Quantitative structure–ion intensity relationship strategy to the prediction of absolute levels without authentic standards

Modeling the relative response factor of small molecules in positive electrospray ionization

Technological advancements in liquid chromatography (LC) electrospray ionization (ESI) high-resolution mass spectrometry (HRMS) have made it an increasingly popular analytical technique in non-targeted analysis (NTA) of environmental and biological samples. One critical limitation of current methods in NTA is the lack of available analytical standards for many of the compounds detected in biological and environmental samples. Computational approaches can provide estimates of concentrations by modeling the relative response factor of a compound (RRF) expressed as the peak area of a given peak divided by its concentration. In this paper, we explore the application of molecular dynamics (MD) in the development of a computational workflow for predicting RRF. We obtained measurements of RRF for 48 compounds with LC - quadrupole time-of-flight (QTOF) MS and calculated their RRF. We used the CGenFF force field to generate the topologies and GROMACS to conduct the (MD) simulations. We calculated the Lennard-Jones and Coulomb interactions between the analytes and all other molecules in the ESI droplet, which were then sampled to construct a multilinear regression model for predicting RRF using Monte Carlo simulations. The best performing model showed a coefficient of determination (R 2) of 0.82 and a mean absolute error (MAE) of 0.13 log units. This performance is comparable to other predictive models including machine learning models. While there is a need for further evaluation of diverse chemical structures, our approach showed promise in predictions of RRF.

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.

Discovery of Three Organothiophosphate Esters in River Water Using High-Resolution Mass Spectrometry

Records of the environmental occurrence of organothiophosphate esters (OTPEs), which are used as flame retardants and food and industrial additives, are unavailable. In this study, we discovered three OTPEs, namely O,O,O-tris(2,4-di-tert-butylphenyl) phosphorothioate (AO168═S), O-butyl O-(butyl-methylphenyl) O-(di-butylphenyl) phosphorothioate (BBMDBPt)/O,O-bis(dibutylphenyl) O-methyl phosphorothioate (BDBPMPt), and O-butyl O-ethyl O-hydrogen phosphorothioate (BEHPt), in the surface water of the Yangtze River Basin by applying a characteristic phosphorothioate fragment-directed high-resolution mass spectrometry method. Among the 17 water samples tested, the detection frequencies of AO168═S and BEHPt were 100% and that of BBMDBPt/BDBPMPt was 29%. The mean concentration of AO168═S was 56.9 ng/L (30.5-148 ng/L), and semi-quantitative analysis revealed that the mean concentrations of BEHPt and BBMDBPt/BDBPMPt were 17.2 ng/L (5.5-65.4 ng/L) and 0.8 ng/L (<the limit of quantification, LOQ, to 6.3 ng/L), respectively. Twelve organophosphate esters were also detected, of which the highest mean concentration was found for tris(2,4-di-tert-butylphenyl) phosphate (AO168═O, 366 ng/L), followed by triphenyl phosphate (84.3 ng/L), triethyl phosphate (19.3 ng/L), and tributyl phosphate (15.7 ng/L). The Spearman's correlation coefficient between AO168═S and AO168═O was 0.547 (p < 0.05), suggesting that AO168═S commonly transforms into AO168═O or that these chemicals have a similar source and behavior in the environment. Future studies are warranted to assess the potential toxicity, environmental behavior, and health risks posed by OTPEs.

Structural annotation, semi-quantification and toxicity prediction of pyrrolizidine alkaloids from functional food: In silico and molecular networking strategy

Many traditional Chinese herbs contain pyrrolizidine alkaloids (PAs), which have been reported to be toxic to livestock and humans. However, the lack of PAs standards makes it difficult to effectively conduct a risk assessment in the varied components of traditional Chinese medicine. It is necessary to propose a suitable strategy to obtain the representative occurrence data of PAs in complex systems. A comprehensive approach for annotating the structures, concentration, and mutagenicity of PAs in three Chinese herbs has been proposed in this article. First, feature-based molecular networking (FBMN) combined with network annotation propagation (NAP) on the Global Natural Products Social Molecular Networking web platform speeds up the process of annotating PAs found in Chinese herbs. Second, a semi-quantitative prediction model based on the quantitative structures and ionization intensity relationship (QSIIR) is used to forecast the amounts of PAs in complex substrates. Finally, the T.E.S.T. was used to provide predictions regarding the mutagenicity of annotated PAs. The goal of this study was to develop a strategy for combining the results of several computer models for PA screening to conduct a comprehensive analysis of PAs, which is a crucial step in risk assessment of unknown PAs in traditional Chinese herbal preparations.

Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data

Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.

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.

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.

Precursor ion approach for simultaneous determination of nonethoxylated and ethoxylated alkylsulfate surfactants

We present a new liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous determination of sodium lauryl sulfate and sodium laureth sulfate homologues in the range of alkyl chain length C₁₂-C₁₆ with 0-5 ethoxy groups. The method is based on scanning the precursor ions fragmenting to m/z 80 and 97 (Precursor Ion Scanning mode), which makes it specific for species with easily cleavable sulfate groups. By monitoring fragmentation of thus discovered quasi-molecular ions we were able to unequivocally identify all sulfate species present in complex mixtures of alkyl and alkyl-ether sulfates with molecular weight ranging from 200 to 600 m/z. Because of the intrinsic sulfate-sensitivity, the presented method can be also applied to non-sodium salts of alkyl- and alkyl-ether sulfates (e.g. ammonium, mono- or triethanolamine, etc.), which are often used by cosmetic manufacturers to justify the misleading SLS- and SLES-free claims (where SLS and SLES refer to sodium lauryl sulfate and sodium laureth sulfate, respectively). The use of reversed phase liquid chromatography (RPLC) column with C4 instead of C18 shortened significantly the overall analysis time and allowed us to use a semiquantitative method (based on single standard for Quantitative Analysis of Multi-component System, QAMS) to determine several SLS and SLES homologues in one run with the limit of quantification (LOQ) = 0.4 µg/mL and of detection (LOD) in the range 0.12-0.97 µg/mL. The method was successfully applied to 17 commercially available cosmetic/household products allowing verification of their manufacturers' declarations.

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.

Prediction of liquid chromatographic retention time using quantitative structure-retention relationships to assist non-targeted identification of unknown metabolites of phthalates in human urine with high-resolution mass spectrometry

The non-targeted analysis and identification of contaminant metabolites such as metabolites of phthalates and their alternatives in human biofluid samples constitutes a growing research field in human biomonitoring because of their importance as biomarkers of human exposure to the parent compounds. High-resolution mass spectrometry (HRMS) combined with high-performance liquid chromatography (HPLC) can provide fast separation and sensitive analysis using this application. However, the diversity of potential metabolites, especially isomers, in human samples, makes mass spectrometry-based structural identification very challenging, even with high-resolution and accurate mass. In this study, we present a retention time (t_R) prediction model based on quantitative structure-retention relationship (QSRR). This model can predict the retention time of a given structure of phthalates including isomers. Twenty-three molecular descriptors were used in the development of the multivariate linear regression QSRR model. The regression coefficient (R²) between predicted and experimental retention times of 26 training set compounds was 0.9912. The combination of the retention time prediction model with identification via accurate mass search and target MS/MS spectrum interpretation can enhance the identification confidence in the lack of reference standards. Two previously unreported phthalate metabolites were identified in human urine, using this model. The results of this study showed that the developed QSRR model could be a useful tool to predict the retention times of unknown metabolites of phthalates and their alternatives in future non-targeted screening analysis. The concentration of these two unknown compounds was also estimated using a quantitative structure-ion intensity relationship (QSIIR) model.

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.

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

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

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.

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.

