Structure–response relationship in electrospray ionization-mass spectrometry of sartans by artificial neural networks

Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.

μPESI-MS/MS System for Screening and Quantitating Drugs in Plasma Samples

Recently, we developed a novel microprobe electrospray ionization (μPESI) source and its coupled MS (μPESI-MS/MS) system. Here, we aimed to widely validate the μPESI-MS/MS method for quantitative analysis of drugs in plasma samples. Furthermore, the relationship between the quantitative performance of the μPESI-MS/MS method and the physicochemical properties of target drugs was analyzed. The μPESI-MS/MS methods for quantitative analysis of 5 representative drugs with a relatively wide range of molecular weight, pK_a, and log P values were developed and validated. The results showed that the linearity, accuracy, and precision of these methods met the requirements of the European Medicines Agency (EMA) guidance. Then a total of 75 drugs from plasma samples were primarily detected using the μPESI-MS/MS methods, among which 48 drugs could be quantitatively measured. Logistics regression suggested that drugs with significantly greater log P and physiological charge had better quantitative performance using the μPESI-MS/MS method. Collectively, these results clearly demonstrate the practical application of the μPESI-MS/MS system as a rapid approach to the quantitative analysis of drugs in plasma samples.

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.

Predicting Preferences for Adduct Formation in Electrospray Ionization: The Case Study of Succinic Acid

A simple theoretical approach is developed that can be used to predict the preference of ion adduct formation (with alkali Li⁺, Na⁺, K⁺ and alkaline earth Ca²⁺, Mg²⁺ metals) in electrospray ionization mass spectrometry (ESI-MS) of succinic acid, associated with several protonation/deprotonation equilibria. The applied strategy consists of using a vacuum environment as well as both implicit and explicit solvation of reactive sites and density functional theory as the method of choice. These distinct levels of theory mimic the smooth transition between the condensed environment and free ion in the gas phase. Good correlation between the Gibbs free energies for protonation/adduct formation processes with peak observation in the obtained mass spectra provide insight into the physical basis behind adduct preference and selectivity. This signifies the relationship between microscopic interactions, ionization efficiency, and types of ions that reach the detector.

Matrix Effects in GC–MS Profiling of Common Metabolites after Trimethylsilyl Derivatization

Metabolite profiling using gas chromatography coupled to mass spectrometry (GC–MS) is one of the most frequently applied and standardized methods in research projects using metabolomics to analyze complex samples. However, more than 20 years after the introduction of non-targeted approaches using GC–MS, there are still unsolved challenges to accurate quantification in such investigations. One particularly difficult aspect in this respect is the occurrence of sample-dependent matrix effects. In this project, we used model compound mixtures of different compositions to simplify the study of the complex interactions between common constituents of biological samples in more detail and subjected those to a frequently applied derivatization protocol for GC–MS analysis, namely trimethylsilylation. We found matrix effects as signal suppression and enhancement of carbohydrates and organic acids not to exceed a factor of ~2, while amino acids can be more affected. Our results suggest that the main reason for our observations may be an incomplete transfer of carbohydrate and organic acid derivatives during the injection process and compound interaction at the start of the separation process. The observed effects were reduced at higher target compound concentrations and by using a more suitable injection-liner geometry.

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.

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.

Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization

Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.

Absolute Quantification of Acylcarnitines Using Integrated Tmt-PP Derivatization-Based LC-MS/MS and Quantitative Analysis of Multi-Components by a Single Marker Strategy

Acylcarnitines (ACs) play important roles in the fatty acid β-oxidation and are considered as diagnostic markers for many diseases. Accurate determination of ACs remains challenging due to their low abundance, high structure diversity, and limited availability of standard compounds. In this study, microwave-assisted Tmt-PP (p-[3,5-(dimethylamino)-2,4,6-triazine] benzene-1-sulfonyl piperazine) derivatization was utilized to facilitate the liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) determination of ACs. The result indicated that Tmt-PP labeling enables the prediction of the retention time and MS response of ACs and enhances their MS response up to 4 times. The introduction of the microwave during the derivatization procedure greatly improved the reaction efficiency, demonstrated by the shortened reaction time from 90 to 1 min. Furthermore, we applied a strategy named quantitative analysis of multi-components by a single marker (QAMS) for the assay of 26 ACs with only 5 AC standards, solving the standard availability issue to a large extent. The established workflow was applied to discover dysregulated ACs in xenograft colon cancer mice, and the quantification results were highly comparable with traditional methods where there were the corresponding standards for each AC. Our study demonstrated that chemical derivatization-based LC-MS/MS integrated with the QAMS strategy is robust for the identification and quantification of ACs and has great potential in targeted metabolomics study.

