Chemical fingerprint analysis for quality assessment and control of Curcuma longa L. rhizomes from Vietnam using a high-performance liquid chromatography-diode array detector (HPLC-DAD) - Tho Do Chau Minh Vinh; Sil Nguyen Thanh; Ngan Nguyen Thanh; Tham Le Kim; Thi Huynh Huynh Anh; Giang Le Thi Truc; Mai Nguyen Thi Hong (Pensoft Publishers, 2024)
Turmeric, extensively cultivated across Southeast Asia, especially in Vietnam, harbors active polyphenols, primarily curcumin (2-5%), renowned for its diverse health benefits. Pharmacopoeias recognize turmeric, yet it lacks standardized quality assessments and encounters challenges in extraction and identification due to natural variations and adulteration. This analytical method is vital for verifying the authenticity, purity, and quality of turmeric products in both the pharmaceutical and nutraceutical industries. This study successfully developed an efficient extraction process for curcumin (CUR), demethoxycurcumin (DMC), and bisdemethoxycurcumin (BDMC) from Curcuma longa L. rhizomes. The herbal powder was extracted with methanol (1:30, w/v) by the ultrasound-assisted method for 10 minutes, and this process was repeated three times. A high-performance liquid chromatography-diode array detector (HPLC-DAD) method was validated for the simultaneous quantification of three analytes, following the AOAC guideline and achieving a correlation coefficient (R2) value greater than 0.9950. Utilizing the HPLC-DAD method, the study developed a chemical fingerprint analysis for three analytes to identify the characteristic chemical components distinguishing turmeric from each region. Nineteen samples collected from various provinces across Vietnam were subjected to analysis. In all analyzed samples, the concentrations of CUR, DMC, and BDMC ranged from 0.77-10.30%, 0.33-6.92%, and 0.03-3.23%, respectively. CUR was determined to be the dominant compound in most samples, while BDMC consistently exhibited the lowest levels of content. Utilizing the findings derived from the analysis of RRT and RPA metrics, the research assessed variances across sample batches. It is suggested that this newly established approach can be applied to construct and develop raw material areas to serve the needs of each field.