Non-targeted volatilomics for the authentication of saffron by gas chromatography-ion mobility spectrometry and multivariate curve resolution.

In the present contribution, a novel non-targeted volatilomic study based on headspace GC-IMS (HS-GC-IMS) was developed for the authentication and geographical origin discrimination of saffron. In this regard, multivariate curve resolution-alternating least squares (MCR-ALS) was employed to recover the pure GC elution and IMS profiles of saffron metabolites. Iranian saffron samples from seven important areas were analyzed by HS-GC-IMS. The resulting second-order GC-IMS datasets were organized in a augmented matrix and processed using MCR-ALS with various constraints. The MCR-ALS resolved GC profiles were analyzed by different pattern recognition techniques; principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and data driven-soft independent modeling of class analogy (DD-SIMCA). The saffron samples were assigned to their seven geographical origins with an accuracy of 89.0 %. Additionally, four adulterants (style, safflower, madder and calendula) were reliably detected with over 94.0 % accuracy. In this context, GC-IMS substantially outperformed the commonly used FT-NIR spectroscopy approach.