@article {1088, title = {Thin Layer Chromatography Fingerprinting and Clustering of Orthosiphon stamineus Benth. from Different Origins}, journal = {Pharmacognosy Journal}, volume = {12}, year = {2020}, month = {February 2020}, pages = {79-87}, type = {Original Article}, chapter = {79}, abstract = {

Introduction: Orthosiphon stamineus has been widely used across Asian countries for the treatment of various diseases. The quality of herbal medicine determine its safety as well as efficacy; and geographical origin is important factor contributing on the quality of herb and its products. Materials and Methods: Thin Layer Chromatography (TLC) method combined with chemometric, Principal Component Analysis (PCA), has been employed to evaluate the quality of Orthosiphon stamineus leaves collected from eleven origins in Indonesia. Results: The results showed that mobile phase suitable for Orthosiphon stamineus was chloroform, dichloromethane, ethyl acetate (7:4:1). The method used has met the requirements of TLC system stability and precision. TLC-fingerprints analyzed with chemometrics showed an ability to discriminate Orthosiphon stamineus from various origins. PCA score plot of the first two principal components (PC) clearly distinguished 3 clusters of samples, whereas the loading plot of the first two PC showed that compounds with the Rf values of 0.0-0.1, 0.1-0.2, 0.2-0.3, and 0.9-1.0 are the most important compounds for clustering of samples. Conclusions: TLCfingerprint combined with the PCA was able to discriminate among the leaves of Orthosiphon stamineus originated from various locations. TLC-fingerprints analyzed with chemometrics can be used as an alternative of marker-oriented method to evaluate the quality of Orthosiphon stamineus.

}, keywords = {Geographical origin, Herbal medicine, Marker, Principal Component Analysis, Quality, TLC}, doi = {10.5530/pj.2020.12.13}, author = {Kartini Kartini and Ervina Rustiana Dewi and Fandi Achmad and Nikmatul Ikhrom Eka Jayani and Mochammad Arbi Hadiyat and Christina Avanti} } @article {774, title = {Chemicals and Bioactivity Discrimination of Syconia of Seven Varieties of Ficus deltoidea Jack via ATR-IR Spectroscopic-Based Metabolomics}, journal = {Pharmacog Journal}, volume = {10}, year = {2018}, month = {November 2018}, pages = {s147-s151}, type = {Original Article}, chapter = {s147}, abstract = {

Introduction: Ficus deltoidea is one of the common Malaysian medicinal plants and currently commercialized as raw ingredients in some local food products. However, those products do not discriminate the varieties of Ficus deltoidea used. Methods: FTIR-based metabolomics coupled with chemometric technique was applied to discriminate chemical components in ethanolic extracts of syconia of seven varieties of Ficus deltoidea namely; var. deltoidea, var. trengganuensis, var. kunstleri, var. angustifolia, var. bilobata, var. intermedia and var. motleyana. Unsupervised multivariate data analysis (MVDA) including principal component analysis (PCA) was used as to evaluate chemical variability among the seven varieties. For discrimination, orthogonal partial least square discriminant analysis (OPLS-DA) was applied, while partial least square (PLS) was used to evaluate the relationship between the alpha-glucosidase inhibition, antioxidant activity and Ficus deltoidea varieties. Results: As a result, OPLS-DA successfully discriminated the seven varieties. The FTIR fingerprints which were responsible for the discrimination includes 1729, 1705, 1448, 1095, 453, 443 cm-1. In addition, PPLS model demonstrated the correlation between var. kunstleri, var. deltoidea and var. intermedia respective chemicals fingerprints and their bioactivity (DPPH, FRAP and \α-glucosidase inhibition). Conclusion: The findings revealed that FTIR spectroscopy, in combination with MVDA, can be used for structural functional discrimination in relation to the sample bioactivity.

}, keywords = {Alpha-glucosidase Inhibition, antioxidant activity, Fourier Transform Infra-red Spectroscopy, Orthogonal Partial Least Square Discriminant Analysis, Principal Component Analysis}, doi = {10.5530/pj.2018.6s.27}, author = {Alkasim Kabiru Yunusa and Zalilawati Mat Rashid and Nashriyah Mat and Che Abdullah Abu Bakar and Abdul Manaf Ali} }