Variables Selection in the Ultraviolet, Visible and Near Infrared Range for Calibration of a Mixture of Vegetable Oils by Absorbance Spectra

Abstract

The aim of the work was a multivariate calibration of the concentration of unrefined sunflower oil, considered as adulteration, in a mixture with flaxseed oil. The relevance of the study is due to the need to develop a simple and effective method for detecting the falsification of flaxseed oil which is superior in the content of essential polyunsaturated fatty acids to olive oil. A few works only are devoted to identifying adulteration of flaxseed oil, unlike olive oil. Multivariate calibration carried out using a model based on the principal component analysis, cluster analysis and projection to latent structures of absorbance spectra in UV, visible and near IR ranges. Calibration uses three methods for spectral variables selection: the successive projections algorithm, the method of searching combination moving window, and method for ranking variables by correlation coefficient. The application of the successive projections algorithm, ranking variables by correlation coefficient and searching combination moving window makes it possible to reduce the value of the root mean square error of prediction from 0.63 % for wideband projection to latent structures to 0.46 %, 0.50 %, and 0.03 %, respectively. The developed method of multivariate calibration by projection to latent structures of absorbance spectra in UV, visible and near IR ranges using the spectral variables selection by searching combination moving window is a simple and effective method of detecting adulteration of flaxseed oil.