Correction of Chromatography-Mass Spectrometry Long-term Instrumental Drift using Quality Control Samples over 155 days

Abstract Rigorous protocol for correction of long-term instrumental data drift is vital for ensuring process reliability and product stability. Over a span of 155 days, we performed 20 repeated tests in 7 batches on a set of six commercial tobacco products using chromatography-mass spectrometry instrument. By measuring pooled quality control (QC) samples for 20 times to establish correction algorithm data set, we reached reliable correction even for data with large fluctuation. Three algorithms-spline interpolation (SC), support vector regression (SVR), and Random Forest (RF)—were used to perform normalisation on 178 target substances in six samples. For chemical components present in the test samples but absent in the QC samples, normalisation was still achievable using either adjacent chromatography peak for correction or by applying the average correction coefficients derived from all QC data. Results show that Random Forest algorithm provides the most stable correction model for long-term, highly variable data. Both principal component analysis (PCA) and standard deviation analysis confirm satisfactory correction performance. In contrast, correction models based on the SC and SVR algorithms showed less stable correction outcomes. For data with large variation, SVR tends to over-fit and over-correct. Our study shows that for long-term data measurements by chromatography-mass spectrometry, periodic QC sample measurements combined with appropriate algorithm for correction can compensate measurement variability, thus enabling reliable data tracking and quantitative comparison over extended periods.