PARAFAC2×N: Coupled decomposition of multi-modal data with drift in N modes

Area of Science:

• Analytical Chemistry
• Chemometrics
• Chromatography

Background:

• Analyzing large datasets from comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) is challenging due to poorly resolved peaks and numerous samples.
• Chromatographic drift in both modulation and mass spectral acquisition dimensions complicates data analysis.

Purpose of the Study:

• To develop a novel approach for modeling GC×GC-TOFMS data with drift along multiple dimensions.
• To enhance the application of multidimensional chromatography with multivariate detection.

Main Methods:

• A new general theory and model were developed to handle data with drift in multiple modes.
• The proposed model was applied to GC×GC-TOFMS data, which can be represented as a 4th order tensor.
• Comparison with existing methods like Multivariate Curve Resolution (MCR) and Parallel Factor Analysis 2 (PARAFAC2) was implicitly considered.

Main Results:

• The new model successfully captures over 99.9% of the variance in a synthetic dataset.
• Demonstrated effective modeling of extreme peak drift and co-elution across two separation modes.
• Provides a robust framework for analyzing complex multidimensional chromatography data.

Conclusions:

• The developed multi-modal drift modeling approach offers a significant advancement for GC×GC-TOFMS data analysis.
• This method overcomes limitations of existing techniques in handling complex chromatographic variations.
• Facilitates wider adoption and more reliable interpretation of GC×GC-TOFMS data.