Explainable Machine Learning Framework for Predicting Retention Time Shifts in Biodiesel Gas Chromatography

Montassar bouzidi

Keywords: Gas Chromatography, Retention Time Shifts, Explainable Machine Learning, Biodiesel Quality.

TL;DR: This paper introduces an explainable machine learning approach to accurately predict and interpret retention time shifts in biodiesel gas chromatography.

Confirmation Of Submission Requirements: I submit an abstract. It uses the template provided on the submission page and is no longer than 2 pages.

Submission Number: 184