Spectral insights: advancing the authentication and quality assessment of Moroccan saffron through UV–visible spectroscopy and multivariate chemometric methods

With the increasing concerns among consumers regarding food quality and safety, the demand for reliable food authentication methods has risen significantly. This study aims to address this challenge by utilizing UV–visible spectroscopy as a fast and accurate analysis tool for geographically discriminating Moroccan saffron samples from five different regions. Analysis of the saffron samples was carried out using a UV–visible spectrophotometer and a quartz cell, over the wavelength range 200–700 nm, with an optical path length of 1 cm. The spectral data were analyzed using three chemometric models: principal component analysis (PCA), support vector machine (SVM) and linear discriminant analysis (LDA). Our findings revealed that samples from the TALIOUINE, ANERGUI, and AIT-MAZIGH regions were categorized as grade I, while samples from the AIT-OUMDIS region fell under grade II, and saffron from the TAZENAKHT region was classified as grade III. Furthermore, we employed LDA and SVM techniques to successfully classify the synchronous spectra of 120 saffron samples. Notably, the best-performing results were yielded by SVM, achieving a remarkable 97.89% accuracy in correctly classifying the samples when using spectral data processed with the standard normal variate method. This study emphasizes the potential and applicability of UV–Vis spectroscopy as a promising technique for effectively classifying and assessing the quality of saffron samples, taking into account their variety and production method.