Pharmaceutical polymorph identification and multicomponent particle mapping with non-negative matrix factorization
This data publication contains the code and demonstration data from a study using non-negative matrix factorization to learn, characterize, and chemically map crystal polymorphs at the single particle scale from high spatial resolution time-of-flight secondary ion mass spectrometry (ToF-SIMS) images. The data from this study includes the ToF-SIMS chemical imaging of three inkjet printed arrays of acetaminophen deposits, corresponding THz Raman spectra, and ToF-SIMS chemical images of a pure acetaminophen powder and a migraine medicine. Also included are the data analysis code (MATLAB 2022a*) used for non-negative matrix factorization and other processes. The code is used to learn the dataset's latent dimensionality and decompose the data into constituent phases representative of acetaminophen polymorphs. The process is also demonstrated by unmixing a multi-component particle migraine medicine sample.Associated publication: https://doi.org/10.1021/acs.analchem.2c03913*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.
Complete Metadata
| bureauCode |
[ "006:55" ] |
|---|---|
| identifier | ark:/88434/mds2-2719 |
| issued | 2022-08-12 |
| landingPage | https://data.nist.gov/od/id/mds2-2719 |
| language |
[ "en" ] |
| programCode |
[ "006:045" ] |
| references |
[ "https://doi.org/10.1021/acs.analchem.2c03913" ] |
| theme |
[ "Chemistry:Analytical chemistry", "Chemistry:Molecular characterization", "Health:Pharmaceuticals", "Materials:Materials characterization", "Materials:Modeling and computational material science" ] |
