Data processing strategies for non-targeted analysis of foods using liquid chromatography/high-resolution mass spectrometry

Suspect and nontarget screening of pesticides and their transformation products in agricultural products using liquid chromatography-high-resolution mass spectrometry

Intensive agricultural production involves the extensive use of chemicals, leading to the presence of pesticides and their transformation products (TPs) in agricultural products. Our study developed a high-coverage method to map the occurrence of pesticides and their transformation products in agricultural products using liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Initially, a suspect list of 1265 pesticides was compiled based on in-house standards and online databases to identify potential parent pesticides. Besides, the reported and predicted TPs, as well as the multi-class characteristic fragment ions (CFIs) of pesticides, were summarized. Subsequently, nontarget features were identified by matching with 10226 TPs and 39-classes of CFIs. Both known and unknown parent pesticides and their TPs can be identified via suspect and nontarget screening procedures. Ultimately, the proposed method was applied to strawberry samples to demonstrate its effectiveness. We identified 67 parent pesticides and 57 TPs in 107 samples, with the majority at low concentrations, and preliminary traceability suggesting they may migrate from soil. The findings suggest that our method can enable suspect and nontarget screening of pesticides and their TPs, and it is also applicable to other food matrices. This method may facilitate regulatory agencies in strengthening the supervision of unknown risk substances or TPs, thereby comprehensively safeguarding consumer health.

Fragmentation characteristics-based nontargeted screening method of exogenous chemical residues in animal-derived foods using reversed-phase and hydrophilic interaction liquid chromatography-high-resolution mass spectrometry

Fragmentation characteristics are crucial for nontargeted screening to discover and identify unknown exogenous chemical residues in animal-derived foods. In this study, first, fragmentation characteristics of 51 classes of exogenous chemical residues were summarized based on experimental mass spectra of standards in reversed-phase and hydrophilic interaction liquid chromatography-high-resolution mass spectrometry (MS) and mass spectra from the MassBank of North America (MoNA) library. According to the proportion of fragmentation characteristics to the total number of chemical residues in each class, four screening levels were defined to classify 51 classes of chemical residues. Then, a nontargeted screening method was developed based on the fragmentation characteristics. The evaluation results of 82 standards indicated that more than 90 % of the chemical residues with MS/MS spectra can be identified at concentrations of 100 and 500 μg/kg, and about 80 % can be identified at 10 μg/kg. Finally, the nontargeted screening method was applied to 16 meat samples and 21 egg samples as examples. As a result, eight chemical residues and transformation products (TPs) of 5 classes in the exemplary samples were found and identified, in which 3 TPs of azithromycin were identified by fragmentation characteristics-assisted structure interpretation. The results demonstrated the practicability of the nontargeted screening method for routine risk screening of food safety.

Metabolites rapid-annotation in mice by comprehensive method of virtual polygons and Kendric mass loss filtering: A case study of Dendrobium nobile Lindl

With significant advancements in high-resolution mass spectrometry, there has been a substantial increase in the amount of chemical component data acquired from natural products. Therefore, the rapid and efficient extraction of valuable mass spectral information from large volumes of high-resolution mass spectrometry data holds crucial significance. This study illustrates a targeted annotation of the metabolic products of alkaloid and sesquiterpene components from Dendrobium nobile (D. nobile) aqueous extract in mice serum through the integration of an in-houses database, R programming, a virtual metabolic product library, polygonal mass defect filtering, and Kendrick mass defect strategies. The research process involved initially establishing a library of alkaloids and sesquiterpenes components and simulating 71 potential metabolic reactions within the organism using R programming, thus creating a virtual metabolic product database. Subsequently, employing the virtual metabolic product library allowed for polygonal mass defect filtering, rapidly screening 1705 potential metabolites of alkaloids and 3044 potential metabolites of sesquiterpenes in the serum. Furthermore, based on the chemical composition database of D. nobile and online mass spectrometry databases, 95 compounds, including alkaloids, sesquiterpenes, and endogenous components, were characterized. Finally, utilizing Kendrick mass defect analysis in conjunction with known alkaloids and sesquiterpenes targeted screening of 209 demethylation, methylation, and oxidation products in phase I metabolism, and 146 glucuronidation and glutathione conjugation products in phase II metabolism. This study provides valuable insights for the rapid and accurate annotation of chemical components and their metabolites in vivo within natural products.

