Predicting ESI/MS Signal Change for Anions in Different Solvents

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms

Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect. With the organic phase ratio from low to high, most compounds changed by 1.0 log units in log IE. The addition of formic acid enhanced the signal but also promoted the matrix effect, which often occurred in compounds with strong ionization capacity. It was speculated that the matrix effect was mainly in the form of competitive charge and charged droplet' gasification sites during MS detection. Subsequently, we present a log IE prediction method built using the COSMO-RS software and the artificial neural network (ANN) algorithm to address this difficulty and overcome the shortcomings of previous models, which always ignore the matrix effect. This model was developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). Furthermore, we validated this approach by predicting the log IE of 70 compounds, including those not involved in the log IE model development. The results presented demonstrate that the method we put forward has an excellent prediction accuracy for log IE (R2pred = 0.880), which means that it has the potential to predict the log IE of new compounds without authentic standards.

Quantification of chemicals in non-targeted analysis without analytical standards - Understanding the mechanism of electrospray ionization and making predictions

The constant creation and release of new chemicals to the environment is forming an ever-widening gap between available analytical standards and known chemicals. Developing non-targeted analysis (NTA) methods that have the ability to detect a broad spectrum of compounds is critical for research and analysis of emerging contaminants. There is a need to develop methods that make it possible to identify compound structures from their MS and MS/MS information and quantify them without analytical standards. Method refinements that utilize machine learning algorithms and chemical descriptors to estimate the instrument response of particular compounds have made progress in recent years. This narrative review seeks to summarize the current state of the field of non-targeted analysis (NTA) toward quantification of unknowns without the use of analytical standards. Despite the limited accumulation of validation studies on real samples, the ongoing enhancement in data processing and refinement of machine learning tools could lead to more comprehensive chemical coverage of NTA and validated quantitative NTA methods, thus boosting confidence in their usage and enhancing the utility of quantitative NTA.

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.

Determination of Response Factors for Analytes Detected during Migration Studies, Strategy and Internal Standard Selection for Risk Minimization

Migration studies are one of the few domains of pharmaceutical analysis employing wide-scope screening methodologies. The studies involve the detection of contaminants within pharmaceutical products that arise from the interaction between the formulation and materials. Requiring both qualitative and quantitative data, the studies are conducted using Liquid Chromatography or Gas Chromatography coupled to a mass spectrometer (LC-MS and GC-MS). While mass spectrometry allows wide-scope analyte detection and identification at the very low Analytical Evaluation Threshold (AET) levels used in these studies, MS detectors are far from "universal response" detectors. Regulation brings the application of uncertainty factors into the picture to limit the risk of potential analytes detected escaping report and further evaluation; however, whether the application of a default value can cover any or all relevant applications is still debatable. The current study evaluated the response of species usually detected in migration studies, generating a suitable representative sample, analyzing said species, and creating a strategy and evaluation mechanism for acceptable classification of the detected species. Incorporating novel methodologies, i.e., Design of Experiments (DoE) for Design Space generation, the LC-MS-based methodology is also evaluated for its robustness in changes performed.

Ion transmission in an electrospray ionization-mass spectrometry interface using an S-lens

We present the design and performance of an in-house built electrospray ionization-mass spectrometry (ESI-MS) interface equipped with an S-lens ion guide. The ion source was designed specifically for our ion beam experiments to investigate the chemical reactivity and deposition of the clusters and nanoparticles. It includes standard ESI-MS interface components, such as nanoelectrospray, ion transfer capillary, and the S-lens. A custom design enables systematic optimization of all relevant factors influencing ion formation and transfer through the interface. By varying the ESI voltage and flow rate, we determined the optimal operating conditions for selected silica emitters. A comparison of the pulled silica emitters with different tip inner diameters reveals that the total ion current is highest for the largest tip, whereas a tip with the smallest diameter exhibited the highest transmission efficiency through the ESI-MS interface. Ion transmission through the transfer capillary is strongly limited by its length, but the loss of ions can be reduced by increasing the capillary voltage and temperature. The S-lens was characterized over a wide range of RF frequencies and amplitudes. Maximum ion current was detected at RF amplitudes greater than 50 V peak-to-peak (p/p) and frequencies above 750 kHz, with a stable ion transmission region of about 20%. A factor of 2.6 increase in total ion current is observed for 650 kHz as RF amplitudes reach 400 V p/p. Higher RF amplitudes also focus the ions into a narrow beam, which mitigates their losses when passing through the ion guide.

