PARAFAC2×N: Coupled decomposition of multi-modal data with drift in N modes
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PARAFAC2×N : Coupled decomposition of multi-modal data with drift in N modes. / Sorochan Armstrong, Michael D.; Hinrich, Jesper Løve; de la Mata, A. Paulina; Harynuk, James J.
In: Analytica Chimica Acta, Vol. 1249, 340909, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - PARAFAC2×N
T2 - Coupled decomposition of multi-modal data with drift in N modes
AU - Sorochan Armstrong, Michael D.
AU - Hinrich, Jesper Løve
AU - de la Mata, A. Paulina
AU - Harynuk, James J.
N1 - Publisher Copyright: © 2023 Elsevier B.V.
PY - 2023
Y1 - 2023
N2 - Analysis of GC×GC-TOFMS data for large numbers of poorly-resolved peaks, and for large numbers of samples remains an enduring problem that hinders the widespread application of the technique. For multiple samples, GC×GC-TOFMS data for specific chromatographic regions manifests as a 4th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel is for all practical purposes nonexistent. A number of solutions to handling GC×GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection. The proposed model captures over 99.9% of variance for a synthetic data set, presenting an extreme example of peak drift and co-elution across two modes of separation.
AB - Analysis of GC×GC-TOFMS data for large numbers of poorly-resolved peaks, and for large numbers of samples remains an enduring problem that hinders the widespread application of the technique. For multiple samples, GC×GC-TOFMS data for specific chromatographic regions manifests as a 4th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel is for all practical purposes nonexistent. A number of solutions to handling GC×GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection. The proposed model captures over 99.9% of variance for a synthetic data set, presenting an extreme example of peak drift and co-elution across two modes of separation.
KW - Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry
KW - Multi-way analysis
KW - PARAFAC2
U2 - 10.1016/j.aca.2023.340909
DO - 10.1016/j.aca.2023.340909
M3 - Journal article
C2 - 36868765
AN - SCOPUS:85148012867
VL - 1249
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
SN - 0003-2670
M1 - 340909
ER -
ID: 339846484