Excitation-emission fluorescence spectroscopy and sparse chemometric methods for grape seed oil classification and authentication

Abstract

Grape seed oil (GSO) authentication and classification have recently become the most important aspects of edible oil quality control due to its high nutritional values. In the present contribution, excitation-emission fluorescence spectroscopy and sparse chemometric methods were used for the classification of GSO obtained from different Iranian grape genotypes and the detection of GSO adulteration with refined sunflower oil (SFO). The fluorescence spectra were collected in the wavelength ranges of λex = 200–500 nm and λem = 200–800 nm. To develop multivariate models, more than 200 samples from five different varieties of GSO were used. The sparse version of N-way partial least squares discriminant analysis (sNPLS-DA) was utilized to build an interpretable classification model. Classification accuracies for the sNPLS-DA model were 1.00 for predicting all grape genotypes. The collected fluorescence data revealed a distinct intensity difference between the Chafteh GSO and other GSOs in λex = 270–310 nm, and λem = 300–350 nm. To simulate the adulteration of GSO with SFO, 35 binary blends of Chafteh GSO samples (as a higher-quality oil) were prepared at adulterant levels from 10% to 50%. Sparse multivariate regression techniques, including the least absolute shrinkage and selection operator (Lasso), Ridge, and Elastic net were applied for quantitative analysis of GSO adulteration. The coefficient of multiple determination (R2) and the RMSE for the Lasso model were 0.914 and 0.013, respectively, for the external test sets. The results showed that the sparse chemometric methods coupled with excitation-emission fluorescence spectroscopy can be used as interpretable and reliable tools for quality control and adulteration detection of oil samples in the food industry and commerce.