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.

Performance of the organic-diffusive gradients in thin-films passive sampler for measurement of target and suspect wastewater contaminants

Although passive sampling is widely accepted as an excellent tool for environmental monitoring, their integration with suspect or non-targeted screening by high-resolution mass spectrometry has been limited. This study describes the application of the organic-diffusive gradients in thin-films (o-DGT) passive sampler as a tool for accurate measurement of both targeted and suspect polar organic contaminants (primarily pharmaceuticals) in wastewater. First, performance of o-DGT was assessed alongside the polar organic chemical integrative sampler (POCIS) and active sampling at two wastewater treatment facilities using targeted analyses. Overall, water concentrations measured by o-DGT, POCIS, and 24-hr integrative active samples were in good agreement with each other. There were exceptions, including a systematic difference between o-DGT and POCIS at certain sites that we propose was a result of site-specific conditions and a difference in sampling rates between the two techniques. The second component of this work involved suspect screening of the o-DGT extracts using high-resolution, high mass accuracy quadrupole time-of-flight mass spectrometry (QTOF). Lamotrigine, venlafaxine, and des-methylvenlafaxine were three suspect compounds identified and selected as proof-of-concept case studies to determine the feasibility and accuracy of o-DGT for estimating water concentrations based upon predicted sampling rates using a previously validated o-DGT diffusion model. Semi-quantification of the suspect compounds was conducting using an average surrogate response factor based on the suite of compounds measured by the targeted analyses. This, combined with the modelled sampling rates provided time-weighted average wastewater concentrations of the identified suspects within a factor of 2 of the true value, confirmed by isotope dilution with mass labelled internal surrogates. To the knowledge of the authors, this work is the first to demonstrate the utility of the o-DGT passive sampler as a potential environmental screening tool that can be integrated into the rapidly advancing field of non-targeted high resolution mass spectrometry.

Signal Drift in Liquid Chromatography Tandem Mass Spectrometry and Its Internal Standard Calibration Strategy for Quantitative Analysis

The present project studied the signal drift in liquid chromatography tandem mass spectrometry (LC-MS/MS) and proposed a strategy for compensating such drift. In the study, four 4-component groups were repeatedly run on different LC-MS/MS systems for over 12 h to investigate the dependence of signal drift on time and hardware systems. The 4-component groups each consisted of (1) an analyte, (2) a stable isotope labeled analyte, (3) a compound with similar structure to the analyte, and (4) a compound with dissimilar structure. All of the species showed significant signal drift, generally more than 25% over 12 h. The analyte and its stable isotope labeled analog always have the same drifting pattern including the trends and direction from one LC-MS/MS system to another. Signal drift was also found to be concentration dependent. Our experiments further proved that a conventional stable isotope labeled internal standard in LC-MS/MS quantification would not compensate the variations caused by concentration-dependent signal drift. An ideal internal standard for LC-MS/MS has both identical structure and similar concentration to the analyte. For that, we proposed a new internal standard strategy, pseudo internal standard (Pseudo IS), for LC-MS/MS quantification. Pseudo IS could effectively compensate signal drift in spite of its significant time, system, and concentration dependencies.

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.

Ionization efficiency ladders as tools for choosing ionization mode and solvent in liquid chromatography/mass spectrometry

RATIONALE

The choice of mobile phase components and optimal ion source, mainly electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI), is a crucial part in liquid chromatography/mass spectrometry (LC/MS) method development to achieve higher sensitivity and lower detection limits. In this study we demonstrate how to rigorously solve these questions by using ionization efficiency scales.

METHODS

Four ionization efficiency scales are used: recorded with both APCI and ESI sources and using both methanol- and acetonitrile-containing mobile phases. Each scale contains altogether more than 50 compounds. In addition, measurements with a chromatographic column were also performed.

