The Ability of Near-Infrared Spectroscopy to Discriminate Plant Protein Mixtures: A Preliminary Study

Abstract

The aim of this paper was to evaluate the effect of two different matrices (e.g., starch base flour vs. protein base flour) on the ability of near-infrared (NIR) spectroscopy to classify binary mixtures of chickpea (protein), corn and tapioca (starch) flours. Binary mixtures were made by mixing different proportions of chickpea plus corn, chickpea plus tapioca, and corn plus tapioca flour. Spectra were collected using NIR spectroscopy and the data analyzed using techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The results showed an effect of the matrix on the PLS-DA classification results, in both classification rates and PLS loadings. The different combinations of flours/mixtures showed changes in absorbance values around 4752 cm−1 that are associated with starch and protein. Nevertheless, the use of NIR spectroscopic might provide a valuable initial screening and identification of the potential contamination of flours along the supply and value chains, enabling more costly methods to be used more productively on suspect samples.