Unlocking new capabilities in the analysis of GC$\times$GC-TOFMS data with shift-invariant multi-linearity

TL;DR

This paper presents SIML, a new deconvolution algorithm for GC×GC-TOFMS data that improves analysis robustness against retention time shifts and noise, outperforming traditional methods.

Contribution

The paper introduces SIML, a novel shift-invariant multi-linearity algorithm that enhances deconvolution accuracy and robustness in GC×GC-TOFMS data analysis.

Findings

SIML outperforms traditional methods in low SNR conditions.

SIML provides more accurate mass spectra and concentration estimates.

SIML demonstrates robustness against retention time shifts.

Abstract

This paper introduces a novel deconvolution algorithm, shift-invariant multi-linearity (SIML), which significantly enhances the analysis of data from a comprehensive two-dimensional gas chromatograph coupled to a mass spectrometric detector (GC$\times$GC-TOFMS). Designed to address the challenges posed by retention time shifts and high noise levels, SIML incorporates wavelet-based smoothing and Fourier-Transform based shift-correction within the multivariate curve resolution-alternating least squares (MCR-ALS) framework. We benchmarked the SIML algorithm against traditional methods such as MCR-ALS and Parallel Factor Analysis 2 with flexible coupling (PARAFAC2$\times$N) using both simulated and real GC$\times$GC-TOFMS datasets. Our results demonstrate that SIML provides unique solutions with significantly improved robustness, particularly in low signal-to-noise ratio scenarios, where it maintains high accuracy in estimating mass spectra and concentrations. The enhanced reliability of quantitative analyses afforded by SIML underscores its potential for broad application in complex matrix analyses across environmental science, food chemistry, and biological research.