Pesticide residues in animal-derived food: Current state and perspectives

Pesticides are widely used in the cultivation and breeding of agricultural products all over the world. However, their direct use or indirect pollution in animal breeding may lead to residual accumulation, migration, and metabolism in animal-derived foods, posing potential health risks to humans through the food chain. Therefore, it is necessary to detect pesticide residues in animal-derived food using simple, reliable, and sensitive methods. This review summarizes sample extraction and clean-up methods, as well as the instrumental determination technologies such as chromatography and chromatography-mass spectrometry for residual analysis in animal-derived foods, including meat, eggs and milk. Additionally, we perspectives on the future of this field. This information aims to assist relevant researchers in this area, contribute to the development of ideas and novel technical methods for residual detection, metabolic research and risk assessment of pesticides in animal-derived food.

3D Printing Technology: Role in Safeguarding Food Security

The rising threats to food security include several factors, such as population growth, low agricultural investment, and poor distribution systems. Consequently, food insecurity results from a confluence of issues, including diseases, processing limitations, and distribution deficiencies. Food insecurity usually occurs in vulnerable areas where certain technologies and traditional food safety testing are not a viable solution for foodborne disease detection. In this regard, 3D printing technologies and 3D printed sensors open the platform to produce portable, accurate, and low-cost sensors that address the gaps and challenges in food security. In this paper, we discuss the perspective role of 3D printed sensors in food security in terms of food safety and food quality monitoring to provide reliable access to nutritious, affordable food. In each section, we highlight the advantages of 3D printing technology in terms of cost-effectiveness, accuracy, accessibility, and reproducibility compared to traditional manufacturing methodologies. Recent developments in robotic technologies for mechanization, such as food handling with soft grippers, are also discussed. Lastly, we delve into the applications of advanced 3D printing technologies in agricultural monitoring, particularly the future of plant wearables, environmental sensing, and overall plant health monitoring.

Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry

Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.

Screening and identification of unknown chemical contaminants in food based on liquid chromatography-high-resolution mass spectrometry and machine learning

Unknown or unexpected chemical contaminants and/or their transformation products in food that may be harmful to humans need to be discovered for comprehensive safety evaluation. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful tool for detecting chemical contaminants in food samples. However, identifying all of peaks in LC-HRMS is not possible, but if class information is known in advance, further identification will become easier. In this work, a novel MS2 spectra classification-driven screening strategy was constructed based on LC-HRMS and machine learning. First, the classification model was developed based on machine learning algorithm using class information and experimental MS2 data of chemical contaminants and other non-contaminants. By using the developed artificial neural network classification model, in total 32 classes of pesticides, veterinary drugs and mycotoxins were classified with good prediction accuracy and low false-positive rate. Based on the classification model, a screening procedure was developed in which the classes of unknown features in LC-HRMS were first predicted through the classification model, and then their structures were identified under the guidance of class information. Finally, the developed strategy was tentatively applied to the analysis of pork and aquatic products, and 8 chemical contaminants and 11 transformation products belonging to 8 classes were found. This strategy enables screening of unknown chemical contaminants and transformation products in complex food matrices.

Recent Advances in Non-Targeted Screening of Compounds in Plastic-Based/Paper-Based Food Contact Materials

Ensuring the safety of food contact materials has become a pressing concern in recent times. However, detecting hazardous compounds in such materials can be a complex task, and traditional screening methods may not be sufficient. Non-targeted screening technologies can provide comprehensive information on all detectable compounds, thereby supporting the identification, detection, and risk assessment of food contact materials. Nonetheless, the non-targeted screening of food contact materials remains a challenging issue. This paper presents a detailed review of non-targeted screening technologies relying on high-resolution mass spectrometry for plastic-based and paper-based food contact materials over the past five years. Methods of extracting, separating, concentrating, and enriching compounds, as well as migration experiments related to non-targeted screening, are examined in detail. Furthermore, instruments and devices of high-resolution mass spectrometry used in non-targeted screening technologies for food contact materials are discussed and summarized. The research findings aim to provide a theoretical basis and practical reference for the risk management of food contact materials and the development of relevant regulations and standards.

