Enhancing Accuracy in E&L with Machine Learning, Upcoming Webinar Hosted by Xtalks

In this free webinar, learn how mass spectrometry response factor variability causes errors in extractables and leachables (E&L) assessments. Attendees will discover how neural network models predict relative response factors for LC/MS and GC/MS to improve quantitation accuracy. The featured speakers will discuss the role of machine learning in accelerating the identification and assessment of complex mixtures and unknown compounds. Attendees will explore expert strategies for integrating chemical characterization into more efficient, regulatory-aligned analytical workflows.

TORONTO, April 1, 2026 /PRNewswire/ -- When it comes to ensuring the safety of medical and pharmaceutical products, chemical characterization plays a key role, particularly through the analysis of extractables and leachables (E&L). A persistent challenge in this work is the significant variability in how different compounds respond during mass spectrometry (MS) analysis, making accurate quantitation both difficult and error-prone. This webinar explores how machine learning can predict relative response factors to improve quantitation accuracy and accelerate decision-making in E&L assessments.

Differences in physicochemical properties often lead to inconsistent detector response across compounds in LC/MS positive and negative ion modes, as well as GC-MS/MS workflows. Traditional response-factor estimation often relies on extensive empirical testing, which can delay development timelines and reduce efficiency in complex mixture analysis.

This webinar will examine how neural network models developed from compounds spanning diverse chemical properties can improve response-factor prediction and strengthen analytical confidence. The featured speakers will discuss practical modelling approaches, real-world applications and considerations for integrating predictive tools into E&L workflows to support more consistent quantitation strategies and regulatory alignment.

Register for this webinar to learn how machine learning response factor prediction improves E&L quantitation accuracy and strengthens analytical decision-making.

Join experts from Jordi Labs, an RQM+ Company, Dr. Michael Louis, Scientific Director; and Adam Eason, Senior Biocompatibility Consultant, for the live webinar on Wednesday, April 22, 2026, at 9:30am EDT (3:30pm CEST/EU-Central).

For more information, or to register for this event, visit Enhancing Accuracy in E&L with Machine Learning.

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