Standard substances free quantification makes LC/ESI/MS non-targeted screening of pesticides in cereals comparable between labs

Examining environmental matrix effects on quantitative non-targeted analysis estimates of per- and polyfluoroalkyl substances

Non-targeted analysis (NTA) is commonly used for the detection and identification of emerging pollutants, including many per- and polyfluoroalkyl substances (PFAS). While NTA outputs are often non-quantitative, concentration estimation is now possible using quantitative non-targeted analysis (qNTA) approaches. To date, few studies have examined matrix effects on qNTA performance, and little is therefore known about the implications of matrix effects on qNTA results and interpretations. Using a set of 19 PFAS, we examined the impacts of drinking water (DW) and waste-activated sludge matrices on qNTA performance across three qNTA approaches: one structure-independent approach based on "global" surrogates and two structure-dependent approaches based on "expert-selected" surrogates and predicted ionization efficiency (IE) regression. The performance of each qNTA approach was examined separately for the PFAS prepared in pure solvent, DW extract, and sludge extract using leave-one-out modeling. Performance was evaluated using previously defined qNTA metrics that describe predictive accuracy, uncertainty, and reliability. The studied sample matrices had minimal effects on qNTA accuracy and larger effects on qNTA uncertainty and reliability. Using solvent-based surrogate data to inform matrix-based estimations yielded lower uncertainty, but also lower reliability, emphasizing that uncertainty must be considered in context of reliability. No single qNTA approach uniformly performed best across all comparisons. Since the IE regression and global surrogates approaches proved most reliable, we recommended them for future qNTA applications. This study highlights the importance of examining multiple performance metrics and utilizing matrix-matched surrogate data in qNTA studies.

Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications

Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targeted screening methods often fail to detect these compounds, making non-target analysis (NTA) using high-resolution mass spectrometry (HRMS) essential for identifying unknown or suspected contaminants. However, interpreting the vast datasets generated by HRMS is complex and requires advanced data processing techniques. Recent advancements in machine learning (ML) models offer great potential for enhancing NTA applications. As such, we reviewed key developments, including optimizing workflows using computational tools, improved chemical structure identification, advanced quantification methods, and enhanced toxicity prediction capabilities. It also discusses challenges and future perspectives in the field, such as refining ML tools for complex mixtures, improving inter-laboratory validation, and further integrating computational models into environmental risk assessment frameworks. By addressing these challenges, ML-assisted NTA can significantly enhance the detection, quantification, and evaluation of EECs, ultimately contributing to more effective environmental monitoring and public health protection.

Communicating with Stakeholders to Identify High-Impact Research Directions for Non-Targeted Analysis

Non-targeted analysis (NTA) using high-resolution mass spectrometry without defined chemical targets has the potential to expand and improve chemical monitoring in many fields. Despite rapid advancements within the research community, NTA methods and data remain underutilized by many potential beneficiaries. To better understand barriers toward widespread adoption, the Best Practices for Non-Targeted Analysis (BP4NTA) working group conducted focus group meetings and follow-up surveys with scientists (n = 61) from various sectors (e.g., drinking water utilities, epidemiologists, n = 9) where NTA is expected to provide future value. Meeting participants included producers and end-users of NTA data with a wide range of familiarity with NTA methods and outputs. Discussions focused on identifying specific barriers that limit adoption and on setting NTA product development priorities. Stated priorities fell into four major categories: 1) education and training materials; 2) QA/QC frameworks and study design guidance; 3) accessible compound databases and libraries; and 4) NTA data linkages with chemical fate and toxicity information. Based on participant feedback, this manuscript proposes research directions, such as standardization of training materials, that BP4NTA and other institutions can pursue to expand NTA use in various application scenarios and decision contexts.

