rapid authentication and classification of grape seed oil using fluorescence spectroscopy combined with sparse classification and regression methods

Authentication and classification of grape seed oil (gso) varieties have attracted much attention infood industry, in recent years [1]. in the present work, excitation-emission fluorescence spectroscopyand sparse chemometric methods were used to classify different varieties of gso taken fromdifferent grape genotypes. moreover, gso adulteration with sunflower oil (sfo) was successfullyassessed using sparse regression methods. fluorescence spectra were obtained in the wavelengthregions of λex= 200-500 nm and λem= 200-800 nm. more than 200 samples from five differentgso genotypes were used to build multivariate models. the sparse version of n-way partial leastsquares discriminant analysis (snpls-da) [2] was used to develop interpretable classificationmodels. the capabilities of the snpls-da model provide a valuable insight about the importantwavelengths in fluorescence spectra to distinguish between gsos. furthermore, adulterant levelsin gso samples were quantified using sparse regression techniques [3], including the least absoluteshrinkage and selection operator (lasso), ridge, and elastic net, and the results were compared withthose obtained using the vip-pls method. the overall accuracy for snpls-da was 1.00 and thecoefficient of multiple determination (r2) for lasso model was 0.914, for the test sets. the resultsin this work revealed that sparse classification and regression models, including snpls-da andlasso, coupled with excitation-emission fluorescence spectroscopy can be effectively used to assessthe quality of oil samples in the food industry