Identification of Edible Oils by Principal Component Analysis of H-1 NMR Spectra
Publication Date
2017
Description
Principal component analysis (PCA) is a statistical method widely used in chemometric studies to analyze large correlated sets of data. An undergraduate laboratory experiment involving PCA of H-1 NMR spectral data is described. Students collect NMR spectra of an unknown oil sample are provided with spectra of six oil standards (canola, corn, olive, peanut, sesame, and sunflower), and are asked to identify the unknown oil using score plots based on the PCA results. This laboratory experiment gives students hands-on experience collecting NMR spectra performing NMR spectral processing and utilizing freely available web-based software to subject the data to PCA and to prepare the subsequent scoring plots.
Journal
Journal of Chemical Education
Volume
94
Issue
9
First Page
1377
Last Page
1382
Department
Chemistry
Link to Published Version
https://pubs.acs.org/doi/abs/10.1021/acs.jchemed.7b00012
DOI
10.1021/acs.jchemed.7b00012
Recommended Citation
Anderson, Shauna L.; Rovnyak, David; and Strein, Timothy. "Identification of Edible Oils by Principal Component Analysis of H-1 NMR Spectra." Journal of Chemical Education (2017) : 1377-1382.