Ionization efficiency (IE) of different compounds in electrospray ionization (ESI) source differs widely, leading to widely differing sensitivities of ESI-MS to different analytes. An approach for quantifying ESI efficiencies (as logIE values) and setting up a self-consistent quantitative experimental ESI efficiency scale of organic compounds under predefined ionization conditions (ionization by monoprotonation) has been developed recently. Using this approach a logIE scale containing 62 compounds of different chemical nature and ranging for 6 orders of magnitude has been established. The scale is based on over 400 relative IE (DeltalogIE) measurements between more than 250 different pairs of compounds. To evaluate which molecular parameters contribute the most to the IE of a compound linear regression analysis logIE values and different molecular parameters were carried out. The two most influential parameters in predicting the IE in ESI source are the pK(a) and the molecular volume of the compound. This scale and the whole approach can be a tool for practicing liquid chromatographists and mass spectrometrists. It can be used in any mass-spectrometry laboratory and we encourage practitioners to characterize their analytes with the logIE values so that a broad knowledge base on electrospray ionization efficiencies of compounds would eventually develop.

Electrospray ionization (ESI) in the negative ion mode has received less attention in fundamental studies than the positive ion electrospray ionization. In this paper, we study the efficiency of negative ion formation in the ESI source via deprotonation of substituted phenols and benzoic acids and explore correlations of the obtained ionization efficiency values (logIE) with different molecular properties. It is observed that stronger acids (i.e., fully deprotonated in the droplets) yielding anions with highly delocalized charge [quantified by the weighted average positive sigma (WAPS) parameter rooted in the COSMO theory] have higher ionization efficiency and give higher signals in the negative-ion ESI/MS. A linear model was obtained, which equally well describes the logIE of both phenols and benzoic acids (R(2) = 0.83, S = 0.40 log units) and contains only an ionization degree in solution (α) and WAPS as molecular parameters. Both parameters can easily be calculated with the COSMO-RS method. The model was successfully validated using a test set of acids belonging neither to phenols nor to benzoic acids, thereby demonstrating its broad applicability and the universality of the above-described relationships between IE and molecular properties.

No abstract

This is the part I of a tutorial review intending to give an overview of the state of the art of method validation in liquid chromatography mass spectrometry (LC-MS) and discuss specific issues that arise with MS (and MS/MS) detection in LC (as opposed to the "conventional" detectors). The Part I briefly introduces the principles of operation of LC-MS (emphasizing the aspects important from the validation point of view, in particular the ionization process and ionization suppression/enhancement); reviews the main validation guideline documents and discusses in detail the following performance parameters: selectivity/specificity/identity, ruggedness/robustness, limit of detection, limit of quantification, decision limit and detection capability. With every method performance characteristic its essence and terminology are addressed, the current status of treating it is reviewed and recommendations are given, how to determine it, specifically in the case of LC-MS methods.

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.