Targeted Analysis, Metabolic Profiling, and Fingerprinting Based on an LC(GC)-MS Approach for the Comprehensive Evaluation of Pesticide Content in Edible Plants

Pesticides are commonly found in plant-based foods, which inevitably reduces food quality and poses significant health risks to consumers. The extensive variety of crops and the wide range of pesticides used means that no single analytical approach can provide clear and comprehensive information on the pesticide-protection status of a crop. Since most pesticide analyses in food rely on chromatographic techniques combined with various MS platforms, this article focuses exclusively on LC-MS and GC-MS system methodologies. In summary, this paper critically reviews analytical modes-specifically, multi reaction monitoring, data-dependent analysis, and data-independent analysis-and scanning regimes, including full scan, MS, MS/MS, suspect screening, and fingerprinting strategies, for pesticide detection in edible plants. The advantages and disadvantages of these methodologies, as well as their complementary applications, are thoroughly examined.

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.

Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS2 Data

Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.

Electrospray Ionization Efficiency Predictions and Analytical Standard Free Quantification for SFC/ESI/HRMS

Supercritical fluid chromatography (SFC) is a promising, sustainable, and complementary alternative to liquid chromatography (LC) and has often been coupled with high resolution mass spectrometry (HRMS) for nontarget screening (NTS). Recent developments in predicting the ionization efficiency for LC/ESI/HRMS have enabled quantification of chemicals detected in NTS even if the analytical standards of the detected and tentatively identified chemicals are unavailable. This poses the question of whether analytical standard free quantification can also be applied in SFC/ES/HRMS. We evaluate both the possibility to transfer an ionization efficiency predictions model, previously trained on LC/ESI/HRMS data, to SFC/ESI/HRMS as well as training a new predictive model on SFC/ESI/HRMS data for 127 chemicals. The response factors of these chemicals ranged over 4 orders of magnitude in spite of a postcolumn makeup flow, expectedly enhancing the ionization of the analytes. The ionization efficiency values were predicted based on a random forest regression model from PaDEL descriptors and predicted values showed statistically significant correlation with the measured response factors (p < 0.05) with Spearman's rho of 0.584 and 0.669 for SFC and LC data, respectively. Moreover, the most significant descriptors showed similarities independent of the chromatography used for collecting the training data. We also investigated the possibility to quantify the detected chemicals based on predicted ionization efficiency values. The model trained on SFC data showed very high prediction accuracy with median prediction error of 2.20×, while the model pretrained on LC/ESI/HRMS data yielded median prediction error of 5.11×. This is expected, as the training and test data for SFC/ESI/HRMS have been collected on the same instrument with the same chromatography. Still, the correlation observed between response factors measured with SFC/ESI/HRMS and predicted with a model trained on LC data hints that more abundant LC/ESI/HRMS data prove useful in understanding and predicting the ionization behavior in SFC/ESI/HRMS.

An enzyme-free, ultrasensitive strategy for simultaneous screening of the p-nitrophenyl substituent organophosphorus pesticides

An enzyme-free, sensitive, and convenient approach was reported for the P-nitrophenyl substituent organophosphorus pesticides (NSOPs) of paraoxon-methyl (PM), paraoxon-ethyl (PE), parathion-methyl (PTM) and parathion-ethyl (PTE)) by indirectly quantification of the 4-nitrophenol (4-NP, hydrolysis product of the NSOPs). NaOH instead of hydrolase/nanozyme was applied, and temperature, pH, ultrasound was investigated to improve the NSOPs hydrolysis. Under the optimized conditions, the hydrolysis efficiencies were up to 99.9 %, 99.9 %, 99.6 %, 96.0 % for PM (10 min), PE (30 min), PTM (90 min) and PTE (120 min), based on which a low detection limits of 0.06 (PM), 0.07 (PE), 0.06 (PTM) and 0.07 (PTE) ppb were calculated with the 4-NP detection limit (0.03 ppb). Furthermore, the method exhibited good performance for the NSOPs with recoveries from 88.87 % to 100.33 % in real samples. This indirect approach offered an ultrasensitive alternative for the NSOPs detection, which holds great potential in practical application for the assessment of food safety and environmental risks.

