Non-targeted screening of pesticides for food analysis using liquid chromatography high-resolution mass spectrometry-a review

Approaches for GC-HRMS Screening of Organic Microcontaminants: GC-APCI-IMS-QTOF versus GC-EI-QOrbitrap

This study explores the capabilities of GC-APCI-IMS-QTOF MS and GC-EI-QOrbitrap MS in screening applications and different strategies for wide-scope screening of organic microcontaminants using target suspect and nontarget approaches. On one side, GC-APCI-IMS-QTOF MS excels at preserving molecular information and adds ion mobility separation, facilitating screening through the list of componentized features containing accurate mass, retention time, CCS, and fragmentation data. On the other side, the extensive and robust fragmentation of GC-EI-QOrbitrap MS allows the application of different strategies for target and nontarget approaches using the NIST library spectra. Our findings revealed that GC-EI-QOrbitrap MS is more sensitive in target approaches. Automated workflows for suspect screening in GC-APCI-IMS-QTOF MS minimize false annotations but face challenges with false negatives due to in-source fragmentation and limitations when using in silico fragmentation tools. Conversely, a nontarget approach in GC-EI-QOrbitrap MS can reliably identify unknowns but results in more false annotations in complex matrices. This work highlights the strengths and limitations of each system and guides for their optimal application for wide-scope screening in environmental and food safety applications.

Retention time-independent strategy for screening pesticide residues in herbs based on a fingerprint database and all ion fragmentation acquisition with LC-QTOF MS

A retention time (RT)-independent strategy for nontargeted screening of pesticide residues in herbs was exploited using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF MS). The core of this strategy is a fingerprint database coupled with a data-independent acquisition (DIA) scan mode of all ion fragmentation (AIF). In the fingerprint database, a total of 150 pesticides with quasimolecular ions and fragment ions at five-level collision energies were collected as qualified ions for screening. During the data acquisition, the AIF scan was performed via real unbiased full-spectrum MS/MS acquisition. Six herb matrices spiked with 30 banned pesticides were used to evaluate the applicability of the strategy in real samples. The use of the narrow ion mass extraction window (10 mDa) and the narrow RT window (0.1 min) enabled the effective extraction of spectra from noisy backgrounds and the discovery of suspected pesticides via similarity matching of filtered qualified ions. On average, more than 11/30 of pesticides at 1 ng mL-1 and more than 23/30 of pesticides at 10 ng mL-1 or lower could be screened out in each matrix using at least two qualified ions. In addition, the AIF mode exhibited superior anti-interference capability compared to data-dependent acquisition (DDA) and sequential window acquisition of all theoretical mass spectra (SWATH), as determined by comparing the limits of screening (LOSs) of 30 banned pesticides spiked into Isatidis Folium. Finally, the developed strategy was applied to screen pesticide residues in extracts of Ganoderma and Foeniculi Fructus. Phorate-sulfone and phorate-sulfoxide were found in Ganoderma, as well as terbufos-sulfone and terbufos-sulfoxide were found in Foeniculi Fructus. In conclusion, the developed RT-independent strategy based on a fingerprint database and AIF acquisition with LC-QTOF MS seems to be one of the most efficient tools for the analysis of nontargeted pesticide residues in complicated matrices.

Facile preparation of hollow covalent organic frameworks as superior and universal matrix clean-up micro-structures for high throughout determination of food hazards

The complicated food matrix seriously limits the one-time test for the potential food hazards in non-targeted analysis. Accordingly, developing advanced sample pretreatment strategy to reduce matrix effects is of great significance. Herein, newly-integrated hollow-structured covalent organic frameworks (HCOFs) with large internal adsorption capacity and target-matched pore size were synthesized via etching the core-shell structured COFs. The as-prepared HCOFs could be directly applied for matrix clean-up of vegetable samples, while further modification of polydopamine (PDA) network facilitated application for animal samples. Both HCOFs and HCOFs@PDA with the comparable sizes to the matrix interference gave excellent adsorption performance to targets, achieving satisfied recoveries (70%-120%) toward 90 pesticides and 44 veterinary drugs in one-test, respectively. This work showed the great potential of the facile-integrated HCOFs with high stability and customized size to remove interference matrix and offered a universal strategy to achieve simultaneous screening of hazards with considerable quantity in high-throughput non-targeted analysis.

