Discovery-Based Analysis for Chemical Trends in Chromatographic Data Sets Using Alteration Analysis and Two-Dimensional Correlation Analysis

Abstract

Alteration analysis (ALA), an unsupervised chemometric technique, was evaluated for its ability to discover statistically significant trends in chromatographic data sets. Recently introduced, adoption of ALA has been limited due to uncertainty regarding its sensitivity to minor changes, and there are no rules implementing ALA especially for multivariate data sets such as liquid or gas chromatography coupled to mass spectrometry. Using in-silico data sets, ALA limits of discovery for various signal-to-noises (S/Ns), rates of change across samples, and a number of samples were assessed. For 10 samples, ALA discovered changes of ∼2% across each sample for low S/Ns (15–50), ∼1% change across each sample for moderate S/Ns (65–200), and as little as a 0.1% change at high S/Ns. ALA was also evaluated for unresolved chromatographic peaks, detecting changes down to a resolution of 0.01. In tandem with ALA, two-dimensional correlation analysis (2DCOR), a nonquantitative technique, was employed post-ALA processing to provide unique insights into the relationships between the chemical changes across simulated data sets. Finally, ALA and 2DCOR were applied to the pyrolysis gas chromatography–mass spectrometry (pyGC-MS) of Kraton G1650, a styrene-ethylene-butylene-stryrene (SEBS) polymer, pyrolyzed at temperatures ranging from 350 to 700 °C. A total of 523 statistically significant chemical compounds were discovered. The ALA output was fed into 2DCOR, and a subset of the data was evaluated to understand the relationship between the chemical changes of four selected statistically significant compounds.