From fundamentals in calibration to modern methodologies: A tutorial for small molecules quantification in liquid chromatography-mass spectrometry bioanalysis

Over the last two decades, liquid chromatography coupled to mass-spectrometry (LC‒MS) has become the gold standard to perform qualitative and quantitative analyses of small molecules. When quantitative analysis is developed, an analyst usually refers to international guidelines for analytical method validation. In this context, the design of calibration curves plays a key role in providing accurate results. During recent years and along with instrumental advances, strategies to build calibration curves have dramatically evolved, introducing innovative approaches to improve quantitative precision and throughput. For example, when a labeled standard is available to be spiked directly into the study sample, the concentration of the unlabeled analog can be easily determined using the isotopic pattern deconvolution or the internal calibration approach, eliminating the need for multipoint calibration curves. This tutorial aims to synthetize the advances in LC‒MS quantitative analysis for small molecules in complex matrices, going from fundamental aspects in calibration to modern methodologies and applications. Different work schemes for calibration depending on the sample characteristics (analyte and matrix nature) are distinguished and discussed. Finally, this tutorial outlines the importance of having international guidelines for analytical method validation that agree with the advances in calibration strategies and analytical instrumentation.

Acidic pH and thiol-driven homogeneous cathodic electrochemiluminescence strategy for determining the residue of organophosphorus pesticide in Chinese cabbage

Electrochemiluminescent (ECL) sensors for organophosphorus pesticides (OPs) have received considerable attention, whereas complicated electrode's immobilization, response to single hydrolysate and anodic emission correlated with ECL assays restrict their potential utilization. Herein, we developed a homogeneous dual-response cathodic ECL system for highly sensitive and reliable analysis of OP using CdTe QDs as emitters. CdTe QDs, emitting red light, were fabricated through a hydrothermal reaction and generated anodic and cathodic ECL emission upon stimulation of tripropyl amine and K₂S₂O₈, respectively. Notably, CdTe QDs-K₂S₂O₈ showed a simultaneous response to thiol and acidic pH, and were regarded as a ECL sensor for methidathion with limit of detection of 0.016 ng/mL based on hydrolysis of acetylthiocholine into thiocholine and CH₃COOH by acetylcholinesterase (AChE) and OPs' inhibition on AChE activity. This sensor also exhibited good practicability to detect methidathion in Chinese cabbage. Overall, the sensor will supply more useful information for ensuring OPs-related food safety.

Analysis and characterization of novel fluorinated compounds used in surface treatments products

Side-chain fluorinated polymers are speculated to be potential precursors to other non-polymeric aliphatic per- and polyfluoroalkyl acids (PFAAs). Limited knowledge of environmental occurrence of this compound class is partly due to lack of structural information and authentic standards. In this study, two novel fluorinated compounds, suspected to be side-chain fluorinated copolymers used in two commercial technical mixtures (Scotchgard™ Pre-2002 formulation and Scotchgard™ Post-2002 formulation) were analyzed and characterized in order to provide information to facilitate detection and quantification. The commercial mixtures were analyzed using tandem mass spectrometry and high-resolution mass spectrometry; besides already reported C4- and C8-fluoroalkylsulfonamido (FASA) side-chains, a proposed structure was determined for the perfluorooctane (C8) sulfonamide-urethane copolymer in the Pre-2002 formulation. Structural isomers were also observed for C4- and C8-FASA-based copolymers. Total fluorine analysis revealed that the Scotchgard™ Pre-2002 Formulation contained a fluorine content of 0.5% and 1.8% for the Scotchgard™ Post-2002 Formulation. The equivalent FASA side-chain content was determined to be 0.8% for Pre-2002 and 3.1% for Post-2002. Both C4- and C8-FASA-based copolymers underwent hydrolysis and oxidation and were transformed to their respective perfluoroalkyl side chain, which suggest that transformation products can be analyzed for example after total oxidizable precursor (TOP) assay. Both compounds were shown to strongly sorb to sediment particles, which also gives indications about their environmental fate and transport pathways.

Estimation of the concentrations of hydroxylated polychlorinated biphenyls in human serum using ionization efficiency prediction for electrospray

Hydroxylated PCBs are an important class of metabolites of the widely distributed environmental contaminants polychlorinated biphenyls (PCBs). However, the absence of authentic standards is often a limitation when subject to detection, identification, and quantification. Recently, new strategies to quantify compounds detected with non-targeted LC/ESI/HRMS based on predicted ionization efficiency values have emerged. Here, we evaluate the impact of chemical space coverage and sample matrix on the accuracy of ionization efficiency-based quantification. We show that extending the chemical space of interest is crucial in improving the performance of quantification. Therefore, we extend the ionization efficiency-based quantification approach to hydroxylated PCBs in serum samples with a retraining approach that involves 14 OH-PCBs and validate it with an additional four OH-PCBs. The predicted and measured ionization efficiency values of the OH-PCBs agreed within the mean error of 2.1 × and enabled quantification with the mean error of 4.4 × or better. We observed that the error mostly arose from the ionization efficiency predictions and the impact of matrix effects was of less importance, varying from 37 to 165%. The results show that there is potential for predictive machine learning models for quantification even in very complex matrices such as serum. Further, retraining the already developed models provides a timely and cost-effective solution for extending the chemical space of the application area.

Sodium adduct formation with graph-based machine learning can aid structural elucidation in non-targeted LC/ESI/HRMS

Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds using their retention time, exact mass, and fragmentation pattern. Challenges remain in differentiating between isomeric compounds. One untapped possibility to facilitate identification of isomers relies on different ionic species formed in electrospray. In positive ESI mode, both protonated molecules and adducts can be formed; however, not all isomeric structures form the same ionic species. The complicated mechanism of adduct formation has hindered the use of this molecular characteristic in the structural elucidation in non-targeted screening. Here, we have studied the adduct formation for 94 small molecules with ion mobility spectra and compared collision cross-sections of the respective ions. Based on the results we developed a fast support vector machine classifier with polynomial kernels for accurately predicting the sodium adduct formation in ESI/HRMS. The model is trained on five independent data sets from different laboratories and uses the graph-based connectivity of functional groups and PubChem fingerprints to predict the sodium adduct formation in ESI/HRMS. The validation of the model showed an accuracy of 74.7% (balanced accuracy 70.0%) on a dataset from an independent laboratory, which was not used in the training of the model. Lastly, we applied the classification algorithm to the SusDat database by NORMAN network to evaluate the proportion of isomeric compounds that could be distinguished based on predicted sodium adduct formation. It was observed that sodium adduct formation probability can provide additional selectivity for about one quarter of the exact masses and, therefore, shows practical utility for structural assignment in non-targeted screening.

A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS

There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .

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.

Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS

LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches- one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information.

An ionic liquid-assisted quantum dot-grafted covalent organic framework-based multi-dimensional sensing array for discrimination of insecticides using principal component analysis and clustered heat map

A robust multi-dimensional sensing array based on VBimBF₄B/MAA-anchored quantum dot (QD)-grafted covalent organic frameworks (COFs) [(V-M)/QD-grafted COFs] was established via one-pot strategy. The multi-dimensional sensing array has the outstanding advantages of physicochemical and thermal stability, large specific surface area, and regular pore structures. The assistance of ionic liquid VBimBF₄B enhanced the transduction efficiency, and the synergistic effect of COFs enhanced detection efficiency. The improved multi-dimensional sensing array by COFs and ionic liquid VBimBF₄B served to identify seven insecticides by non-specific interactions via hydrogen bonding, and the differences in the kinetics of the binding to the insecticides resulted in variation of the three-output channel (fluorescence, phosphorescence, and light scattering) signals, thus generating a distinct optical fingerprint. The unique fingerprint patterns of seven kinds of common insecticides at 200 μg L^-1 were successfully discriminated using principal component analysis and clustered heat map analysis. The multi-dimensional sensing array showed a response to seven insecticides based on three spectral channels over the range of 0.001-0.4 μg mL^-1 with a limit of detection of 1.08-18.68 μg L^-1. The spiked recovery of tap water was 79.86-134.22%, with RSD ranging from 0.89-14.9%. This study broadens the applications of sensing arrays technology and provides a promising building block for insecticide determination.