Farmland's silent threat: Comprehensive multimedia assessment of micropollutants through non-targeted screening and targeted analysis in agricultural systems

Intricate agricultural ecosystems markedly influence the dynamics of organic micropollutants, posing substantial threats to aquatic organisms and human health. This study examined the occurrence and distribution of organic micropollutants across soils, ditch sediment, and water within highly intensified farming setups. Using a non-targeted screening method, we identified 405 micropollutants across 10 sampling sites, which mainly included pesticides, pharmaceuticals, industrial chemicals, and personal care products. This inventory comprised emerging contaminants, banned pesticides, and controlled pharmaceuticals that had eluded detection via conventional monitoring. Targeted analysis showed concentrations of 3.99-1021 ng/g in soils, 4.67-2488 ng/g in sediment, and 12.5-9373 ng/L in water, respectively, for Σ40pesticides, Σ8pharmaceuticals, and Σ3industrial chemicals, indicating notable spatial variability. Soil organic carbon content and wastewater discharge were likely responsible for their spatial distribution. Principal component analysis and correlation analysis revealed a potential transfer of micropollutants across the three media. Particularly, a heightened correlation was decerned between soil and sediment micropollutant levels, highlighting the role of sorption processes. Risk quotients surpassed the threshold of 1 for 13-23 micropollutants across the three media, indicating high environmental risks. This study highlights the importance of employing non-targeted and targeted screening in assessing and managing environmental risks associated with micropollutants.

Assessment of emerging and persistent contaminants in an anthropogenic-impacted watershed: Application using targeted, non-targeted, and in vitro bioassay techniques

Emerging and persistent contaminants (EPC) pose a significant challenge to water quality monitoring efforts. Effect-based monitoring (EBM) techniques provide an efficient and systematic approach in water quality monitoring, but they tend to be resource intensive. In this study, we investigated the EPC distribution for various land uses using target analysis (TA) and non-target screening (NTS) and in vitro bioassays, both individually and integrated, in the upper Ping River Catchment, northern Thailand. Our findings of NTS showed that urban areas were the most contaminated of all land use types, although agriculture sites had high unexpected pollution levels. We evaluated the reliability of NTS data by comparing it to TA and observed varying inconsistencies likely due to matrix interferences and isobaric compound interferences. Integrating NTS with in vitro bioassays for a thorough analysis posed challenges, primary due to a scarcity of concentration data for key compounds, and potentially additive or non-additive effects of mixture samples that could not be accounted for. While EBM approaches place emphasis on toxic sites, this study demonstrated the importance of considering non-bioactive sites that contain toxic compounds with antagonistic effects that may go undetected by traditional monitoring approaches. The present work emphasizes the importance of improving NTS workflows and ensuring high-quality EBM analyses in future water quality monitoring programs.

A chemometric-assisted method for automatic, rapid and non-targeted detection of multi-pesticides in plant-derived foods by gas chromatography-mass spectrometry

An automatic, rapid and non-targeted detection method for multi-pesticides in plant-derived foods was developed by gas chromatography-mass spectrometry and chemometrics. In this method, a novel algorithm named moving window iterative target transformation factor analysis was proposed. Although there are challenges of peak overlapping and background interference, the retention time and corrected mass spectra of unknown pesticides can be automatically obtained through iteration calculation in the 'moving window' with reference to the pesticide mass spectral library. One mixed pesticide standard and nine varieties of plant-derived foods were investigated with the proposed method. By contrast, a fast temperature programme was used to shorten detection time compared to the standard temperature programme. For the mixed standard, the mass spectra and retention times of all 39 pesticides were successfully obtained from the overlapping signal. Furthermore, all spiked pesticides were successfully detected in plant-derived foods within 10 min using a fast temperature programme.

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.

