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Discovery-Based Analysis for Chemical Trends in Chromatographic Data Sets Using Alteration Analysis and
Matthew J Herman1, Chris E Freye1
1Los Alamos National Laboratory, Q-5, High Explosives Science and Technology, Los Alamos, New Mexico 87545, United States.
Analytical Chemistry
|January 11, 2025
Summary
Alteration analysis (ALA) effectively detects subtle chemical changes in complex data, even with low signal-to-noise ratios. This chemometric technique, combined with 2D correlation analysis, reveals significant trends in chromatographic data and polymer analysis.
Area of Science:
- Chemometrics
- Analytical Chemistry
- Polymer Science
Background:
- Alteration analysis (ALA) is an unsupervised chemometric technique for trend discovery in chromatographic data.
- Limited adoption of ALA stems from uncertainty about its sensitivity to minor changes and lack of established rules for multivariate data.
- Multivariate data sets, such as those from liquid or gas chromatography coupled to mass spectrometry, require robust analytical methods.
Purpose of the Study:
- To evaluate the sensitivity and discovery limits of Alteration Analysis (ALA) for chromatographic data.
- To assess ALA's performance across various signal-to-noise ratios (S/Ns), rates of change, and sample sizes using in-silico data.
- To explore the combined application of ALA and two-dimensional correlation analysis (2DCOR) for enhanced data interpretation.
Main Methods:
- In-silico datasets were used to assess ALA's detection limits for varying S/Ns, rates of change, and sample numbers.
- ALA was evaluated for its ability to detect changes in unresolved chromatographic peaks.
- Two-dimensional correlation analysis (2DCOR) was applied post-ALA to investigate relationships between chemical changes in simulated datasets and a real polymer sample (Kraton G1650).
Main Results:
- ALA successfully detected minor changes (down to 0.1%) across samples, even at low signal-to-noise ratios (S/Ns) and with unresolved chromatographic peaks (resolution of 0.01).
- Application to pyrolysis gas chromatography-mass spectrometry (pyGC-MS) of Kraton G1650 identified 523 statistically significant compounds.
- Combined ALA and 2DCOR analysis provided insights into the relationships between chemical changes of selected compounds during polymer pyrolysis.
Conclusions:
- Alteration analysis (ALA) is a sensitive and effective method for discovering statistically significant trends in complex chromatographic data.
- The combination of ALA with 2DCOR enhances the interpretation of chemical changes in multivariate datasets.
- ALA and 2DCOR are valuable tools for analyzing complex samples like polymers using techniques such as pyGC-MS.
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