Integration of NIR spectroscopy and chemometrics for authentication and quantitation of adulteration in sweet marjoram (Origanum majorana L.)

Origanum majorana L. (marjoram) has considerable promise as a folk remedy for digestive ailments in addition to its ethnomedicinal use in gynecological disorders. Due to the high demand for the botanical raw material, it is prone to intentional and inadvertent adulteration by morphologically similar herbs. In the current study, the adulteration of marjoram with its common adulterants; oregano, thyme, olive, basil and sage has been thor-oughly investigated. To distinguish between marjoram and its prevalent adulterants, an integrated near infrared (NIR) spectroscopic method coupled with chemometric data analysis was developed. For exploratory pattern recognition, unsupervised multivariate models such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were implemented, followed by supervised models. Authentication of marjoram was successfully assessed using soft independent modelling of class analogy (SIMCA) with 100% sensitivity. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) modeling was employed to discriminate pure marjoram from its deliberately adulterated mixtures. Further, amounts of the adulterants were quantified in the botanical mixtures using partial least squares regression (PLS-R). The calibration and validation goodness of fit were determined to be greater than 0.9 and almost one, with a low root mean square error of prediction (RMSEP) and high ratio of performance to deviation (RPD) and range error ratio (RER) which highlight the model significant potential. Moreover, the optimal number of latent variables was found to be 5 as computed by permutation test. In the current study, the validity and reliability of the models employed to assess marjoram authenticity and purity was evidenced by both internal and external validation methods with a little to no data pre-processing.