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.

Predicting liquid chromatography-electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities

A priori estimation of analyte response is crucial for the efficient development of liquid chromatography-electrospray ionization/mass spectrometry (LC-ESI/MS) methods, but remains a demanding task given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure-property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC-ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the peak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC-ESI/MS that provided a holistic overview of the analyte's response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA-GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency.

Analysis of copper-induced protein precipitation across the E. coli proteome

Metal cations have been exploited for their precipitation properties in a wide variety of studies, ranging from differentiating proteins from serum and blood to identifying the protein targets of drugs. Despite widespread recognition of this phenomenon, the mechanisms of metal-induced protein aggregation have not been fully elucidated. Recent studies have suggested that copper's (Cu) ability to induce protein aggregation may be a main contributor to Cu-induced cell death. Here, we provide the first proteome-wide analysis of the relative sensitivities of proteins across the Escherichia coli proteome to Cu-induced aggregation. We utilize a metal-induced protein precipitation (MiPP) methodology that relies on quantitative bottom-up proteomics to define the metal concentration-dependent precipitation properties of proteins on a proteomic scale. Our results establish that Cu far surpasses other metals in promoting protein aggregation and that the protein aggregation is reversible upon metal chelation. The bulk of the Cu bound in the protein aggregates is Cu1+, regardless of the Cu2+ source. Analysis of our MiPP data allows us to investigate underlying biophysical characteristics that determine a protein's sensitivity to Cu-induced aggregation, which is independent of the relative concentration of protein in the lysate. Overall, this analysis provides new insights into the mechanism behind Cu cytotoxicity, as well as metal cation-induced protein aggregation.

Equilibration for Electrospray Ionization Mass Spectrometry in Quantitative Analysis

Electrospray ionization mass spectrometry (ESI-MS) is widely used in drug development, therapeutic drug monitoring, and other fields. However, unstable mass spectral signals, especially during the initial stages of instrument operation, plague analysts. Generally, in quantitative experiments, the stability of response can be achieved by running the analytical system for some time. However, the equilibration time required for the responses of different compounds to stabilize has been elusive. To investigate the response stability of the ESI-MS system, 72 compounds with different physicochemical properties were employed on three systems, and flow injection analysis was performed in positive ion mode. With the use of 5.00% (response stable factor, RSF) as the stability limit, about 80% of the compounds were stable within 60 min. Under a 2.00% criterion, the stabilization time was significantly longer. The stabilization time varies with different instruments and physicochemical properties of the compounds. When positive ion detection is performed in an acidic mobile phase, the octanol-water partition coefficient (Log P), molecular weight, and molar volume can all affect the time required to stabilize the response. In general, it is necessary to balance the ESI-MS system for an appropriate time before sample detection, especially for the analysis of compounds with strong hydrophilicity, small molecular weight, or small molar volume under the conditions above.

First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography-Atmospheric Pressure Chemical Ionization-Quadrupole Time of Flight-Mass Spectrometry

The ionization efficiency of emerging contaminants was modeled for the first time in gas chromatography-high-resolution mass spectrometry (GC-HRMS) which is coupled to an atmospheric pressure chemical ionization source (APCI). The recent chemical space has been expanded in environmental samples such as soil, indoor dust, and sediments thanks to recent use of high-resolution mass spectrometric techniques; however, many of these chemicals have remained unquantified. Chemical exposure in dust can pose potential risk to human health, and semiquantitative analysis is potentially of need to semiquantify these newly identified substances and assist with their risk assessment and environmental fate. In this study, a rigorously tested semiquantification workflow was proposed based on GC-APCI-HRMS ionization efficiency measurements of 78 emerging contaminants. The mechanism of ionization of compounds in the APCI source was discussed via a simple connectivity index and topological structure. The quantitative structure–property relationship (QSPR)-based model was also built to predict the APCI ionization efficiencies of unknowns and later use it for their quantification analyses. The proposed semiquantification method could be transferred into the household indoor dust sample matrix, and it could include the effect of recovery and matrix in the predictions of actual concentrations of analytes. A suspect compound, which falls inside the application domain of the tool, can be semiquantified by an online web application, free of access at http://trams.chem.uoa.gr/semiquantification/.

