Factor analysis of chemical mixtures: Non-negative Factor Solutions for Spectra of Cereal Amino Acids

Abstract

From spectral data for a set of mixtures of unknown compounds, the spectra and the amounts of the pure components can be estimated without physical separation of the compounds. Spectra for the amino acid content of whole finger millet grain samples are used as the example. Different methods of factor analysis and weighting were compared. The number of relevant “pure components” (i.e. protein groups) was found to be 3 in finger millet grain grown under widely varying fertilizer conditions. Ranges of acceptable spectra of these “pure components” and ranges of their amounts were found by applying non-negativity criteria to the factor analysis solutions. The spectra were then estimated concisely by performing the factor analysis on the data scaled to different units in which all components except one remained constant and were excluded from the factor solution in turn. The amounts of the three “pure components” were estimated by multiple regression. Thus the rotationally ambiguous factor analysis solution was converted to a physically meaningful description of the unknown compounds in the mixtures.