Role of extracted phytochemicals from Rosa sterilis S. D. Shi in DSS-induced colitis mice: potential amelioration of UC

Rosa sterilis S. D. Shi is a new variety of R. roxburghii Tratt and its fruits are rich in bioactive components, but its effects and mechanisms against intestinal inflammation are currently unknown. In this study, the main components of the ethanol extract of R. sterilis S. D. Shi fruits (RSE) were identified and its anti-inflammatory efficacy in DSS-induced mice was evaluated. A total of nine compounds were identified, including 1-O-E-cinnamoyl-(6-arabinosylglucose), ellagic acid-O-rhamnoside, (epi) catechin, niga-ichigoside F1, etc. The results demonstrated that RSE ameliorated DSS-induced inflammation in mouse colon tissues by increasing mucin expression, reducing the production of TNF-α, IL-1β, and IL-6, inhibiting the mRNA expression of COX-2 and iNOS, regulating the composition of gut microbiota through suppressing Escherichia-Shigella while increasing Akkermansia muciniphila, and promoting the production of SCFAs, especially acetic acid. Briefly, RSE showed outstanding potential for anti-inflammatory activity and is expected to be a promising dietary supplement for healthy individuals to prevent or relieve colitis and colitis-related diseases, which provided a new direction for functional food development.

Targeted Analysis of Veterinary Drugs in Food Samples by Developing a High-Resolution Tandem Mass Spectral Library

Veterinary drug residues present in foods can pose severe health threats to the population. The present study aims to develop a high-resolution mass spectral library of 158 veterinary drugs of 16 different classes for their rapid identification in food samples through liquid chromatography-high-resolution electrospray ionization-tandem mass spectrometry (LC-HR-ESI-MS/MS). Standard drugs were pooled according to their log P values and exact masses before analysis. Spectra were collected at system automated collision energy, i.e., of 25-60 eV and four predetermined collision energies (10, 20, 30, and 40 eV) for each compound using a schedule precursor list of [M + H]+, [M + Na]+, and [M + NH4]+ ions. The utility of the developed database was checked by analyzing food samples. A total of 17 veterinary drugs based on the reference standard retention times (RTs), HR-MS spectra, and MS/MS spectra were identified in the analyzed samples. Moreover, five veterinary drugs were selected for quantitative analysis, including doxycycline hyclate, lincomycin, sulfasalazine, moxifloxacin, and diphenoxylate, using liquid chromatography-ion trap mass-spectrometry (LC-IT-MS). Concentrations of the drug were obtained to vary from 0.0805 to 0.9731 mg/kg in food samples and were found to be exceeded in most of the cases as per the maximum residue levels described by Food and Agriculture Organization (FAO)/World Health Organization (WHO). The MS data were submitted to the MetaboLights online database (MTBLS2914). This study will help in the high-throughput screening of multiclass veterinary drugs in foodstuffs.

Efficient food safety analysis for vegetables by a heteropore covalent organic framework derived silicone tube with flow-through purification

A heteropore covalent organic framework incorporated silicone tube (S-tube@PDA@COF) was used as adsorbent to purify the matrices in vegetable extracts. The S-tube@PDA@COF was fabricated by a facile in-situ growth method and characterized by scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction and N₂ adsorption-desorption. The as-prepared composite exhibited high removal efficiency of phytochromes and recovery (81.13-116.62%) of 15 chemical hazards from 5 representative vegetable samples. This study opens a promising avenue toward the facile synthesis of covalent organic frameworks (COFs)-derived silicone tubes for streamline operation in food sample pretreatment.

Evaluating non-targeted analysis methods for chemical characterization of organic contaminants in different matrices to estimate children's exposure

BACKGROUND

Children are vulnerable to environmental exposure of contaminants due to their small size, lack of judgement skills, as well as their proximity to dust, soil, and other environmental sources. A better understanding about the types of contaminants that children are exposed to or how their bodies retain or process these compounds is needed.

OBJECTIVE

In this study, we have implemented and optimized a methodology based on non-targeted analysis (NTA) to characterize chemicals in dust, soil, urine, and in the diet (food and drinking water) of infant populations.

METHODS

To evaluate potential toxicological concerns associated with chemical exposure, families with children between 6 months and 6 years of age from underrepresented groups were recruited in the greater Miami area. Samples of soil, indoor dust, food, water, and urine were provided by the caregivers, prepared by different techniques (involving online SPE, ASE, USE, QuEChERs), and analyzed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). Data post-processing was performed using the small molecule structure identification software, Compound Discoverer (CD) 3.3, and identified features were plotted using Kendrick mass defect plot and Van Krevelen diagrams to show unique patterns in different samples and regions of anthropogenic compound classifications.

