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Parallel to the growing global interest in alternative medical therapies, high measures of counterfeit pharmaceuticals enter the global market and, therefore, detection of such marketed products is essential. This article throws an illuminating spot on the adulteration of () with and exhaustively extracted. A speedy and nondestructive near-infrared method in conjunction with the mathematical tools of chemometrics was used to distinguish between genuine cinnamon and its common adulterants. The principal component analysis and the hierarchical cluster analysis models successfully discriminated between unadulterated and adulterated samples. In the second part of the work, soft independent modeling of class analogy was implemented to construct a chemometric model to authenticate samples. The constructed model could successfully predict and judge the quality of powder without any misleading predictions. Finally, partial least squares regression was approached to establish the correlation for adulterated samples regarding their cassia and exhausted cinnamon content. The of calibration and validation were all higher than 0.9, while the root mean square errors were all lower than 0.05, indicating that the established models were successful. Overall, the developed models were shown to have significant potential as time-saving and accurate methods for identification of true cinnamon powder, which can help guarantee both quality aspects of identity and purity of the herbal drug by avoiding its adulteration and could be implemented as a routine screening in its quality control with no need for any sample preparation.

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