Application of 2D correlation spectroscopy and outer product analysis to infrared spectra of sugar beets

Abstract

The reference method for the measurement of the percentage of sugar in sugar beets is polarimetry. Infrared spectroscopy is now being more and more used in the food industry. Based on polarimetric reference values and infrared spectra, regression techniques, such as partial least square regression (PLS), may be used to develop mathematical models to predict the sugar content of beets. Before establishing the model, it is preferable to select informative parts of the infrared spectra in order to improve the model performance. Several different methods can be used to determine regions of interest in a spectrum, such as outer product analysis (OPA) and 2D correlation spectroscopy (2DCOS). Outer product analysis can be used to facilitate the interpretation of near infrared (NIR) and mid infrared (MIR) signals. In this method, the spectra acquired in the two domains are combined by calculating the outer product matrix of the two vector signals of each sample. It is then possible to perform statistical analyses, such as principal components analysis or partial least square regression, on the resulting data set of matrices in order to highlight relations between spectral features in the two domains. This can facilitate the attribution of NIR bands based on their relation to MIR peaks. Results of the 2D correlation spectroscopy will also be presented. This is another method that can be used to allow bands in the NIR spectrum to be resolved and assigned to characteristic absorbances in the MIR spectrum. The principle of this method is to detect regions in both spectra (NIR and MIR) that change simultaneously as sugar content varies.