RESULTS

The performance of the NTA workflow was evaluated using quality control standards in terms of accuracy, precision, selectivity, and sensitivity, with an average of 98.2%, 20.3%, 98.4% and 71.1%, respectively. Sample preparation was successfully optimized for soil, dust, water, food, and urine. A total of 30, 78, 103, 20 and 265 annotated features were frequently identified (detection frequency >80%) in the food, dust, soil, water, and urine samples, respectively. Common features detected in each matrix were prioritized and classified, providing insight on children's exposure to organic contaminants of concern and their potential toxicities.

IMPACT STATEMENT

Current methods to assess the ingestion of chemicals by children have limitations and are generally restricted by specific classes of targeted organic contaminants of interest. This study offers an innovative approach using non-targeted analysis for the comprehensive screening of organic contaminants that children are exposed to through dust, soil, and diet (drinking water and food).

Total Oxidizable Precursor (TOP) Assay-Best Practices, Capabilities and Limitations for PFAS Site Investigation and Remediation

The comprehensive characterization of per- and polyfluoroalkyl substances (PFASs) is necessary for the effective assessment and management of risk at contaminated sites. While current analytical methods are capable of quantitatively measuring a number of specific PFASs, they do not provide a complete picture of the thousands of PFASs that are utilized in commercial products and potentially released into the environment. These unmeasured PFASs include many PFAS precursors, which may be converted into related PFAS chemicals through oxidation. The total oxidizable precursor (TOP) assay offers a means of bridging this gap by oxidizing unknown PFAS precursors and intermediates and converting them into stable PFASs with established analytical standards. The application of the TOP assay to samples from PFAS-contaminated sites has generated several new insights, but it has also presented various technical challenges for laboratories. Despite the increased number of literature studies that include the TOP assay, there is a critical and growing gap in the application of this method beyond researchers in academia. This article outlines the benefits and challenges of using the TOP assay with aqueous samples for site assessments and suggests ways to address some of its limitations.

Influence of data acquisition modes and data analysis approaches on non-targeted analysis of phthalate metabolites in human urine

Humans are often exposed to phthalates and their alternatives, on account of their widespread use in PVC as plasticizers, which are associated with harmful human effects. While targeted biomonitoring provides quantitative information for exposure assessment, only a small portion of phthalate metabolites has been targeted. This results in a knowledge gap in human exposure to other unknown phthalate compounds and their metabolites. Although the non-targeted analysis (NTA) approach is capable of screening a broad spectrum of chemicals, there is a lack of harmonized workflow in NTA to generate reproducible data within and between different laboratories. The objective of this study was to compare two different NTA data acquisition modes, the data-dependent (DDA) and independent (DIA) acquisition (DDA), as well as two data analysis approaches, based on diagnostic ions and Compound Discoverer software for the prioritization of candidate precursors and identification of unknown compounds in human urine. Liquid chromatography coupled to high-resolution mass spectrometry was used for sample analysis. The combination of three-diagnostic-ion extraction and DDA data acquisition was able to improve data filtering and data analysis for prioritizing phthalate metabolites. With DIA, 25 molecular features were identified in human urine, while 32 molecular features were identified in the same urine samples using DDA data. The number of molecular features identified with level 1 confidence was 11 and 9 using DIA and DDA data, respectively. The study demonstrated that besides sample preparation, the impact of data acquisition must be taken into account when developing a NTA method and a consistent protocol for evaluating such an impact is necessary.

Nontargeted screening of veterinary drugs and their metabolites in milk based on mass defect filtering using liquid chromatography-high-resolution mass spectrometry

The development of nontargeted screening strategy for veterinary drugs and their metabolites is very important for food safety. In this study, a nontargeted screening strategy was developed to find the potentially hazardous substances based on mass defect filtering (MDF) using liquid chromatography-high-resolution mass spectrometry. First, the drug metabolites of 112 veterinary drugs from seven classes of antimicrobials were predicted. Second, three MDF models were established, including the traditional rectangular MDF, the enhanced parallelogram MDF, and the polygonal MDF. Finally, the strategy was applied to nontargeted screening of veterinary drugs in 36 milk samples. The polygonal MDF model based on the distribution area of parent drugs and their metabolites showed a better filtering effect. After removing food components and performing MDF, about 10% of the substances remained, and four veterinary drugs and six drug metabolites were discovered and identified, showing the effectiveness of this strategy. The nontargeted screening strategy can rapidly remove interfering substances and find the suspected compounds. It can also be used for nontargeted screening of veterinary drugs and their metabolites in other food matrices.

