Rapid discrimination of Nigella Sativa oil from different geographical origins using ATR-FTIR-unsupervised and supervised pattern recognition techniques

Previous metabolomics studies have proven the efficiency of infrared (IR) spectroscopy as a tool for authenticity assessment. Attenuated total reflectance – Fourier transform infrared spectroscopy (ATR-FTIR), with principal component analysis (PCA) and bidirectional orthogonal projection to latent structures-discriminant analysis (O2PLS-DA), was used to create a model for the discrimination of N. sativa oil samples gathered from three geographical areas; Egypt, Ethiopia and Syria. It is reported that, for herbal drug treatments even from the equal species, the quality and efficacy are somewhat different according to their growing conditions. The unsupervised pattern recognition of variable authentic samples utilizing PCA and HCA (Hierarchical Cluster Analysis) revealed that each oil type was separately clustered from the others. The data matrix of ATR-FTIR was handled by the supervised pattern recognition technique O2PLS-DA to give a better clustering and a better separation between sample types than given by PCA. For the first time, this study built a partial least square (PLS) model used to predict thymoquinone concentration in any new N. sativa seed oil only by its ATR FT-IR spectrum. Furthermore, OPLS and its HCA model of authentic samples have been used to predict types of commercial oil samples obtained from the Egyptian market and compare them with sellers' claims.As demonstrated in this study, the recommended method might be used to regularly evaluate the phytochemical variability in N. sativa seed oil varieties sourced from various regions, which could aid in meeting the demand for their quality and safety.