Authentication and detection of common adulterants in Clove buds (Syzygium aromaticum L.) powders and oils using near IR combined to multivariate analysis

This study investigates the use of NIR diffuse reflectance spectroscopy with multivariate analysis for quality control and authentication of clove buds powders as well as their oils. Unsupervised and supervised chemometric analysis techniques, including principal component analysis (PCA), data driven soft independent modeling of class analogy (DD-SIMCA) were implemented to authenticate clove and its oil and distinguish them from their adulterants. The SIMCA model showed 100% sensitivity and specificity values in detecting adulterants in clove powder samples and clove oil samples. This showcases the accuracy and reliability of the DD-SIMCA approach in accurately distinguishing between different classes. The models were validated using test samples, and the absence of noise modeling was confirmed through permutation. PLS regression analysis was utilized to measure levels of adulterants in clove powder and clove oil samples. The models produced good results, with RMSEC values ranging from 0.68 to 1.5% for clove oil and from 0.96 to 1.27% for clove powder. External validation was performed and the limits of quantitation ranged from 1.8% to 4.9% for clove powder and clove oil. The models can detect sample adulteration and ensure authenticity. The results showed that the suggested method and models can be used to detect sample adulteration, ensure authenticity, and have high sample throughput. The method had several advantages, including simplicity, speed of analysis, with no sample preparation needed.