Non-targeted analysis based on quantitative prediction and toxicity assessment for emerging contaminants in tire particle leachates

Non-targeted analysis (NTA) has great potential to screen emerging contaminants in the environment, and some studies have conducted in-depth investigation on environmental samples. Here, we used a NTA workflow to identify emerging contaminants in used tire particle (TP) leachates, followed by quantitative prediction and toxicity assessment based on hazard scores. Tire particles were obtained from four different types of automobiles, representing the most common tires during daily transportation. With the instrumental analysis of TP leachates, a total of 244 positive and 104 negative molecular features were extracted from the mass data. After filtering by a specialized emerging contaminants list and matching by spectral databases, a total of 51 molecular features were tentatively identified as contaminants, including benzothiazole, hexaethylene glycol, 2-hydroxybenzaldehyde, etc. Given that these contaminants have different mass spectral responses in the mass spectrometry, models for predicting the response of contaminants were constructed based on machine learning algorithms, in this case random forest and artificial neural networks. After five-fold cross-validation, the random forest algorithm model had better prediction performance (MAECV = 0.12, Q2 = 0.90), and thus it was chosen to predict the contaminant concentrations. The prediction results showed that the contaminant at the highest concentration was benzothiazole, with 4,875 μg/L in the winter tire sample. In addition, the joint toxicity assessment of four types of tires was conducted in this study. According to different hazard levels, hazard scores increasing by a factor 10 were developed, and hazard scores of all the contaminants identified in each TP leachate were summed to obtain the total hazard score. All four tires were calculated to have relatively high risks, with winter tires having the highest total hazard score of 40,751. This study extended the application of NTA research and led to the direction of subsequent targeting studies on highly concentrated and toxic contaminants.

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.

Structural Elucidation of Agrochemical Metabolic Transformation Products Based on Infrared Ion Spectroscopy to Improve In Silico Toxicity Assessment

Toxicological assessments of newly developed agrochemical agents consider chemical modifications and their metabolic and biotransformation products. To carry out an in silico hazard assessment, understanding the type of chemical modification and its location on the original compound can greatly enhance the reliability of the evaluation. Here, we present and apply a method based on liquid chromatography-mass spectrometry (LC-MS) enhanced with infrared ion spectroscopy (IRIS) to better delineate the molecular structures of transformation products before in silico toxicology evaluation. IRIS facilitates the recording of IR spectra directly in the mass spectrometer for features selected by retention time and mass-to-charge ratio. By utilizing quantum-chemically predicted IR spectra for candidate molecular structures, one can either derive the actual structure or significantly reduce the number of (isomeric) candidate structures. This approach can assist in making informed decisions. We apply this method to a plant growth stimulant, digeraniol sinapoyl malate (DGSM), that is currently under development. Incubation of the compound in Caco-2 and HepaRG cell lines in multiwell plates and analysis by LC-MS reveals oxidation, glucuronidation, and sulfonation metabolic products, whose structures were elucidated by IRIS and used as input for an in silico toxicology assessment. The toxicity of isomeric metabolites predicted by in silico tools was also assessed, which revealed that assigning the right metabolite structure is an important step in the overall toxicity assessment of the agrochemical. We believe this identification approach can be advantageous when specific isomers are significantly more hazardous than others and can help better understand metabolic pathways.

Metabolites in the regulatory risk assessment of pesticides in the EU

A large majority of chemicals is converted into metabolites through xenobiotic-metabolising enzymes. Metabolites may present a spectrum of characteristics varying from similar to vastly different compared with the parent compound in terms of both toxicokinetics and toxicodynamics. In the pesticide arena, the role of metabolism and metabolites is increasingly recognised as a significant factor particularly for the design and interpretation of mammalian toxicological studies and in the toxicity assessment of pesticide/metabolite-associated issues for hazard characterization and risk assessment purposes, including the role of metabolites as parts in various residues in ecotoxicological adversities. This is of particular relevance to pesticide metabolites that are unique to humans in comparison with metabolites found in in vitro or in vivo animal studies, but also to disproportionate metabolites (quantitative differences) between humans and mammalian species. Presence of unique or disproportionate metabolites may underlie potential toxicological concerns. This review aims to present the current state-of-the-art of comparative metabolism and metabolites in pesticide research for hazard and risk assessment, including One Health perspectives, and future research needs based on the experiences gained at the European Food Safety Authority.

Analysis of Pesticide Residues on Fruit Using Swab Spray Ionization Mass Spectrometry

The vast quantity and high variety of pesticides globally used in agriculture entails considerable risks for the environment and requires ensuring the safety of food products. Therefore, powerful analytical tools are needed to acquire qualitative and quantitative data for monitoring pesticide residues. The development of ambient ionization mass spectrometry methods in the past two decades has demonstrated numerous ways to generate ions under atmospheric conditions and simultaneously to reduce the need for extended sample preparation and circumvent chromatographic separation prior to mass analysis. Swab spray ionization enables the generation of ions directly from swabs via the application of high voltage and solvent flow. In this study, swab sampling of fruit surfaces and subsequent ionization directly from the swab in a modified electrospray ion source was employed for the screening and quantitation of pesticide residues. Aspects regarding sample collection, sampling efficacy on different surfaces, and swab background are discussed. The effect of solvent composition on pesticide-sodium adduct formation and the suppression of ionization by the background matrix have been investigated. Furthermore, a novel approach for the quantitation of pesticide residues based on depletion curve areas is presented. It is demonstrated that swab spray ionization is an effective and quick method for spectral library-based identification and the quantitative analysis of polar contact pesticide residues on food.