RESULTS

We observed a correlation between calibration graph slopes under LC conditions and logIE values in ESI (but not APCI) thereby validating the use of logIE values for choosing the ion source. Most of the studied compounds preferred ESI as an ion source and methanol as mobile organic phase. APCI remains the ion source of choice for polycyclic aromatic hydrocarbons. For APCI, both acetonitrile and methanol provide similar ionization efficiencies with few exceptions.

CONCLUSIONS

Overall the results of this work give a concise guideline for practitioners in choosing an ion source for LC/MS analysis on the basis of the chemical nature of the analytes.

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.

Pharmacognosy in the digital era: shifting to contextualized metabolomics

Humans have co-evolved alongside numerous other organisms, some having a profound effect on health and nutrition. As the earliest pharmaceutical subject, pharmacognosy has evolved into a meta-discipline devoted to natural biomedical agents and their functional properties. While the acquisition of expanding data volumes is ongoing, contextualization is lagging. Thus, we assert that the establishment of an integrated and open databases ecosystem will nurture the discipline. After proposing an epistemological framework of knowledge acquisition in pharmacognosy, this study focuses on recent computational and analytical approaches. It then elaborates on the flux of research data, where good practices could foster the implementation of more integrated systems, which will in turn help shaping the future of pharmacognosy and determine its constitutional societal relevance.

The requirements for low-temperature plasma ionization support miniaturization of the ion source

Ambient ionization mass spectrometry (AI-MS), the ionization of samples under ambient conditions, enables fast and simple analysis of samples without or with little sample preparation. Due to their simple construction and low resource consumption, plasma-based ionization methods in particular are considered ideal for use in mobile analytical devices. However, systematic investigations that have attempted to identify the optimal configuration of a plasma source to achieve the sensitive detection of target molecules are still rare. We therefore used a low-temperature plasma ionization (LTPI) source based on dielectric barrier discharge with helium employed as the process gas to identify the factors that most strongly influence the signal intensity in the mass spectrometry of species formed by plasma ionization. In this study, we investigated several construction-related parameters of the plasma source and found that a low wall thickness of the dielectric, a small outlet spacing, and a short distance between the plasma source and the MS inlet are needed to achieve optimal signal intensity with a process-gas flow rate of as little as 10 mL/min. In conclusion, this type of ion source is especially well suited for downscaling, which is usually required in mobile devices. Our results provide valuable insights into the LTPI mechanism; they reveal the potential to further improve its implementation and standardization for mobile mass spectrometry as well as our understanding of the requirements and selectivity of this technique. Graphical abstract Optimized parameters of a dielectric barrier discharge plasma for ionization in mass spectrometry. The electrode size, shape, and arrangement, the thickness of the dielectric, and distances between the plasma source, sample, and MS inlet are marked in red. The process gas (helium) flow is shown in black.

Development of quantitative screen for 1550 chemicals with GC-MS

With hundreds of thousands of chemicals in the environment, effective monitoring requires high-throughput analytical techniques. This paper presents a quantitative screening method for 1550 chemicals based on statistical modeling of responses with identification and integration performed using deconvolution reporting software. The method was evaluated with representative environmental samples. We tested biological extracts, low-density polyethylene, and silicone passive sampling devices spiked with known concentrations of 196 representative chemicals. A multiple linear regression (R² = 0.80) was developed with molecular weight, logP, polar surface area, and fractional ion abundance to predict chemical responses within a factor of 2.5. Linearity beyond the calibration had R² > 0.97 for three orders of magnitude. Median limits of quantitation were estimated to be 201 pg/μL (1.9× standard deviation). The number of detected chemicals and the accuracy of quantitation were similar for environmental samples and standard solutions. To our knowledge, this is the most precise method for the largest number of semi-volatile organic chemicals lacking authentic standards. Accessible instrumentation and software make this method cost effective in quantifying a large, customizable list of chemicals. When paired with silicone wristband passive samplers, this quantitative screen will be very useful for epidemiology where binning of concentrations is common.