Rapid profiling of potential antitumor polymethoxylated flavonoids in natural products by integrating cell biospecific extraction with neutral loss/diagnostic ion filtering-based high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry

INTRODUCTION

Citri Reticulatae Pericarpium Viride (CRPV, Qing Pi in Chinese) has been widely used in traditional Chinese medicine. Polymethoxylated flavonoids (PMFs), which are a special group of flavonoids with strong antitumor activity, are broadly distributed in citrus peels. However, systematic investigation of antitumor PMFs in CRPV has received little attention to date.

OBJECTIVES

An MCF-7 cell biospecific extraction method integrated with neutral loss/diagnostic ion filtering-based HPLC-QTOF-MS/MS strategy was developed for rapid and specific profiling of antitumor PMFs and systematic identification of PMFs in CRPV.

METHODOLOGY

By incubating MCF-7 cells with CRPV extract, potential antitumor PMFs specifically bound to cells and were isolated. Then, by systematic investigation of fragmentation pathways, neutral loss and diagnostic ion filtering strategies were proposed to comprehensively and accurately identify PMFs.

RESULTS

Sixteen antitumor PMFs were unambiguously or tentatively identified. Among them, minor compound 15 (5-hydroxy-6,7,8,3',4'-pentamethoxyflavone with a free hydroxyl group at C-5) exhibited excellent antitumor activity, with an IC₅₀ value of 2.81 ± 0.76 μg/mL, which is lower than that of 5-fluorouracil (IC₅₀ , 4.92 ± 0.83 μg/mL). Nobiletin (12) and tangeretin (16), two major PMFs, presented moderate antitumor activities with IC₅₀ values of 13.06 ± 1.85 and 17.07 ± 1.18 μg/mL, respectively, and their contents were sensitively and precisely determined.

CONCLUSIONS

To the best of our knowledge, this is the first report on the systematic investigation of antitumor PMFs in CRPV. The study will lay a foundation for the quality control and clinical application of CRPV.

Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods

Non-targeted analysis (NTA) using high-resolution mass spectrometry has enabled the detection and identification of unknown and unexpected compounds of interest in a wide range of sample matrices. Despite these benefits of NTA methods, standardized procedures do not yet exist for assessing performance, limiting stakeholders’ abilities to suitably interpret and utilize NTA results. Herein, we first summarize existing performance assessment metrics for targeted analyses to provide context and clarify terminology that may be shared between targeted and NTA methods (e.g., terms such as accuracy, precision, sensitivity, and selectivity). We then discuss promising approaches for assessing NTA method performance, listing strengths and key caveats for each approach, and highlighting areas in need of further development. To structure the discussion, we define three types of NTA study objectives: sample classification, chemical identification, and chemical quantitation. Qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. Quantitative study performance can be assessed using estimation procedures developed for targeted methods with consideration for additional sources of uncontrolled experimental error. This article is intended to stimulate discussion and further efforts to develop and improve procedures for assessing NTA method performance. Ultimately, improved performance assessments will enable accurate communication and effective utilization of NTA results by stakeholders.

Current Role of Mass Spectrometry in the Determination of Pesticide Residues in Food

The extensive use of pesticides represents a risk to human health. Consequently, legal frameworks have been established to ensure food safety, including control programs for pesticide residues. In this context, the performance of analytical methods acquires special relevance. Such methods are expected to be able to determine the largest number of compounds at trace concentration levels in complex food matrices, which represents a great analytical challenge. Technical advances in mass spectrometry (MS) have led to the development of more efficient analytical methods for the determination of pesticides. This review provides an overview of current analytical strategies applied in pesticide analysis, with a special focus on MS methods. Current targeted MS methods allow the simultaneous determination of hundreds of pesticides, whereas non-targeted MS methods are now applicable to the identification of pesticide metabolites and transformation products. New trends in pesticide analysis are also presented, including approaches for the simultaneous determination of pesticide residues and other food contaminants (i.e., mega-methods), or the recent application of techniques such as ion mobility–mass spectrometry (IM–MS) for this purpose.

