Unfold principal component analysis

Guimet et al. used two potential multiway methods for the discrimination between virgin olive oils and pure olive oils the unfold principal component analysis (U-PCA) and parallel factor analysis (PARAFAC), for the exploratory analysis of these two types of oils. Both methods were applied to the excitation-emission fluorescence matrices (EEM) of olive oils and followed the comparison of the results with the ones obtained with multivariate principal component analysis (PCA) based on a fluorescence spectrum recorded at only one excitation wavelength. 

Guimet, R, Boque, R., and Ferre, J. Application of unfold principal component analysis and parallel factor analysis to the exploratory analysis of olive oils by means of excitation-emission matrix fluorescence spectroscopy. Analytica Chimica Acta, 515, 75-85. 2004. 

The resulting matrix is then unfolded into a one-dimensional vector, which can be merged with the shape description, and is suitable for multivariate statistics analysis such as principal component analysis (PGA) and partial least squares (PLS). 

In mid-2004, Schanze reported anirmic poly(/Meta-PEs) that show solution photophysical properties in MeOH—water mixtures that are consistent with folding [40]. Principal component analysis allowed the spectra to be deconvoluted into two pure component spectra, which were interpreted as the folded and unfolded states. Calculating the components of free energy gave AH = 10.8 kcal mol and... 

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