Geographical origin differentiation and quality determination of saffron using a portable Hyperspectral imaging system

A portable Hyperspectral Imaging (HSI) system (400–1000 nm) coupled with chemometrics techniques was developed to identify the geographical origin and to determine the Crocins content of saffron rapidly and non-destructively. Reflectance spectra of 160 saffron samples from two geographical origins were captured and preprocessed using the Savitzky–Golay (SG) algorithm. PCA as an unsupervised clustering and PLS-DA, RBF, and MLP as supervised techniques were applied for the classification and geographical origin differentiation of the samples. Afterward, the sample's Crocins content was measured and their associations with their spectra were modeled using PLSR, RBF, and MLP models. The PCA results indicated that saffron samples were originally distributed in two different clusters. PLS-DA model with a spectral range from 400 to 1000 nm achieved the best clustering performance (Sensitivity and Specificity: 100 %). The RBF model showed good predictive capabilities in the saffron Crocins content prediction and quality evaluation (R2cal > 0.94 and R2pred > 0.83).