Present-Day Practice of Non-Target Chemical Analysis

Abstract

We review the main techniques, procedures, and information products used in non-target analysis (NTA) to reveal the composition of substances. Sampling and sample preparation methods are preferable that ensure the extraction of analytes from test samples in a wide range of analyte properties with the most negligible loss. The necessary techniques of analysis are versions of chromatography–high-resolution tandem mass spectrometry (HRMS), yielding individual characteristics of analytes (mass spectra, retention properties) to accurately identify them. The prioritization of the analytical strategy discards unnecessary measurements and thereby increases the performance of the NTA. Chemical databases, collections of reference mass spectra and retention characteristics, algorithms, and software for processing HRMS data are indispensable in NTA.

Background ions into exclusion list: A new strategy to enhance the efficiency of DDA data collection for high-throughput screening of chemical contaminations in food

Foods contaminated with hazardous compounds, could pose potential risks for human health. To date, there is still a big challenge in accurate identification. In this study, a novel data-dependent acquisition (DDA) approach, based on a combination of inclusion list and exclusion list, was proposed to acquire more effective MS/MS spectra. This strategy was successfully applied in a large-scale screening survey to detect 50 mycotoxins in oats, 155 veterinary drugs in dairy milk, and 200 pesticides in tomatoes. Compared with traditional acquisition modes, this new strategy has higher detection rate, particularly at ultra-low concentration by eliminating background influence, thereby generating the MS/MS spectra for more potential hazardous materials instead of matrix interference. Additionally, the obtained MS/MS spectra are simpler and more likely to be traced back than DIA. Moreover, this new strategy would be more comprehensively applied in food safety monitoring with the improvement of HRMS and post-acquisition techniques.

Chemical Characterization and Non-targeted Analysis of Medical Device Extracts: A Review of Current Approaches, Gaps, and Emerging Practices

The developers of medical devices evaluate the biocompatibility of their device prior to FDA's review and subsequent introduction to the market. Chemical characterization, described in ISO 10993-18:2020, can generate information for toxicological risk assessment and is an alternative approach for addressing some biocompatibility end points (e.g., systemic toxicity, genotoxicity, carcinogenicity, reproductive/developmental toxicity) that can reduce the time and cost of testing and the need for animal testing. Additionally, chemical characterization can be used to determine whether modifications to the materials and manufacturing processes alter the chemistry of a patient-contacting device to an extent that could impact device safety. Extractables testing is one approach to chemical characterization that employs combinations of non-targeted analysis, non-targeted screening, and/or targeted analysis to establish the identities and quantities of the various chemical constituents that can be released from a device. Due to the difficulty in obtaining a priori information on all the constituents in finished devices, information generation strategies in the form of analytical chemistry testing are often used. Identified and quantified extractables are then assessed using toxicological risk assessment approaches to determine if reported quantities are sufficiently low to overcome the need for further chemical analysis, biological evaluation of select end points, or risk control. For extractables studies to be useful as a screening tool, comprehensive and reliable non-targeted methods are needed. Although non-targeted methods have been adopted by many laboratories, they are laboratory-specific and require expensive analytical instruments and advanced technical expertise to perform. In this Perspective, we describe the elements of extractables studies and provide an overview of the current practices, identified gaps, and emerging practices that may be adopted on a wider scale in the future. This Perspective is outlined according to the steps of an extractables study: information gathering, extraction, extract sample processing, system selection, qualification, quantification, and identification.

Evaluation of different extractions for the metabolite identification of malachite green in brook trout and shrimp

Applications of mass spectrometry-based metabolomics in food science have developed fast in the last decade. Sample preparation and data processing are critical in non-target/metabolomic workflows but there is currently no standardized protocol for the development of these methods. The impact of data processing parameters or the inclusion of a different matrix is not often taken into account during the selection of an extraction. Thus, this study aimed to investigate the impact of different extractions, e.g., QuEChERS, and data processing on the determination of malachite green metabolites in two different organisms, brook trout and shrimp. The results obtained confirm the need for a harmonized approach for the validation of non-target workflows, as depending on the comparison criteria, the matrix, the mode of ionization or data processing, a different extraction could be chosen. This study also identified for the first time des-methylated leucomalachite green as another metabolite in the two organisms.

Open-source feature detection for non-target LC-MS analytics

RATIONALE

Non-target screening techniques using high-resolution mass spectrometers become more and more important for environmental sciences. Highly reliable and sophisticated software solutions are required to deal with the large amount of data obtained from such analyses.