Guide to Semi-Quantitative Non-Targeted Screening Using LC/ESI/HRMS

Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS.

Development and Application of a Novel Semi-quantification Approach in LC-QToF-MS Analysis of Natural Products

Use of high-resolution mass spectrometry (HRMS) including a MS calibration method has enabled simultaneous identification and quantification of knowns/unknowns. This has expanded our knowledge about the existing sample relevant chemical space in a way beyond reconciliation with a quantification task. This is largely due to fact that reference standards are not always available to achieve quantitative analysis. In this scenario, a semi-quantitative approach can fill the gap and provide a rough estimation of concentration. This research aimed to develop and compare several semi-quantification approaches based on chemical similarity or properties. The ionization efficiency scale was created for several groups of natural products. Advanced modeling approach based on a support vector machine was conducted to learn from the experimental ionization efficiency and apply it to unknowns or suspected compounds to predict their ionization efficiency in electrospray ionization mode. The developed semi-quantification workflows could be useful in most HRMS based "omics" areas, especially in natural products discovery.

Transfer learning for small molecule retention predictions

Small molecule retention time prediction is a sophisticated task because of the wide variety of separation techniques resulting in fragmented data available for training machine learning models. Predictions are typically made with traditional machine learning methods such as support vector machine, random forest, or gradient boosting. Another approach is to use large data sets for training with a consequent projection of predictions. Here we evaluate the applicability of transfer learning for small molecule retention prediction as a new approach to deal with small retention data sets. Transfer learning is a state-of-the-art technique for natural language processing (NLP) tasks. We propose using text-based molecular representations (SMILES) widely used in cheminformatics for NLP-like modeling on molecules. We suggest using self-supervised pre-training to capture relevant features from a large corpus of one million molecules followed by fine-tuning on task-specific data. Mean absolute error (MAE) of predictions was in range of 88-248 s for tested reversed-phase data sets and 66 s for HILIC data set, which is comparable with MAE reported for traditional machine learning models based on descriptors or projection approaches on the same data.

Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater

A wide range of micropollutants can be monitored with non-targeted screening; however, the quantification of the newly discovered compounds is challenging. Transformation products (TPs) are especially problematic because analytical standards are rarely available. Here, we compared three quantification approaches for non-target compounds that do not require the availability of analytical standards. The comparison is based on a unique set of concentration data for 341 compounds, mainly pesticides, pharmaceuticals, and their TPs in 31 groundwater samples from Switzerland. The best accuracy was observed with the predicted ionization efficiency-based quantification, the mean error of concentration prediction for the groundwater samples was a factor of 1.8, and all of the 74 micropollutants detected in the groundwater were quantified with an error less than a factor of 10. The quantification of TPs with the parent compounds had significantly lower accuracy (mean error of a factor of 3.8) and could only be applied to a fraction of the detected compounds, while the mean performance (mean error of a factor of 3.2) of the closest eluting standard approach was similar to the parent compound approach.

Label-free hairpin-like aptamer and EIS-based practical, biostable sensor for acetamiprid detection