Graphical abstract

Physicochemical Parameters Affecting the Electrospray Ionization Efficiency of Amino Acids after Acylation

Electrospray ionization (ESI) is widely used in liquid chromatography coupled to mass spectrometry (LC–MS) for the analysis of biomolecules. However, the ESI process is still not completely understood, and it is often a matter of trial and error to enhance ESI efficiency and, hence, the response of a given set of compounds. In this work we performed a systematic study of the ESI response of 14 amino acids that were acylated with organic acid anhydrides of increasing chain length and with poly(ethylene glycol) (PEG) changing certain physicochemical properties in a predictable manner. By comparing the ESI response of 70 derivatives, we found that there was a strong correlation between the calculated molecular volume and the ESI response, while correlation with hydrophobicity (log P values), pK_a, and the inverse calculated surface tension was significantly lower although still present, especially for individual derivatized amino acids with increasing acyl chain lengths. Acylation with PEG containing five ethylene glycol units led to the largest gain in ESI response. This response was maximal independent of the calculated physicochemical properties or the type of amino acid. Since no actual physicochemical data is available for most derivatized compounds, the responses were also used as input for a quantitative structure–property relationship (QSPR) model to find the best physicochemical descriptors relating to the ESI response from molecular structures using the amino acids and their derivatives as a reference set. A topological descriptor related to molecular size (SPAN) was isolated next to a descriptor related to the atomic composition and structural groups (BIC0). The validity of the model was checked with a test set of 43 additional compounds that were unrelated to amino acids. While prediction was generally good (R² > 0.9), compounds containing halogen atoms or nitro groups gave a lower predicted ESI response.

Predicting ESI/MS Signal Change for Anions in Different Solvents

LC/ESI/MS is a technique widely used for qualitative and quantitative analysis in various fields. However, quantification is currently possible only for compounds for which the standard substances are available, as the ionization efficiency of different compounds in ESI source differs by orders of magnitude. In this paper we present an approach for quantitative LC/ESI/MS analysis without standard substances. This approach relies on accurately predicting the ionization efficiencies in ESI source based on a model, which uses physicochemical parameters of analytes. Furthermore, the model has been made transferable between different mobile phases and instrument setups by using a suitable set of calibration compounds. This approach has been validated both in flow injection and chromatographic mode with gradient elution.

pH Effects on Electrospray Ionization Efficiency

Electrospray ionization efficiency is known to be affected by mobile phase composition. In this paper, a detailed study of analyte ionization efficiency dependence on mobile phase pH is presented. The pH effect was studied on 28 compounds with different chemical properties. Neither pK _a nor solution phase ionization degree by itself was observed to be sufficient at describing how aqueous phase pH affects the ionization efficiency of the analyte. Therefore, the analyte behavior was related to various physicochemical properties via linear discriminant analyses. Distinction between pH-dependent and pH-independent compounds was achieved using two parameters: number of potential charge centers and hydrogen bonding acceptor capacity (in the case of 80% acetonitrile) or polarity of neutral form of analyte and pK _a (in the case of 20% acetonitrile). It was also observed that decreasing pH may increase ionization efficiency of a compound by more than two orders of magnitude. Graphical Abstract ᅟ.

Electrospray Ionization Efficiency Is Dependent on Different Molecular Descriptors with Respect to Solvent pH and Instrumental Configuration

Over the past decades, electrospray ionization for mass spectrometry (ESI-MS) has become one of the most commonly employed techniques in analytical chemistry, mainly due to its broad applicability to polar and semipolar compounds and the superior selectivity which is achieved in combination with high resolution separation techniques. However, responsiveness of an analytical method also determines its suitability for the quantitation of chemical compounds; and in electrospray ionization for mass spectrometry, it can vary significantly among different analytes with identical solution concentrations. Therefore, we investigated the ESI-response behavior of 56 nitrogen-containing compounds including aromatic amines and pyridines, two compound classes of high importance to both, synthetic organic chemistry as well as to pharmaceutical sciences. These compounds are increasingly analyzed employing ESI mass spectrometry detection due to their polar, basic character. Signal intensities of the peaks from the protonated molecular ion (MH⁺) were acquired under different conditions and related to compound properties such as basicity, polarity, volatility and molecular size exploring their quantitative impact on ionization efficiency. As a result, we found that though solution basicity of a compound is the main factor initially determining the ESI response of the protonated molecular ion, other factors such as polarity and vaporability become more important under acidic solvent conditions and may nearly outweigh the importance of basicity under these conditions. Moreover, we show that different molecular descriptors may become important when using different types of instruments for such investigations, a fact not detailed so far in the available literature.