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Structure-Preserving Joint Non-negative Tensor Factorization to Identify Reaction Pathways Using Bayesian Networks.

Anjana Puliyanda¹, Kaushik Sivaramakrishnan¹, Zukui Li¹

¹Department of Chemical and Materials Engineering, University of Alberta, 9211 116 Street NW, Edmonton, Alberta T6G 1H9, Canada.

Journal of Chemical Information and Modeling

|November 23, 2021

Summary

This summary is machine-generated.

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This study introduces a novel method for analyzing complex chemical mixtures using coupled non-negative tensor factorization. It effectively combines spectroscopic data from Fourier transform infrared (FTIR) and proton nuclear magnetic resonance (¹H NMR) for enhanced species identification and reaction pathway analysis.

Area of Science:

Chemical analysis
Spectroscopy
Data science

Background:

Identifying species and monitoring reactions in complex mixtures is crucial for chemical process understanding.
Spectroscopic data from multiple sensors (FTIR, ¹H NMR) and process modes (temperature, residence time) contain hidden, complementary information.
Existing methods struggle to fully leverage the combined information from diverse spectroscopic techniques.

Purpose of the Study:

To develop a structure-preserving and interpretable approach for jointly analyzing spectroscopic data from multiple sensors.
To achieve unique decomposition of complex chemical system data.
To enable causal structure inference for identifying reaction pathways.

Main Methods:

Coupled non-negative tensor factorization (NMTF) to jointly decompose multi-sensor spectroscopic data.

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Interpretation of tensor decomposition projections as pseudo-component spectra and concentrations.

Bayesian networks for causal structure inference among pseudo-component spectra.

Grid tensor factorization for scalable parallelization and robustness to data artifacts.

Main Results:

The coupled NMTF approach successfully captures hidden patterns from FTIR and ¹H NMR data.
Pseudo-component spectra and concentrations were derived, enabling reaction pathway elucidation.
FTIR data facilitated reaction sequence development based on functional groups.
¹H NMR data provided complementary insights, disambiguating components with similar FTIR spectra.
A scalable and robust tensor decomposition method was developed for high-dimensional process data.

Conclusions:

Jointly analyzing FTIR and ¹H NMR data via coupled NMTF provides a comprehensive understanding of chemical systems.
This integrated approach enhances species identification and reaction mechanism discovery in complex mixtures.
The developed scalable tensor factorization method offers a robust solution for real-world process data analysis.