Acetamiprid (ACE) is a kind of broad-spectrum pesticide that has potential health risk to human beings. Aptamers (Ap-DNA (1)) have a great potential as analytical tools for pesticide detection. In this work, a label-free electrochemical sensing assay for ACE determination is presented by electrochemical impedance spectroscopy (EIS). And the specific binding model between ACE and Ap-DNA (1) was further investigated for the first time. Circular dichroism (CD) spectroscopy and EIS demonstrated that the single strand AP-DNA (1) first formed a loosely secondary structure in Tris-HClO₄ (20 mM, pH = 7.4), and then transformed into a more stable hairpin-like structure when incubated in binding buffer (B-buffer). The formed stem-loop bulge provides the specific capturing sites for ACE, forming ACE/AP-DNA (1) complex, and induced the R_CT (charge transfer resistance) increase between the solution-based redox probe [Fe(CN)₆]^3−/4− and the electrode surface. The change of ΔR_CT (charge transfer resistance change, ΔR_CT = R_CT(after)-R_CT(before)) is positively related to the ACE level. As a result, the AP-DNA (1) biosensor showed a high sensitivity with the ACE concentration range spanning from 5 nM to 200 mM and a detection limit of 1 nM. The impedimetric AP-DNA (1) sensor also showed good selectivity to ACE over other selected pesticides and exhbited excellent performance in environmental water and orange juice samples analysis, with spiked recoveries in the range of 85.8% to 93.4% in lake water and 83.7% to 89.4% in orange juice. With good performance characteristics of practicality, sensitivity and selectivity, the AP-DNA (1) sensor holds a promising application for the on-site ACE detection.

Triple-dimensional spectroscopy combined with chemometrics for the discrimination of pesticide residues based on ionic liquid-stabilized Mn-ZnS quantum dots and covalent organic frameworks

Manganese-doped zinc sulfide quantum dots (Mn-ZnS QDs) are promising candidates for multi-channel sensing analysis due to their multi-dimensional optical properties. In this study, we integrated amino-silane and ionic liquid co-modified Mn-ZnS QDs and covalent organic frameworks (COFs) into optosensing nanoparticles to provide triple-dimensional optical response signals and combined them with chemometrics for the analysis of multiple pesticide residues. Through the exploration and optimization of a series of conditions, fluorescence, room temperature phosphorescence, and ultraviolet-visible combined with chemometrics were used for the discrimination and recognition of multiple pesticide residues in fruits and vegetables. The ionic liquid of 1-vinyl-3-ethylimidazolium tetrafluoroborate was used to modify Mn-ZnS QDs to improve the optical response and enrichment of pesticide adsorption sites, which were also synergistically enhanced by the COF support. This is a potential method to discriminate pesticides efficiently and enables fast and reliable analysis of pesticides in the agricultural and food industries.

A theorem on a product of lognormal variables and hybrid models for children's exposure to soil contaminants

This study developed hybrid Bayesian models to investigate the modeling process for children's exposure to soil contaminants, which involves the intrinsic uncertainty of the exposure model, people's judgments regarding random variables, and limited data resources. A hybrid Bayesian p-box was constructed, which was facilitated by a multiple integral dimensionality reduction (MIDR) theorem. The results indicated that exposure frequency (EF) dominated the exposure dose. The hybrid Bayesian p-box for the Frequentist-Bayesian (F-B) model at the 95th percentile of the simulated average daily dose (ADD) values corresponded to a 4.40 order-of-magnitude difference between the upper and lower bounds of the p-box. This considerable uncertainty was magnified by the combination of the highest posterior density (HPD) regions for three groups of the distribution parameters. For the Interior-Bayesian (I-B) hybrid model, the uncertainty of the outcomes, namely, [1.75 × 10^-8, 2.18 × 10^-8] mg kg^-1d^-1, was limited by the HPD regions for only one parameter unless the hyperparameters for the variables' distributions were further evaluated. It was concluded that the hybrid models could provide a novel understanding of the complexity of the exposure modeling process compared to the traditional modeling method.

Strategies for Drawing Quantitative Conclusions from Nontargeted Liquid Chromatography-High-Resolution Mass Spectrometry Analysis

This Feature aims at giving an overview of different possibilities for quantitatively comparing the results obtained from LC-HRMS-based nontargeted analysis. More specifically, quantification via structurally similar internal standards, different isotope labeling strategies, radiolabeling, and predicted ionization efficiencies are reviewed.