Selectivity of solid phase extraction of freshwater dissolved organic matter and its effect on ultrahigh resolution mass spectra

Solid phase extraction (SPE) is often used for enrichment and clean-up prior to analysis of dissolved organic matter (DOM) by electrospray ionization (ESI) coupled to ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). It is generally accepted that extraction by SPE is not quantitative with respect to carbon concentration. However, little information is available on the selectivity of different SPE sorbents and the resulting effect for the acquired DOM mass spectra. Freshwater samples were extracted by the widely used PPL, HLB and C18 sorbents and the molecular composition and size distribution of the DOM in the extracts and in the permeates was compared to the original sample. Dissolved organic carbon (DOC) recoveries ranged between 20% and 65% for the three tested SPE sorbents. Size-exclusion chromatography coupled to organic carbon detection (SEC-OCD) revealed that limited recovery by PPL and HLB was primarily due to incomplete elution of a fraction of apparent high molecular weight from the solid phase. In contrast, incomplete retention on the solid phase, mainly observed for the C18 cartridge, was attributed to a fraction of low molecular weight. The FT-ICR mass spectra of the original sample and the SPE extracts did not differ significantly in their molecular weight distribution, but they showed sorbent specific differences in the degree of oxygenation and saturation. We concluded that the selective enrichment of freshwater DOM by SPE is less critical for subsequent FT-ICR MS analysis, because those fractions that are not sufficiently recovered have comparatively small effects on the mass spectra. This was confirmed by the extraction of model compounds, showing that very polar and small molecules are poorly extracted, but also have a low response in ESI-MS. Of the three tested SPE cartridges the PPL material offered the best properties for DOM enrichment for subsequent FT-ICR MS analysis as it minimizes too strong and too weak DOM-sorbent interactions.

Comprehensive Qualitative Ingredient Profiling of Chinese Herbal Formula Wu-Zhu-Yu Decoction via a Mass Defect and Fragment Filtering Approach Using High Resolution Mass Spectrometry

The Wu-Zhu-Yu decoction is a traditional Chinese medicine formula for the treatment of headache. To reveal its material basis, a rapid and reliable liquid chromatography-high resolution mass spectrometry method was established for comprehensive profiling of the chemical ingredients in the Wu-Zhu-Yu decoction. The method was used on a quadrupole time-of-flight mass spectrometer along with an advanced data processing procedure consisting of mass accuracy screening, mass defect filtering and fragment filtering. After eliminating interference with a filtering approach, the MS data profiling was made more distinct and accurate. With the optimized conditions of only 35 min LC separation and single sample injection of each positive or negative ion mode, a total of 168 compounds were characterized, including 23 evodiamine and its analogous alkaloids, 12 limonoids, 17 gingerols, 38 ginsenosides, 15 flavonoids, 16 organic acids, 14 alkaloids, 5 saponins, 3 2,2-dimethylchromenes and 25 other compounds. The fragmentation patterns of representative compounds were illustrated as well. Integrative qualitative analysis of the Wu-Zhu-Yu decoction by high resolution mass spectrometry was accomplished and reported for the first time. The study demonstrated that the established method was a powerful and reliable strategy for comprehensive detection and would be widely applicable for identification of complicated components from herbal prescriptions, and may provide a basis for chemical analysis of other complex mixtures.

Transferability of the electrospray ionization efficiency scale between different instruments

For the first time, quantitative electrospray (ESI) ionization efficiencies (IE), expressed as logIE values, obtained on different mass-spectrometric setups (four mass analyzers and four ESI sources) are compared for 15 compounds of diverse properties. The general trends of change of IE with molecular structure are the same with all experimental setups. The obtained IE scales could be applied on different setups: there were no statistically significant changes in the order of ionization efficiency and the root mean of squared differences of the logIE values of compounds between the scales compiled on different instruments were found to be between 0.21 and 0.55 log units. The results show that orthogonal ESI source geometry gives better differentiating power and additional pneumatic assistance improves it even more. It is also shown that the ionization efficiency values are transferable between different mass-spectrometric setups by three anchoring points and a linear model. The root mean square error of logIE prediction ranged from 0.24 to 0.72 depending on the instrument. This work demonstrates for the first time the inter-instrument transferability of quantitative electrospray ionization efficiency data. Graphical Abstract ᅟ.