Optimization of the Data Treatment Steps of a Non-targeted LC-MS-Based Workflow for the Identification of Trace Chemical Residues in Honey

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome

Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational tools to detect and identify unknown or suspected chemicals in the exposome. Fully understanding the chemical exposome requires characterization of both environmental media and human specimens. As such, we conducted a review to examine the use of different NTA and SSA methods in various exposure media and human samples, including the results and chemicals detected. The literature review was conducted by searching literature databases, such as PubMed and Web of Science, for keywords, such as "non-targeted analysis", "suspect screening analysis" and the exposure media. Sources of human exposure to environmental chemicals discussed in this review include water, air, soil/sediment, dust, and food and consumer products. The use of NTA for exposure discovery in human biospecimen is also reviewed. The chemical space that has been captured using NTA varies by media analyzed and analytical platform. In each media the chemicals that were frequently detected using NTA were: per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in water, pesticides and polyaromatic hydrocarbons (PAHs) in soil and sediment, volatile and semi-volatile organic compounds in air, flame retardants in dust, plasticizers in consumer products, and plasticizers, pesticides, and halogenated compounds in human samples. Some studies reviewed herein used both liquid chromatography (LC) and gas chromatography (GC) HRMS to increase the detected chemical space (16%); however, the majority (51%) only used LC-HRMS and fewer used GC-HRMS (32%). Finally, we identify knowledge and technology gaps that must be overcome to fully assess potential chemical exposures using NTA. Understanding the chemical space is essential to identifying and prioritizing gaps in our understanding of exposure sources and prior exposures. IMPACT STATEMENT: This review examines the results and chemicals detected by analyzing exposure media and human samples using high-resolution mass spectrometry based non-targeted analysis (NTA) and suspect screening analysis (SSA).

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.

Systematic assessment of acquisition and data-processing parameters in the suspect screening of veterinary drugs in archive matrices using LC-HRMS

Monitoring strategies for veterinary drugs in products of animal origin are shifting towards a more risk-based approach. Such strategies not only target a limited number of predefined .substances but also facilitate detection of unexpected substances. By combining the use of archive matrices such as feather meal with suspect-screening methods, early detection of new hazards in the food and feed industry can be achieved. Effective application of such strategies is hampered by complex data interpretation and therefore, targeted data analysis is commonly applied. In this study, the performance of a suspect-screening data processing workflow using a suspect list or the online spectral database mzCloud^TM was explored to facilitate detection of veterinary drugs in archive matrices. Data evaluation parameters specifically investigated for application of a suspect list were mass tolerance and the addition or omission of retention times. Application of a mass tolerance of 1.5 ppm leads to an increase in the number of false positives, as does omission of retention times in the suspect list. Different acquisition modes yielding different qualities of MS2 data were studied and proved to be a critical factor, where data-dependent acquisition is preferred when matching to the mzCloud^TM database. Using this approach, it is possible to search for compounds on a dedicated suspect list based on the exact mass and retention times and, at the same time, detect unexpected compounds without a priori information. A pilot study was conducted and fourteen different antibiotics were detected (and confirmed by MS/MS). Three of these antibiotics were not included in the suspect list. The optimised suspect-screening method proved to be fit for the purpose of finding veterinary drugs in feather meal, which are not in the scope of the current monitoring methods and therefore, it gives added value in the perspective of a risk-based monitoring.

A data-independent acquisition approach based on HRMS to explore the biodegradation process of organic micropollutants involved in a biological ion-exchange drinking water filter

Drinking water producers continuously develop innovative treatment processes to effectively remove organic micropollutants from raw water. Biological ion-exchange (BIEX) water treatment is one of these new techniques under development and showing great potential. In order to investigate if biodegradation is highly involved in such a removal technique, cultures were prepared with microorganisms sampled on the resins of a BIEX filter. Then, organic micropollutants were spiked into these cultures and their (bio)degradation was followed over 30 days by ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS). The purpose of this study was firstly to develop an analytical method using UHPLC-HRMS able to monitor the degradation of three spiked organic micropollutants in culture. Beyond quantification, this method allowed the simultaneous recording of fragmentation information via the use of a data-independent acquisition approach to perform a non-exhaustive search of transformation products related to the spiked micropollutants in culture aliquots. Secondly, a data treatment approach was developed to process raw spectral data generated by aliquots analysis by optimizing the precursor isolation mass windows, the accurate mass tolerance, peak intensity thresholds and choice of database. The use of this new method with a post-data acquisition treatment approach completed by the exhaustive study of fragmentation spectra allowed the tentative identification of 11 transformation products related to the spiked compounds. Finally, 16S rRNA gene amplicon sequencing revealed that bacterial genera known for their ability to degrade the spiked micropollutants were present in the microbial community of the BIEX drinking water filter.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

An overview of chemical contaminants and other undesirable chemicals in alcoholic beverages and strategies for analysis

The presence of chemical contaminant in alcoholic beverages is a widespread and notable problem with potential implications for human health. With the complexity and wide variation in the raw materials, production processes, and contact materials involved, there are a multitude of opportunities for a diverse host of undesirable compounds to make their way into the final product-some of which may currently remain unidentified and undetected. This review provides an overview of the notable contaminants (including pesticides, environmental contaminants, mycotoxins, process-induced contaminants, residues of food contact material [FCM], and illegal additives) that have been detected in alcoholic products thus far based on prior reviews and findings in the literature, and will additionally consider the potential sources for contamination, and finally discuss and identify gaps in current analytical strategies. The findings of this review highlight a need for further investigation into unwanted substances in alcoholic beverages, particularly concerning chemical migrants from FCMs, as well as a need for comprehensive nontargeted analytical techniques capable of determining unanticipated contaminants.

Non-targeted study of the thermal degradation of tylosin in honey, water and water:honey mixtures

Tylosin A is a macrolide antibiotic used in beekeeping. The aim of the study was to characterise the behaviour of tylosin A in honey after heating and during storage, and to identify its degradation products using a non-targeted approach. In addition, the possibility of a semi-quantification of tylosin B using tylosin A was assessed as a case study for the semi-quantification of degradation products using the parent compounds. The results showed significant degradation of tylosin A in aqueous solution (~96%) as well as in spiked and incurred honey dissolved in water (~50% and ~29%, respectively) after heating at 100°C for 90 min. However, at a lower heating temperature of 70°C, degradation was only observed in water (~31%). When stored at room temperature (27°C) for one year, tylosin A degraded significantly (~47%) in an incurred honey sample. Tylosin B, the only reported degradation product of tylosin A in honey so far, increased significantly in aqueous solution under all treatments, but it only increased in spiked water-honey mixture after heating at 100°C. Two new degradation products, namely 5-O-mycaminosyltylonolide (OMT) and lactenocin, were tentatively identified in water and spiked honey after heating at 100°C. The results of the present study reinforce the conclusion that relying only on the water model or spiked food matrix is not sufficient to understand the thermal degradation of antibiotics in food matrices. Finally, a semi-quantification of tylosin B with a relative error of 20% in an incurred honey sample was possible using the response factor of tylosin A, its parent compound. The results of this study prove that a semi-quantification using the parent compound to quantify its degradation compound can provide satisfactory results, but this will be analyte-dependent.