For years, researchers and analysts have relied on targeted analysis methods. In spite of high accuracy of the obtained results, this has lead to overlooking a number of pharmaceutical byproducts, important metabolites, and environmental contaminants. The suspect and non-targeted analysis with LC/HRMS have come to change this. The non-targeted analysis allows detecting hundreds or thousands of compounds without preselection of the analytes.

The most significant obstacles for non-targeted LC/HRMS screening today are the scarcely defined scope of the method and, more importantly, the inability to provide quantitative information. This is well shown by the data. Currently, the Human Metabolome Database contains 114 100 compounds and only 3383 (<3%) of these have been both detected and quantified. Due to the intensive developments in the field of compound identification tools, the proportion of compounds that can be identified with non-targeted methods is constantly increasing. The inability to quantify these compounds is a problem not only in metabolomics, but also in pharmaceutical analysis, environmental analysis, and even in monitoring illegal substances.

The inability to provide quantitative data for non-targeted screening with LC/ESI/HRMS originates from the vastly different ionization efficiencies of different compounds in ESI source. Overall, the ionization efficiencies of the compounds detectable with LC/ESI/MS vary up to 100 million times. For example, even relatively similar compounds like the positional isomers 2- and 4-nitrophenol yield a 40 times different response at equal concentrations. Additionally, LC conditions also affect the ionization efficiencies.

If no commercial analytical standards are available, the only choices so far have been to either (1) synthesise the standards 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 million times. Quantem provides a third, overwhelmingly faster, cost-effective and accurate option.

Quantem Model combines the fundamental research in the field of mass spectrometry with data science to provide the first solution to situations where there simply are no analytical standards 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.

With Quantem you can switch to an approach where your quantification is not limited by the availability of analytical standards.

What is more:

• Quantification is fast – quantify around 10 000 peaks in 24h;
• Quantification is cost-effective – you won’t need to buy expensive analytical standards or spend time and money on synthesising them;
• Quantification is accurate – average quantification error is less than 5 times;
• Quantification can be done retrospectively – you can easily quantify your 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 concentration can be estimated even if you have multiple candidate structures.
• Quantem approach can be implemented immediately without any further modifications to your current analysis practice and is easy to use.