Discovery analysis for GCxGC trends using alteration and 2D correlation (Chris Freye, MDCW 2026)

🎤 Presenter: Christopher Freye (Los Alamos National Laboratory)

💾 PDF presentation

Abstract

Two-dimensional gas chromatography (GC×GC) when coupled with mass spectrometry (MS) especially high-resolution mass spectrometry (HRMS) provides the ideal technique to discover minute chemical changes. However, GC×GC analyses result in extremely large datasets (GBs of information) wherein the statistically significant chemical changes are buried within the background chemical matrix. To combat these issues, many chemometric techniques have been developed or applied. Recently we introduced a new chemometric technique for GC-MS termed alteration analysis (ALA) and two-dimensional correlation (2DCOR) which can be used to discover statistically significant chemical changes across a series of chromatograms and understand the relationship between the statistically relevant changes. ALA generates three sets of information, the basic alteration map (BAM) which is the magnitude of the change, the synchronous alteration map (SAM) which describes the linear change, and the asynchronous alteration map (AAM) which describes the non-linear change. 2DCOR can then be used to understand the relationship between the chemical changes but 2DCOR can also resolve the sequence of the changes. A workflow for GC×GC-MS will be demonstrated. Special attention will be given to what differentiates the GC×GC work flow from GC for both the ALA and 2DCOR.

Video Transcription

The presentation focuses on how advanced mathematical and chemometric methods can be used to extract meaningful information from highly complex chromatographic data, especially in multidimensional chromatography coupled with mass spectrometry (GC×GC-MS, LC-MS).

Why traditional data analysis falls short

Modern multidimensional chromatography generates extremely large datasets, often containing billions of data points. Visual inspection, peak tables, or classical identification-based workflows are impractical—especially when:

• Samples are highly complex (e.g. petroleum, metabolomics, high explosives)
• Most compounds are unknown unknowns
• The key question is not what the compounds are, but whether samples differ and how

Program managers and decision-makers are primarily interested in detecting statistically significant changes, not exhaustive compound identification.

Introduction of Alteration Analysis (ALA)

To address subtle, trend-based changes across series of chromatograms, the speaker introduces Alteration Analysis (ALA), a chemometric approach adapted from spectroscopy and applied to chromatography.

ALA quantitatively evaluates how individual data points change across multiple samples, allowing detection of:

• Extremely subtle trends
• Overlapping peaks
• Changes hidden in noise or baseline interference

Key ALA outputs include:

• BAM (Basic Alteration Map): magnitude of change
• SAM (Systematic Alteration Map): linear changes
• AM (Alteration Map): nonlinear changes

Even changes as small as 1% per sample can be detected reliably, including peaks that are heavily overlapped.

Beyond detection: understanding relationships with 2D Correlation

Once statistically significant changes are identified, two-dimensional correlation analysis (2D-COR) is used to understand relationships between changing compounds, including:

• Whether compounds are correlated or anti-correlated
• The sequence of changes over time (which changes happen first)

This is especially critical for degradation and aging studies, where intermediate species may appear and disappear before the final state—information that traditional methods like Fisher ratio or PCA often miss.

Solving GC×GC challenges with tiling

GC×GC data introduces additional challenges due to:

• Peak misalignment
• Exponential data growth during correlation calculations

The presenter explains how tile-based approaches reduce data dimensionality, overcome alignment issues, and make ALA and 2D correlation computationally feasible—even for massive GC×GC-MS datasets.

Real-world application: explosive aging studies

The methodology was applied to aging and decomposition of high explosives, where:

• Over 250 chemical changes were identified
• Many intermediate species would have been missed using traditional comparisons
• ALA combined with 2D correlation enabled reconstruction of degradation pathways
• The most significant change was not necessarily the first change, a critical insight for risk assessment and stability studies

Key takeaway

By combining Alteration Analysis and 2D correlation, the approach:

• Detects statistically guaranteed chemical changes
• Handles subtle, overlapped, and noisy signals
• Determines magnitude, type, interrelationships, and order of changes
• Shifts chromatographic data analysis from identification-driven to decision-driven

This math-driven workflow enables faster, more reliable interpretation of complex chromatographic datasets and provides actionable insights for applications such as explosive aging, pharmaceutical degradation, and complex mixture analysis.

This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.