We have now gathered our knowledge from years of ESI mechanism research and bundled it with data science to provide you with the possibility to quantify analytes in LC/ESI/MS analysis without the use of standard substances.
The availability of standard substances is one of the main limitations in LC/ESI/MS analysis, especially in the fields of metabolomics, environmental analysis as well as illegal substance monitoring. If no commercial standards are available, the only choices so far have been to either (1) synthesise them in-house which is very expensive and time-consuming or (2) use other compounds for quantification and ignore the possibility of vastly different response factors The latter choice could lead to errors up to 10 milion times. We have developed a third, overwhelmingly faster, cost-effective and accurate option.
The developed solution has been launched by Quantem Analytics and aims at providing standard-substance-free quantification solutions for LC/ESI/MS analysis. We combine the fundamental research in the field of mass spectrometry with data science to provide the first solution to situations where there simply are no standard substances available for quantification. Quantem uses machine learning to predict response factors of analytes taking into account the eluent composition at the retention time and instrument you are using as well as the matrix. This novel approach is applicable to:
- Numerous types of analytes with logP from -10 to +10 and molar mass below 1500 Daltons;
- Different matrices, e.g. biological samples (urine, plasma, etc), food samples (cereal, etc), plant-based materials, etc.;
- All common eluent compositions, both in terms of organic modifiers and additives;
- Both positive and negative mode ESI;
- Gradient elution, including different flow rates;
This, in turn, opens various new possibilities:
- Switching to an approach where your quantification is not limited by the availability of standard substances but rather your ability to identify the peaks;
- Quantification of more than 1000 peaks within 24 h;
- Retrospective analysis. Quantification of analysis data acquired even years ago;
- Direct comparison between standard-substance-free analysis results obtained on different instruments and even in different labs opening the door for large scale collaboration in the field of quantitative non-target analysis;
The accuracy of the Quantem predictions is high, the average error is below 5 times, i.e. if the method predicts a concentration of 1 ppm the true values is probably in the range of 0.2 – 5 ppm. In the vast majority of cases, this is sufficient input for making data-driven decisions.
If you have any further questions you can contact Quantem through https://quantem.co