External parameter orthogonalization-support vector machine for processing of attenuated total reflectance-mid-infrared spectra: A solution for saffron authenticity problem

Abstract

In the present work, a new approach based on external parameter orthogonalization combined with support vector machine (EPO-SVM) is proposed for processing of attenuated total reflectance-Fourier transform mid-infrared (ATR-FT-MIR) spectra with the goal of solving authentication problem in saffron, the most expensive spice in the world. First, one-hundred authentic saffron samples are clustered by principal component analysis (PCA) with EPO as the best preprocessing strategy. Then, EPO-SVM is used for the detection of four commonly used plant-derived adulterants (i.e. safflower, calendula, rubia, and style) in binary mixtures (saffron and each of plant adulterants) and its performance is compared with other common classification methods. The obtained results showed that the EPO-SVM approach has a much better classification accuracy (>95%) than other methods (accuracy<89.2%). Finally, two different sample sets including mixture of saffron and four plant adulterants and commercial saffron samples are used for validation of the developed EPO-SVM model. In this regard, classification figures of merit in terms of sensitivity, specificity and accuracy were respectively 96.6%, 97.1%, and 96.8% which showed good classification performance. It is concluded that the proposed EPO-PCA and EPO-SVM approaches can be considered as reliable tools for authentication and adulteration detection in saffron samples.