dyes in apparel were quantified at concentrations up to 9230 μg dye/g shirt, with geometric means ranging 7.91–300 μg dye/g shirt. Total dye load in apparel was quantified at up to 11,430 μg dye/g shirt. This research supported the development of reference standards and library mass spectra for azobenzene disperse dyes previously absent from standard and spectral libraries. By analyzing the scope and quantities of azo dyes in children's polyester apparel, this study will facilitate a more robust understanding of sources of these potentially allergenic and mutagenic compounds.

A series of esters of diastereomeric menthols: Comprehensive mass spectral libraries and gas chromatographic data

A comprehensive data collection of mass spectral and gas chromatographic data of a series of esters of diastereomeric menthols (menthol, isomenthol, neomenthol, and neoisomenthol), in total 150 chemical entities, obtained by GC–MS on commonly used capillary columns of different polarity (non-polar HP-5MS and polar HP-Innowax), was created. Also, MS libraries containing electron ionization MS recorded on single quadrupole as well as on quadrupole ion-trap mass detectors together with the RIs on non-polar and polar columns were compiled (available as supplementary materials). The results point out to frequent misidentification of neoisomenthyl acetate as isomenthyl acetate in the literature, and the means of how to resolve this issue was suggested. The outcomes of this study provide chemists and food technologists with a useful tool in the field of food analysis of compounds with important food aroma properties.

Advanced Analytical Approaches for the Analysis of Polyphenols in Plants Matrices—A Review

Phenolic compounds are plants’ bioactive metabolites that have been studied for their ability to confer extensive benefits to human health. As currently there is an increased interest in natural compounds identification and characterization, new analytical methods based on advanced technologies have been developed. This paper summarizes current advances in the state of the art for polyphenols identification and quantification. Analytical techniques ranging from high-pressure liquid chromatography to hyphenated spectrometric methods are discussed. The topic of high-resolution mass spectrometry, from targeted quantification to untargeted comprehensive chemical profiling, is particularly addressed. Structure elucidation is one of the important steps for natural products research. Mass spectral data handling approaches, including acquisition mode selection, accurate mass measurements, elemental composition, mass spectral library search algorithms and structure confirmation through mass fragmentation pathways, are discussed.

Future feed control – Tracing banned bovine material in insect meal

In the present study, we assessed if different legacy and novel molecular analyses approaches can detect and trace prohibited bovine material in insects reared to produce processed animal protein (PAP). Newly hatched black soldier fly (BSF) larvae were fed one of the four diets for seven days; a control feeding medium (Ctl), control feed spiked with bovine hemoglobin powder (BvHb) at 1% (wet weight, w/w) (BvHb 1%, w/w), 5% (BvHb 5%, w/w) and 10% (BvHb 10%, w/w). Another dietary group of BSF larvae, namely *BvHb 10%, was first grown on BvHb 10% (w/w), and after seven days separated from the residual material and placed in another container with control diet for seven additional days. Presence of ruminant material in insect feed and in BSF larvae was assessed in five different laboratories using (i) real time-PCR analysis, (ii) multi-target ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS), (iii) protein-centric immunoaffinity-LC-MS/MS, (iv) peptide-centric immunoaffinity-LC-MS/MS, (v) tandem mass spectral library matching (SLM), and (vi) compound specific amino acid analysis (CSIA). All methods investigated detected ruminant DNA or BvHb in specific insect feed media and in BSF larvae, respectively. However, each method assessed, displayed distinct shortcomings, which precluded detection of prohibited material versus non-prohibited ruminant material in some instances. Taken together, these findings indicate that detection of prohibited material in the insect-PAP feed chain requires a tiered combined use of complementary molecular analysis approaches. We therefore advocate the use of a combined multi-tier molecular analysis suite for the detection, differentiation and tracing of prohibited material in insect-PAP based feed chains and endorse ongoing efforts to extend the currently available battery of PAP detection approaches with MS based techniques and possibly δ13CAA fingerprinting.

Non-target analysis of organic waste amended agricultural soils: Characterization of added organic pollution

Amendment of soil with organic urban and animal wastes can keep arable soil fertile without the need for synthetic fertilizers. However, pollutants present in these types of waste might be carried into the soil with unintended consequences for the environment. We studied an experimental agricultural plot, which had been amended with either synthetic inorganic fertilizers, human urine, manure, or wastewater treatment sludge at very high rates. We applied chemical non-target analysis to characterize present organic micropollutants, intending to compare treatments and highlight suspects of environmental concern. Soil samples were prepared by pressurized liquid and purified with solid-phase extraction before analysis with nanoflow ultra-high performance liquid chromatography coupled to high-resolution Orbitrap tandem mass spectrometry. Automated elucidation with two mass spectral libraries, multiple large chemical databases and environmental NORMAN suspect lists was able to annotate (level 3 and level 2) ∼ 20% of the 2306 detected features. A following principal component- and differential-analysis could separate the soil treatment groups’ pollution profiles and highlight high relative abundance features. From cattle manure, natural compounds such as bile acids and steroids were found. Human urine led to pollution with common pharmaceuticals such as metoprolol and propranolol. The highest number was added by wastewater treatment sludge, with 25 significant contaminants, spanning blood pressure regulators, antidepressants, synthetic steroids and sleep medication. Furthermore, using Kendrick mass defect plots, a series of polypropylene glycols could be revealed in the soil. Non-target analysis appears to be a promising method to characterize organic pollutants in soils.

High resolution mass spectrometry workflow for the analysis of food contaminants: Application to plant toxins, mycotoxins and phytoestrogens in plant-based ingredients

ABSTRACT An analytical workflow including mass spectral library, generic sample preparation, chromatographic separation, and analysis by high-resolution mass spectrometry (HRMS) was developed to gain insight into the occurrence of plant toxins, mycotoxins and phytoestrogens in plant-based food. This workflow was applied to 156 compounds including 90 plant toxins (pyrrolizidine alkaloids, tropane alkaloids, glycoalkaloids, isoquinoline alkaloids and aristolochic acids), 54 mycotoxins (including ergot alkaloids and Alternaria toxins) and 12 phytoestrogens (including isoflavones, lignans and coumestan) in plant-based protein ingredients, cereal and pseudo-cereal products. A mass spectral library was built based on fragmentation spectra collected at 10 different collision energies in both positive and negative ionisation modes for each toxin. Emphasis was put on a generic QuEChERS-like sample preparation followed by ultra-high-pressure liquid chromatography using alkaline mobile phase allowing the separation of more than 50 toxic pyrrolizidine alkaloids. HRMS acquisition comprised a full-scan event for toxins detection followed by data-dependent MS2 for toxin identification against mass spectrum. Method performance was evaluated using fortified samples in terms of sensitivity, repeatability, reproducibility and recovery. All toxins were positively identified at levels ranging from 1 µg kg−1 to 100 µg kg−1. Quantitative results obtained by a standard addition approach met SANTE/12682/2019 criteria for 132 out of 156 toxins. Such a workflow using generic, sensitive and selective multi-residue method allows a better insight into the occurrence of regulated and non-regulated toxins in plant-based foods and to conduct safety evaluation and risk assessments when needed.