Structural Elucidation of Agrochemicals and Related Derivatives Using Infrared Ion Spectroscopy

Agrochemicals frequently undergo various chemical and metabolic transformation reactions in the environment that often result in a wide range of derivates that must be comprehensively characterized to understand their toxicity profiles and their persistence and outcome in the environment. In the development phase, this typically involves a major effort in qualitatively identifying the correct chemical isomer(s) of these derivatives from the many isomers that could potentially be formed. Liquid chromatography-mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy are often used in attempts to characterize such environment transformation products. However, challenges in confidently correlating chemical structures to detected compounds in mass spectrometry data and sensitivity/selectivity limitations of NMR frequently lead to bottlenecks in identification. In this study, we use an alternative approach, infrared ion spectroscopy, to demonstrate the identification of hydroxylated derivatives of two plant protection compounds (azoxystrobin and benzovindiflupyr) contained at low levels in tomato and spinach matrices. Infrared ion spectroscopy is an orthogonal tandem mass spectrometry technique that combines the sensitivity and selectivity of mass spectrometry with structural information obtained by infrared spectroscopy. Furthermore, IR spectra can be computationally predicted for candidate molecular structures, enabling the tentative identification of agrochemical derivatives and other unknowns in the environment without using physical reference standards.

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.

Investigation and probabilistic health risk assessment of pesticide residues in cucumber, tomato, and okra fruits from Khuzestan, Iran

In this study, 30 pesticide residues in 45 fresh-eating cucumber, tomato, and okra fruit samples collected from the Khuzestan province as the main agricultural products in Iran using the QuEChERS extraction method were analyzed. In addition, noncarcinogen and carcinogen health risk assessments were evaluated. Results indicated that 93% of cucumber samples had at least one pesticide, of course, less than the maximum residue limit (MRL). All tomato and okra fruit samples were contaminated by diazinon. All pesticides detected in tomato samples were below national MRL except for thiamethoxam in four samples. In okra fruit samples, all detected diazinon and malathion, but only tebuconazole fungicide exceeded MRL. In addition, the hazard index (HI) was 0.23 and 1.06 in cucumber samples, 0.33 and 1.51 in tomato samples, and 5.5E-03 and 0.025 in okra fruit samples in adults and children, respectively. The use of cucumber and tomato may have notable risks in the short term in children group age. Ranking based on total CR was 1.2E-05 in tomato, 7.7E-06 for cucumber, and in okra 9.1E-11 because of the difenoconazole residue. However, significant carcinogenic risk threatens cucumber and tomato consumers.

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.

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.

Analysis of cannabis seizures by non-targeted liquid chromatography-tandem mass spectrometry

Due to the popularity of recreational cannabis use, contamination of this drug with diverse classes of chemicals, including pesticides, mycotoxins, and synthetic cannabinoids, has been identified as major threat for public health. For the detection of these compounds in seized cannabis, a screening workflow involving non-targeted liquid chromatography-tandem mass spectrometry (LCMS/MS) was developed. A Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) method was used for the extraction of small bioorganic molecules from ground dried material. Instrumental analysis involved chromatographic separation of compounds and subsequent mass spectrometric detection. Collection of MS and MS/MS information was accomplished by data-dependent acquisition. Compound identification was primarily based on matching acquired MS/MS-spectra to several thousands of reference spectra stored in multiple libraries. Additionally, for selected cannabinoid and pesticide standards, a retention time library was developed. Performance of the workflow was evaluated for 182 pesticides. All tested pesticides were detectable at 5000 μg/kg, 94 % at 500 μg/kg, and 50 % at 50 μg/kg. The workflow was applied to the screening of seized cannabis samples. 41 out of 93 analysed samples (44 %) were tested positive for one or more contaminants impairing quality and/or safety of the material. The detected contaminants included a synthetic cannabinoid (5F-MDMB-PINACA), fifteen pesticide residues (boscalid, carbendazim, chlorantraniliprole, chlorpyrifos, chlorotoluron, cyprodinil, diflubenzuron, ethiofencarb sulfoxide, hexythiazox, iprodione, metalaxyl, pyrimethanil, terbutryn, thiophanate methyl, and trifloxystrobin), and a mycotoxin (sterigmatocystin).

