Correction of gas chromatography-mass spectrometry long-term instrumental drift using quality control samples over 155 days.

ABSTRACT: Long-term instrumental data drift is a critical challenge for ensuring process reliability and product stability. In this study, we conducted 20 repeated tests on the smoke of six commercial tobacco products using gas chromatography-mass spectrometry (GC-MS) instrument over 155 days. We propose a simple, cost-effective, and reliable peak-area correction approach to address long-term data drift, especially on GC-MS data. Using 20 pooled quality control (QC) samples, we establish correction algorithm data set, and achieved reliable peak correction even for compositions exhibiting large fluctuations. Three algorithms - spline interpolation (SC), support vector regression (SVR), and Random Forest (RF) - were applied to normalise 178 target chemicals in 20 repeated measurements on six samples.

SUBMITTER: Yu J 

PROVIDER: S-EPMC12630760 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature