Near-Infrared Mixture Identification by an Automated Library Searching Method: A Multivariate Approach

Abstract

A mathematical technique for the identification of components in the near-infrared spectra of liquid mixtures without any prior chemical information is demonstrated. Originally, the technique was developed for searching mid-infrared spectral libraries. It utilizes principal component analysis to generate an orthonormal reference library and to compute the projections or scores of a mixture spectrum onto the principal space spanned by the orthonormal set. Both library and mixture spectra are analyzed and processed in Fourier domain to enhance the searching performance. A calibration matrix is calculated from library scores and is used to predict the mixture composition. Five liquid mixtures were correctly identified with the use of the calibration algorithm, whereas only one mixture was correctly characterized with a straight dot-product metric. The predictions were verified with the use of an adaptive filter to remove each of the resulting components from the library and the mixture spectra. In addition, a similarity index between the original mixture spectrum and a regenerated mixture spectrum is used as a final confirmation of the predictions. The effects of random noise on the searching method were also examined, and further enhancements of searching performance are suggested for identifying poor-quality mixture spectra.