Standard-Free Quantification of Dicarboxylic Acids: Case Studies with Salt-Rich Effluents and Serum

The present study evaluates the ionization efficiency (IE) of linear and branched C₂-C₁₄ dicarboxylic acids (DCAs) by electrospray ionization (ESI) under different conditions. The influence of the concentration of organic modifier (MeOH); mobile phase additive; and its concentration, pH, and DCA structure on IE values is studied using flow injection analysis. The IE values of DCAs increase with the increase of MeOH concentration but also decrease with an increase of pH. The former is due to the increase in solvent evaporation rates; the latter is caused by an ion-pairing between the diacid and the cation (ammonium), which is confirmed by the study with different amines. The investigation of DCA ionization in the presence of different acidic mobile phase additives showed that a significant improvement in the (-)ESI responses of analytes was achieved in the presence of weak hydrophobic carboxylic acids, such as butyric or propanoic acid. Conversely, the use of strong carboxylic acids, such as trichloroacetic acid, was found to cause signal suppression. The results of the IE studies were used to develop the liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method that provided instrumental limits of detection in the range from 6 to 180 pg. Furthermore, upon applying the nonparametric Gaussian process, a model for the prediction of IE values was developed, which contains the number of carbons in the molecule and MeOH concentration as model parameters. As a case study, dicarboxylic acids are quantified in salt-rich effluent and blood serum samples using the developed LC-HRMS method.

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.

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/ .

Abundance of organosulfates derived from biogenic volatile organic compounds: Seasonal and spatial contrasts at four sites in China

Atmospheric organosulfates (OSs) derived from biogenic volatile organic compounds (BVOCs) encode chemical interaction strength between anthroposphere and biosphere. We report BVOC-derived OSs in the summer of 2016 and the winter of 2017 at four locations in China (i.e., Hong Kong (HK), Guangzhou (GZ), Shanghai (SH), and Beijing (BJ)). The spatial coverage of three climatic zones from the south to the north in China is accompanied with a wide range of aerosol inorganic sulfate (4.9-13.8 μg/m³). We employed a combined targeted and untargeted approach using high-performance liquid chromatography-Orbitrap mass spectrometry to quantify/semi-quantify ~200 OSs and nitrooxy OSs derived from four types of precursors, namely C₂-C₃ oxygenated VOCs, isoprene, monoterpenes (MT), and sesquiterpenes (ST). The seasonal averages of the total quantified OSs across the four sites are in the range of 201-545 (summer) and 123-234 ng/m³ (winter), with the isoprene-derived OSs accounting for more than 80% (summer) and 57% (winter). The C_2-3 OSs and isoprene-derived OSs share the same seasonality (summer >winter) and the same south-north spatial gradient as those of isoprene emissions. In contrast, the MT- and ST-derived OSs are of either comparable abundance or slightly higher abundance in winter at the four sites. The spatial contrasts for MT- and ST-derived OSs are not clearly discernable among GZ, SH, and BJ. HK is noted to have invariably lower abundances of all groups of OSs, in line with its aerosol inorganic sulfate being the lowest. These results indicate that BVOC emissions are the driving factor regulating the formation of C_2-3 OSs and isoprene-derived OSs. Other factors, such as sulfate abundance, however, play a more important role in the formation of MT- and ST-derived OSs. This in turn suggests that the formation kinetics and/or pathways differ between these two sub-groups of BVOCs-derived OSs.

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.

