In this free webinar, learn how variations in mass spectrometry response factors introduce errors in extractables and leachables (E&L) assessments. Participants will discover how neural network models can predict relative response factors for LC/MS and GC/MS to improve quantitative accuracy. Featured speakers will discuss the role of machine learning in accelerating the identification and evaluation of complex mixtures and unknown compounds. Participants will explore expert strategies for integrating chemical characterization into more efficient and compliant analytical workflows.
Toronto, April 1, 2026 /PRNewswire/ — When it comes to ensuring the safety of medical products and drugs, chemical characterization plays a key role, especially through extractables and leachables (E&L) analysis. A persistent challenge in this research is that the way different compounds react during mass spectrometry (MS) analysis varies widely, making accurate quantification difficult and error-prone. This webinar explores how machine learning can predict relative response factors to improve quantitative accuracy and speed decision-making in E&L evaluations.
www.rqmplus.com
Due to differences in physicochemical properties, detector responses are often inconsistent between compounds in LC/MS positive and negative ion modes and GC-MS/MS workflows. Traditional response factor estimation often relies on extensive empirical testing, which can delay development schedules and reduce the efficiency of complex mixture analysis.
This webinar examines how neural network models developed from compounds across a variety of chemical properties can improve prediction of response factors and enhance confidence in analyses. Featured speakers will discuss practical modeling approaches, real-world applications, and considerations for integrating predictive tools into E&L workflows to support more consistent quantitative strategies and regulatory alignment.
Register for this webinar to learn how machine learning response factor prediction improves E&L quantitative accuracy and enhances analytical decision making.
Join Dr. Michael Louis, expert scientific director at Jordi Labs, an RQM+ company. and Adam Eason, Senior Biocompatibility Consultant, will be on stage for a live webinar on Wednesday, April 22, 2026, at 9:30 a.m. ET (3:30 p.m. CET).
For more information or to register for this event, see Enhancing Accuracy in E&L with Machine Learning.
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