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

Large-Scale Implementation and Flaw Investigation of Human Serum Suspect Screening Analysis for Industrial Chemicals

Non-targeted analysis (NTA), including both suspect screening analysis (SSA) and unknown compound analysis, has gained increasing popularity in various fields for its capability in identifying new compounds of interests. Current major challenges for NTA SSA are that (1) tremendous effort and resources are needed for large-scale identification and confirmation of suspect chemicals and (2) suspect chemicals generally show low matching rates during identification and confirmation processes. To narrow the gap between these challenges and smooth implementation of NTA SSA methodology in the biomonitoring field, we present a thorough SSA workflow for the large-scale screen, identification, and confirmation of industrial chemicals that may pose adverse health effects in pregnant women and newborns. The workflow was established in a study of 30 paired maternal and umbilical cord serum samples collected at delivery in the San Francisco Bay area. By analyzing LC-HRMS and MS/MS data, together with the assistance of a combination of resources including online MS/MS spectra libraries, online in silico fragmentation tools, and the EPA CompTox Chemicals Dashboard, we confirmed the identities of 17 chemicals, among which monoethylhexyl phthalate, 4-nitrophenol, tridecanedioic acid, and octadecanedioic acid are especially interesting due to possible toxicities and their high-volume use in industrial manufacturing. Similar to other previous studies in the SSA field, the suspect compounds show relatively low MS/MS identification (16%) and standard confirmation (8%) rates. Therefore, we also investigated origins of false positive features and unidentifiable suspected features, as well as technical obstacles encountered during the confirmation process, which would promote a better understanding of the flaw of low confirmation rate and encourage gaining more effective tools for tackling this issue in NTA SSA.

Biosensing based on upconversion nanoparticles for food quality and safety applications

Food safety and quality regulations inevitably call for sensitive and accurate analytical methods to detect harmful contaminants in food and to ensure safe food for the consumer. Both novel and well-established biorecognition elements, together with different transduction schemes, enable the simple and rapid analysis of various food contaminants. Upconversion nanoparticles (UCNPs) are inorganic nanocrystals that convert near-infrared light into shorter wavelength emission. This unique photophysical feature, along with narrow emission bandwidths and large anti-Stokes shift, render UCNPs excellent optical labels for biosensing because they can be detected without optical background interferences from the sample matrix. In this review, we show how this exciting technique has evolved into biosensing platforms for food quality and safety monitoring and highlight recent applications in the field.

Development of a High-Throughput Affinity Mass Spectrometry (AMS) Platform Using Laser Diode Thermal Desorption Ionization Coupled to Mass Spectrometry (LDTD-MS)

Affinity selection mass spectrometry (MS) or, simply, affinity mass spectrometry (AMS) is a label-free technology that has been used to identify high-affinity ligands of target proteins of interest by screening against small-molecule compound libraries and identifying molecules that are enriched in the presence of the target protein. We have previously applied Agilent Technology's (Santa Clara, CA) RapidFire solid-phase extraction (SPE)-based high-throughput MS technology to screen small-molecule libraries using AMS. However, SPE-based technologies rely on fluidics for desalting and separation prior to mass analysis with attendant high solvent consumption, relatively high sample volume requirements, risk of sample carryover, and frequent maintenance. To address these challenges, we have established an AMS platform using a laser diode thermal desorption-atmospheric pressure chemical ionization (LDTD-APCI) ionization source (Phytronix, Quebec, Canada) coupled with a SCIEX 5600+ TripleTOF MS (Framingham, MA). We also validated a data-independent acquisition (DIA) Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) method for the robust detection and analysis of small-molecule affinity hits. An informatics platform developed in-house has resulted in a streamlined data analysis workflow for high-throughput AMS screening campaigns and reduced data processing time without compromising data quality. Finally, 68,000 compounds were screened in a single plate and affinity selected hits were confirmed in an orthogonal enzyme activity assay.