Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour

Abstract

Quinoa flour is prone to economically motivated adulteration due to its high nutritional value and growing demand worldwide. In this study, portable near-infrared spectroscopy (NIRS) combined with multivariate analysis was used to detect adulteration in quinoa flour. The initial investigation was carried out using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) to identify the most potential adulterate in quinoa flour. Quinoa flour samples were then adulterated with identified adulterant in the range of 0–51% (w/w). The partial least squares regression (PLSR) model built with original spectral data had the best performance for detecting the level of adulteration, with a coefficient of determination (Rp2) of 0.94, root-mean-square error of prediction (RMSEP) of 3.04%, ratio of prediction to deviation (RPD) of 4.04, and range error ratio (RER) of 11.84. The variable importance in projection (VIP) resulting from the PLSR model was used to select 13 informative spectral bands. The new PLSR model led to an R2p of 0.98, RMSEP of 1.60%, RPD of 7.71, and RER of 22.56. The results demonstrated the potential of portable NIRS as a rapid, low cost and non-destructive analytical tool for adulteration detection in quinoa flour.