Our poster presentations from conferences can be found here.
Standard substances free quantification makes LC/ESI/MS non-targeted screening of pesticides in cereals comparable between labs
TingtingWang, JaanusLiigand, Henrik LauritzFrandsen, JørnSmedsgaard, AnneliKruve
Food Chemistry 2020, 318
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.
Characterization of wines with liquid chromatography electrospray ionization mass spectrometry: quantification of amino acids via ionization efficiency values
Artur Gornischeff, AnneliKruve, Riin Rebane
Journal of Chromatography A 2020
Quantification of analysis results for the suspect and non-targeted screening is essential for obtaining meaningful insight from the measurements. Ionization efficiency predictions is a possible approach to enable quantitation without standard substances. This is, however, especially challenging for the analysis carried out by combining the full scan mode either with fragmentation experiments in data-dependent or data-independent acquisition mode.
Here we investigate the correlation of ionization efficiency values measured in full scan mode with the response factors measured in multiple reaction monitoring (MRM) mode for derivatized amino acids. We observe good correlation (R2 of 0.80) for 6-Aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) derivatized amino acids. This encourages the use of the measured ionization efficiency values to estimate amino acid concentrations in different beverages. We apply the measured ionization efficiency values for estimating the concentration of amino acids for measurements done both in full scan as well as in MRM mode in wines and beers. We show that the calculated concentrations are in very good correlation with measured values (R2 of 0.71 to 1.00). The method possesses average trueness of 70.5% and shows an insignificant matrix effect.
Strategies for Drawing Quantitative Conclusions from Nontargeted Liquid Chromatography−High-Resolution Mass Spectrometry Analysis
Analytical Chemistry 2020
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.
Quantification for non-targeted LC/MS screening without standard substances
Jaanus Liigand, Tingting Wang, Joshua Kellogg, Jørn Smedsgaard, Nadja Cech, Anneli Kruve
Scientific Reports 2020
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 (RF) 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 5.9 times, which is well compatible with the accuracy of the toxicology predictions.