Quantitative analysis of the relationship of derivatization reagents and detection sensitivity of electrospray ionization-triple quadrupole tandem mass spectrometry: Hydrazines as prototypes

Chemical derivatization-assisted electrospray ionization-triple quadrupole mass spectrometry (ESI-QqQ-MS) has become an efficient tool for the quantification of low-molecular-weight molecules. Many studies found that the derivatives of the same analytes derivatized by different derivatization reagents with the same reaction group had different detection sensitivity, even under the same conditions of electrospray ionization-mass spectrometry (ESI-MS). This phenomenon was suggested to be caused by the different modifying groups in the derivatization reagents. However, there is still a lack of systematic study on how modifying groups in the derivatization reagents affect the detection sensitivity of their corresponding derivatives of analytes, especially theoretical investigations. In this study, we employed a quantitative structure-activity relationship (QSAR) modeling approach to explore the relationship between modifying group structures and the detection sensitivity of derivatization reagents and their derivatives during ESI-MS detection. A total of 110 derivatization reagents of the hydrazine family and their hexanal derivatives (substituted hydrazones) were selected as the prototypes to construct QSAR models. The established models suggested that several molecular descriptors, related to hydrophobicity, electronegativity, and molecular shape, were related to the detection sensitivity of hexanal derivatives induced by different modifying groups in the derivatization reagents. Besides, we found that the detection sensitivity of compounds detected in selected ion mode (SIM) showed a positive correlation with that obtained in multiple reaction monitoring mode (MRM), and the ionization efficiency was the key factor on the detection sensitivity in both modes.

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.

30 Years of research on ESI/MS response: Trends, contradictions and applications

The variation of ionization efficiency for different compounds has puzzled researchers since the invention of the electrospray mass spectrometry (ESI/MS). Ionization depends on the properties of the compound, eluent, matrix, and instrument. Despite significant research, some aspects have remained unclear. For example, research groups have reached contradicting conclusions regarding the ionization processes. One of the best-known is the significance of the logP value for predicting the ionization efficiency. In this tutorial review, we analyse the methodology used for ionization efficiency measurements as well as the most important trends observed in the data. Additionally, we give suggestions regarding the measurement methodology and modelling strategies to yield meaningful and consistent ionization efficiency data. Finally, we have collected a wide range of ionization efficiency values from the literature and evaluated the consistency of these data. We also make this collection available for everyone for downloading as well as for uploading additional and new ionization efficiency data. We hope this GitHub based ionization efficiency repository will allow a joined community effort to collect and unify the current knowledge about the ionization processes.

Synthesis of Carboxylic Acid and Dimer Ester Surrogates to Constrain the Abundance and Distribution of Molecular Products in α-Pinene and β-Pinene Secondary Organic Aerosol

Liquid chromatography/negative electrospray ionization mass spectrometry [LC/(-)ESI-MS] is routinely employed to characterize the identity and abundance of molecular products in secondary organic aerosol (SOA) derived from monoterpene oxidation. Due to a lack of authentic standards, however, commercial terpenoic acids (e.g., cis-pinonic acid) are typically used as surrogates to quantify both monomeric and dimeric SOA constituents. Here, we synthesize a series of enantiopure, pinene-derived carboxylic acid and dimer ester homologues. We find that the (-)ESI efficiencies of the dimer esters are 19-36 times higher than that of cis-pinonic acid, demonstrating that the mass contribution of dimers to monoterpene SOA has been significantly overestimated in past studies. Using the measured (-)ESI efficiencies of the carboxylic acids and dimer esters as more representative surrogates, we determine that molecular products measureable by LC/(-)ESI-MS account for only 21.8 ± 2.6% and 18.9 ± 3.2% of the mass of SOA formed from ozonolysis of α-pinene and β-pinene, respectively. The 28-36 identified monomers (C_7-10H_10-18O_3-6) constitute 15.6-20.5% of total SOA mass, whereas only 1.3-3.3% of the SOA mass is attributable to the 46-62 identified dimers (C_15-19H_24-32O_4-11). The distribution of identified α-pinene and β-pinene SOA molecular products is examined as a function of carbon number (n_C), average carbon oxidation state (OS¯C), and volatility (C*). The observed order-of-magnitude difference in (-)ESI efficiency between monomers and dimers is expected to be broadly applicable to other biogenic and anthropogenic SOA systems analyzed via (-) or (+) LC/ESI-MS under various LC conditions, and demonstrates that the use of unrepresentative surrogates can lead to substantial systematic errors in quantitative LC/ESI-MS analyses of SOA.

