Abstract
Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. In this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.
Original language | English |
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Article number | 020702 |
Journal | Journal of Applied Physics |
Volume | 133 |
Issue number | 2 |
DOIs | |
State | Published - Jan 14 2023 |
Funding
This work was supported by Exxon Mobil Chemical Company (A.J., W.L., D.A., and O.S.O.), by the Center for Nanophase Materials Sciences [Department of Energy (DOE) Office of Science User Facility] (A.I.), and by Procter & Gamble Company (M.I.U.H., J.H., and O.S.O.). We thank Dr. Sergei Kalinin at the Oak Ridge National Laboratory for his advice and fruitful discussion. This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).
Funders | Funder number |
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Center for Nanophase Materials Sciences | |
Exxon Mobil Chemical Company | |
U.S. Department of Energy | |
Office of Science | |
Procter and Gamble Fund | DE-AC0500OR22725 |