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

Abstract

The lack of authentic standards represents a major bottleneck in the quantitative analysis of complex samples. Here we propose a quantitative structure and ionization intensity relationship (QSIIR) approach to predict the absolute levels of compounds in complex matrixes. An absolute quantitative method for simultaneous quantification of 25 organic acids was firstly developed and validated. Napierian logarithm (LN) of the relative slope rate derived from the calibration curves was applied as an indicator of the relative ionization intensity factor (RIIF) and serves as the dependent variable for building a QSIIR model via a multiple linear regression (MLR) approach. Five independent variables representing for hydrogen bond acidity, HOMO energy, the number of hydrogen bond donating group, the ratio of organic phase, and the polar solvent accessible surface area were found as the dominant contributors to the RIIF of organic acids. This QSIIR model was validated to be accurate and robust, with the correlation coefficients (R(2)), R(2) adjusted, and R(2) prediction at 0.945, 0.925, and 0.89, respectively. The deviation of accuracy between the predicted and experimental value in analyzing a real complex sample was less than 20% in most cases (15/18). Furthermore, the high adaptability of this model was validated one year later in another LC/MS system. The QSIIR approach is expected to provide better understanding of quantitative structure and ionization efficiency relationship of analogous compounds, and also to be useful in predicting the absolute levels of analogous analytes in complex mixtures.

Keywords: CE; ESI; GFA; HOMO; HPLC; Ionization intensity; LN; Liquid chromatography quadrupole time-of-flight mass spectrometers; Liquid chromatography triple quadrupole mass spectrometers; MLR; MRM; MS; Napierian logarithm; Organic acids; Q1 MI; Q1 multiple ions; QSIIR; QSRR; Quantitative structure and ionization intensity relationship; RIIF; RMS; TIC; capillary electrophoresis; electrospray ionization; genetic function approximation; high performance liquid chromatography; highest occupied molecular orbital; mass spectrometry; multiple linear regression; multiple reactions monitor; quantitative structure retention relationship; quantitative structure–ion intensity relationship; relative ionization intensity factor; residual mean square; total ion chromatogram.