New Approach Combining Molecular Fingerprints and Machine Learning to Estimate Relative Ionization Efficiency in Electrospray Ionization

Electrospray ionization (ESI) is widely used as an ionization source for the analysis of complex mixtures by mass spectrometry. However, different compounds ionize more or less effectively in the ESI source, meaning instrument responses can vary by orders of magnitude, often in hard-to-predict ways. This precludes the use of ESI for quantitative analysis where authentic standards are not available. Relative ionization efficiency (RIE) scales have been proposed as a route to predict the response of compounds in ESI. In this work, a scale of RIEs was constructed for 51 carboxylic acids, spanning a wide range of additional functionalities, to produce a model for predicting the RIE of unknown compounds. While using a limited number of compounds, we explore the usefulness of building a predictor using popular supervised regression techniques, encoding the compounds as combinations of different structural features using a range of common "fingerprints". It was found that Bayesian ridge regression gives the best predictive model, encoding compounds using features designed for activity coefficient models. This produced a predictive model with an R ² score of 0.62 and a root-mean-square error (RMSE) of 0.362. Such scores are comparable to those obtained in previous studies but without the requirement to first measure or predict the physical properties of the compounds, potentially reducing the time required to make predictions.

Quantification for non-targeted LC/MS screening without standard substances

Non-targeted and suspect analyses with liquid chromatography/electrospray/high-resolution mass spectrometry (LC/ESI/HRMS) are gaining importance as they enable identification of hundreds or even thousands of compounds in a single sample. Here, we present an approach to address the challenge to quantify compounds identified from LC/HRMS data without authentic standards. The approach uses random forest regression to predict the response of the compounds in ESI/HRMS with a mean error of 2.2 and 2.0 times for ESI positive and negative mode, respectively. We observe that the predicted responses can be transferred between different instruments via a regression approach. Furthermore, we applied the predicted responses to estimate the concentration of the compounds without the standard substances. The approach was validated by quantifying pesticides and mycotoxins in six different cereal samples. For applicability, the accuracy of the concentration prediction needs to be compatible with the effect (e.g. toxicology) predictions. We achieved the average quantification error of 5.4 times, which is well compatible with the accuracy of the toxicology predictions.

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.

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

LC/ESI/MS is the technique of choice for qualitative and quantitative food monitoring; however, analysis of a large number of compounds is challenged by the availability of standard substances. The impediment of detection of food contaminants has been overcome by the suspect and non-targeted screening. Still, the results from one laboratory cannot be compared with the results of another laboratory as quantitative results are required for this purpose. Here we show that the results of the suspect and non-targeted screening for pesticides can be made quantitative with the aid of in silico predicted electrospray ionization efficiencies and this allows direct comparison of the results obtained in two different laboratories. For this purpose, six cereal matrices were spiked with 134 pesticides and analysed in two independent labs; a high correlation for the results with the R² of 0.85.

Recent developments and emerging trends of mass spectrometric methods in plant hormone analysis: a review

Plant hormones are naturally occurring small molecule compounds which are present at trace amounts in plant. They play a pivotal role in the regulation of plant growth. The biological activity of plant hormones depends on their concentrations in the plant, thus, accurate determination of plant hormone is paramount. However, the complex plant matrix, wide polarity range and low concentration of plant hormones are the main hindrances to effective analyses of plant hormone even when state-of-the-art analytical techniques are employed. These factors substantially influence the accuracy of analytical results. So far, significant progress has been realized in the analysis of plant hormones, particularly in sample pretreatment techniques and mass spectrometric methods. This review describes the classic extraction and modern microextraction techniques used to analyze plant hormone. Advancements in solid phase microextraction (SPME) methods have been driven by the ever-increasing requirement for dynamic and in vivo identification of the spatial distribution of plant hormones in real-life plant samples, which would contribute greatly to the burgeoning field of plant hormone investigation. In this review, we describe advances in various aspects of mass spectrometry methods. Many fragmentation patterns are analyzed to provide the theoretical basis for the establishment of a mass spectral database for the analysis of plant hormones. We hope to provide a technical guide for further discovery of new plant hormones. More than 140 research studies on plant hormone published in the past decade are reviewed, with a particular emphasis on the recent advances in mass spectrometry and sample pretreatment techniques in the analysis of plant hormone. The potential progress for further research in plant hormones analysis is also highlighted.

