When discovery metabolomics becomes quantitative

Traditional omics workflows often separate discovery and quantitation into distinct experiments. Researchers first perform untargeted analyses to identify molecules of interest, then develop targeted methods to accurately quantify those findings. But what if discovery data was already quantitative? 

In this webinar, Paul Baker will explore how ZT Scan DIA is helping bridge the gap between untargeted discovery and quantitative analysis. By improving precursor selectivity and reducing spectral interference, ZT Scan DIA enables more accurate MS/MS-level quantitation directly from untargeted datasets. Attendees will learn how retrospective quantitation can unlock additional value from discovery experiments, reducing the need for follow-up analyses while providing greater confidence in biological conclusions. Through real-world examples, discover how ZT Scan DIA is transforming discovery data into a long-term quantitative asset.

Join the free webinar with certification of attendance.

Attend the Webinar to Learn: 

• Why discovery and quantitation have traditionally required separate workflows.
• How ZT Scan DIA improves selectivity and supports accurate MS/MS quantitation.
• The impact of reduced interference on quantitative confidence.
• How retrospective quantitation can accelerate biological insights.
• How a single untargeted acquisition can support both discovery and quantitative analysis.

This Webinar is Ideal For:

• Life science/omics researchers
• Lab managers
• Professionals exploring metabolomics, lipidomics, or multi-omics in academia, clinical, and pharma laboratories.

Meet the Webinar Speaker

Paul Baker is a Senior Scientist at SCIEX with extensive experience in mass spectrometry workflow development and advanced acquisition strategies for omics research. His work focuses on improving both qualitative and quantitative performance through innovative technologies that enhance selectivity, identification confidence, and analytical robustness. Paul has played a key role in the development and application of next-generation DIA approaches, helping researchers overcome challenges associated with complex biological samples and enabling more confident, reproducible results across a broad range of omics disciplines.