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

Abstract

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 1H 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.

Antibacterial Activity and Metabolomics Profiling of Torch Ginger (Etlingera elatior Jack) Flower Oil Extracted Using Subcritical Carbon Dioxide (CO2)

“…Evidence-Based Complementary and Alternative Medicine that demonstrated strong antimicrobial activity. Anderson et al [49] identified several compounds of 6 oil samples using the combination of 1 H-NMR and the principle component analysis (PCA). In another study, NMR analysis was used to discriminate several oils including olive, hazelnut, and sunflower [50].…”

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

“…The original relaxation fingerprint data were normalized by dividing the intensity of the signal acquired by using the single pulse excitation experiment with the same experimental conditions. Edible oil analysis methods have relied heavily on PCA owing to its high reliability and versatility [ 35 , 36 , 37 ]. In this work, PCA was applied to verify the capability of the relaxation fingerprints approach in edible vegetable oils discrimination.…”

Reply to “Comment on ‘Identification of Edible Oils by Principal Component Analysis of 1H NMR Spectra’”

“…1 Readers may examine Figure S7 (student data with faculty-corrected referencing) of Anderson et al's paper. 2 Second, to clarify the efficacy of our published technique, 2 we include here ( Figure 1) additional examples of studentacquired and student-analyzed data using the reported untargeted methodology, which achieved very good classification.…”

“…In our report, 2 we included a discussion of some of the features that emerged from the unbiased PCA analysis, and not surprisingly, these features are very similar to the features that were used by Yeh 1 and reported by Vigli et al 3 We wish to reiterate that our work focused not on optimizing clustering, which has been ably demonstrated in the research literature, but on an efficient and rugged hands-on experiment that provides students with an authentic, entrylevel learning experience in multivariate analysis that could be completed in one laboratory period, has assessable learning goals, and accurately represents the initial steps in unbiased variable reduction and data clustering. 2 In order to model for students the general approach taken in research, we purposefully chose an untargeted analysis. The subsequent grouping, filtering, and scaling within MetaboAnalyst 4 lead to excellent oil classification, and the efficient workflow is important to enabling students to conduct the work within the time constraints of the laboratory period.…”

“…The grouping is constrained by the digital resolution (i.e., binning) of the original data, and a part of our work was to optimize the selection of the bin sizes to best work with the grouping in MetaboAnalyst. 2 We find Tai-Sheng Yeh's approach 1 to be a useful extension of the original experiment, potentially as a postlaboratory exercise or as a second laboratory period in which students use their own unbiased PCA results to conduct variable reduction in order to optimize classification. Our preference is not to give students the features at the onset, but rather to have them experience the process of using unbiased methods to discover them.…”