Semi-quantitative non-target analysis of water with liquid chromatography/high-resolution mass spectrometry: How far are we?

Combining high-resolution mass spectrometry (HRMS) with liquid chromatography (LC) has considerably increased the capability of analytical chemistry. Among others, it has stimulated the growth of the non-target analysis, which aims at identifying compounds without their preceding selection. This approach is already widely applied in various fields, such as metabolomics, proteomics, etc. The applicability of LC/HRMS-based non-target analysis in environmental analyses, such as water studies, would be beneficial for understanding the environmental fate of polar pollutants and evaluating the health risks exposed by the new emerging contaminants. During the last five to seven years the use of LC/HRMS-based non-target analysis has grown rapidly. However, routine non-target analysis is still uncommon for most environmental monitoring agencies and environmental scientists. The main reasons are the complicated data processing and the inability to provide quantitative information about identified compounds. The latter shortcoming follows from the lack of standard substances, considered so far as the soul of each quantitative analysis for the newly discovered pollutants. To overcome this, non-target analyses could be combined with semi-quantitation. This Perspective aims at describing the current methods for non-target analysis, the possibilities and challenges of standard substance-free semi-quantitative analysis, and proposes tools to join these two fields together.

Influence of the amino acid composition on the ionization efficiencies of small peptides

Electrospray ionization is widely used to generate gas phase ions from a variety of molecules ranging from small ions to large proteins, while the ionization mechanism is claimed to depend on the size of the molecule. For small molecules, the ionization efficiency, the amount of gas phase ions produced in the electrospray process, depends on the properties of the compound. Here, we show that the amino acid composition also influences the ionization efficiency of the oligopeptide. Additionally, we show that the ionization efficiencies of oligopeptides consisting of more than five amino acid residues are very similar to one another, and assuming equal ionization efficiencies is feasible. Therefore, accurate ionization efficiency predictions are needed mainly for small oligopeptides. For these oligopeptides, the amino acid composition can be used to estimate the ionization efficiency in an easy to use manner.

A systematic approach toward comparing electrospray ionization efficiencies of derivatized and non-derivatized amino acids and biogenic amines

Ionization efficiency (IE) in mass spectrometry (MS) has been studied for many different compounds, and different IE scales have been constructed in order to quantitatively characterize IE. In the case of MS, derivatization has been used to increase the sensitivity of the method and to lower the limits of detection. However, the influence of derivatization on IE across different compounds and different derivatization reagents has not been thoroughly researched, so that practitioners do not have information on the IE-enhancing abilities of different derivatization reagents. Moreover, measuring IE via direct infusion of compounds cannot be considered fully adequate. Since derivatized compounds are in complex mixtures, a chromatographic method is needed to separate these compounds to minimize potential matrix effects. In this work, an IE measurement system with a chromatographic column was developed for mainly amino acids and some biogenic amines. IE measurements with liquid chromatography electrospray ionization mass spectrometry (LC/ESI/MS) were carried out, and IE scales were constructed with a calibration curve for compounds with and without derivatization reagent diethyl ethoxymethylenemalonate. Additionally, eluent composition effects on ionization were investigated. Results showed that derivatization increases IE for most of the compounds (by average 0.9 and up to 2-2.5 logIE units) and derivatized compounds have more similar logIE values than without derivatization. Mobile phase composition effects on ionization efficiencies were negligible. It was also noted that the use of chromatographic separation instead of flow injection mode slightly increases IE. In this work, for the first time, IE enhancement of derivatization reagents was quantified under real LC/ESI/MS conditions and obtained logIE values of derivatized compounds were linked with the existing scale.