Machine Learning-Based System for Matrix Effect Correction in Gas Chromatography

Abstract

Gas chromatography of multicomponent gaseous mixtures suffers from the matrix effect, causing significant measurement errors in methanation processes. To address this challenge, this study evaluates various predictive models designed to determine component concentrations from chromatographic intensities across three different chromatographs (A, B and C).