title: Quantitative analysis of α-mangostin in hydrophilic ointment using near-infrared spectroscopy.
abstract: The objective of this research was to quantify the α-mangostin content in mangosteen pericarp (MP) ointment as a colloidal dispersion using near-infrared (NIR) spectroscopy. Various concentrations of MP (IP and EP) ointments containing both internal and external pericarps were prepared and the NIR spectra of these ointments were measured. The NIR spectrum of each ointment was correlated with α-mangostin concentration by partial least square (PLS) regression. Validation of the models was performed and their predictive ability was also investigated. The equation and R(2) value for the prediction of α-mangostin concentration in IP ointment were y=0.9843x+0.4441 and 0.9730 and those in EP ointment were y=0.9569x+0.1142 and 0.9136, respectively. The biases of the IP and EP ointment models were 0.23 and 0.00, respectively. The results showed that NIR could be a useful tool for the quality control of herbal medicine in hydrophilic ointment without any sample preparation. It could predict α-mangostin content in hydrophilic ointment at very low concentration with sufficient accuracy.
authors: Center for Research and Development of Herbal Health Products, Faculty of Pharmaceutical Sciences, Khon Kaen University , Khon Kaen , Thailand .
authors: Jomjai
authors: J
authors: Peerapattana
authors: Kuniko
authors: K
authors: Otsuka
authors: Yusuke
authors: Y
authors: Hattori
authors: Makoto
authors: M
authors: Otsuka
mesh_terms: D029762:Garcinia mangostana; D057927:Hydrophobic and Hydrophilic Interactions; D009824:Ointments / Q000032:analysis* / Q000737:chemistry; D010936:Plant Extracts / Q000032:analysis* / Q000737:chemistry; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D044004:Xanthones / Q000032:analysis* / Q000737:chemistry
chemical_list: D009824:Ointments; D010936:Plant Extracts; D044004:Xanthones; C021053:mangostin
keywords: Garcinia mangostana L.; herbal product; non-destructive analysis; ointments; partial least square regression