Identification of Edible Oils by Principal Component Analysis of 1H NMR Spectra

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 oil), 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.