METHODS

Processing of high-resolution LC-HRMS data was performed upon conversion into an open, XML-based data format followed by an automated assignment of chromatographic peaks using the open-source programming language R. Raw data from three different LC-HRMS systems were processed as a proof of principle.

RESULTS

We present a simple and straightforward algorithm to extract chromatographic peaks from previously m/z-centroided data based on the open-source programming language R and C++. The working principle and processing parameters are explained in detail. A ready-to-use script is provided in the supporting information.

CONCLUSIONS

The developed algorithm enables a comprehensible automated peak picking of non-target LC-MS data. Application to three completely different HRMS raw data files showed reasonable False Positives and False Negatives detection and moderate calculation times.

Bioassay-Guided Interpretation of Antimicrobial Compounds in Kumu, a TCM Preparation From Picrasma quassioides' Stem via UHPLC-Orbitrap-Ion Trap Mass Spectrometry Combined With Fragmentation and Retention Time Calculation

The stem of Picrasma quassioides (PQ) was recorded as a prominent traditional Chinese medicine, Kumu, which was effective for microbial infection, inflammation, fever, and dysentery, etc. At present, Kumu is widely used in China to develop different medicines, even as injection (Kumu zhusheye), for combating infections. However, the chemical basis of its antimicrobial activity has still not been elucidated. To examine the active chemicals, its stem was extracted to perform bioassay-guided purification against Staphylococcus aureus and Escherichia coli. In this study, two types of columns (normal and reverse-phase) were used for speedy bioassay-guided isolation from Kumu, and the active peaks were collected and identified via an UHPLC-Orbitrap-Ion Trap Mass Spectrometer, combined with MS Fragmenter and ChromGenius. For identification, the COCONUT Database (largest database of natural products) and a manually built PQ database were used, in combination with prediction and calculation of mass fragmentation and retention time to better infer their structures, especially for isomers. Moreover, three standards were analyzed under different conditions for developing and validating the MS method. A total of 25 active compounds were identified, including 24 alkaloids and 1 triterpenoid against S. aureus, whereas only β-carboline-1-carboxylic acid and picrasidine S were active against E. coli. Here, the good antimicrobial activity of 18 chemicals was reported for the first time. Furthermore, the spectrum of three abundant β-carbolines was assessed via their IC₅₀ and MBC against various human pathogens. All of them exhibited strong antimicrobial activities with good potential to be developed as antibiotics. This study clearly showed the antimicrobial chemical basis of Kumu, and the results demonstrated that HRMS coupled with MS Fragmenter and ChromGenius was a powerful tool for compound analysis, which can be used for other complex samples. Beta-carbolines reported here are important lead compounds in antibiotic discovery.

Revealing Unknown Controlled Substances and New Psychoactive Substances Using High-Resolution LC-MS/MS Machine Learning Models and the Hybrid Similarity Search Algorithm

High-resolution LC-MS/MS tandem mass spectra-based machine learning models are constructed to address the analytical challenge of identifying unknown controlled substances and new psychoactive substances (NPS's). Using a training set comprised of 770 LC-MS/MS barcode spectra (with binary entries 0 or 1) obtained generally by high-resolution mass spectrometers, three classification machine learning models were generated and evaluated. The three models are artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (k-NN) models. In these models, controlled substances and NPS's were classified into 13 subgroups (benzylpiperazine, opiate, benzodiazepine, amphetamine, cocaine, methcathinone, classical cannabinoid, fentanyl, 2C series, indazole carbonyl compound, indole carbonyl compound, phencyclidine, and others). Using 193 LC-MS/MS barcode spectra as an external test set, accuracy of the ANN, SVM, and k-NN models were evaluated as 72.5%, 90.0%, and 94.3%, respectively. Also, the hybrid similarity search (HSS) algorithm was evaluated to examine whether this algorithm can successfully identify unknown controlled substances and NPS's whose data are unavailable in the database. When only 24 representative LC-MS/MS spectra of controlled substances and NPS's were selectively included in the database, it was found that HSS can successfully identify compounds with high reliability. The machine learning models and HSS algorithms are incorporated into our home-coded AI-SNPS (artificial intelligence screener for narcotic drugs and psychotropic substances) standalone software that is equipped with a graphic user interface. The use of this software allows unknown controlled substances and NPS's to be identified in a convenient manner.