A hierarchical screening method for detection of illegal adulterants in Fur seal ginseng pills by profiling analysis of HPLC multi‐dimensional fingerprints. Issue 8 (26th February 2019)

• Record Type: Journal Article
• Authors: Wang, Ke; Zeng, Huiyun; Zhang, Yi; Xie, Xiujuan; Yue, Zhenfeng; Zhang, Wei; Fu, Chujun; Luo, Laicheng; Fan, Huajun
• Is Part Of: Journal of separation science. Volume 42:Issue 8(2019)
• Page Start: 1509
• Page End: 1519
• Publication Date: 2019-02-26
• Subjects: functional foods -- Fur seal ginseng pills -- hierarchical screening -- illegal adulterants -- multi‐dimensional fingerprints -- traditional Chinese medicine; Separation (Technology) -- Periodicals; Chromatographic analysis -- Periodicals; 543.089
• DOI: 10.1002/jssc.201900003
• Languages: English
• ISSNs: 1615-9306

Abstract

Abstract: A novel method for hierarchical screening of illegal adulterants in Fur seal ginseng pills products was developed by multi‐dimensional fingerprint profiling analysis. Fingerprint feature of the samples was acquired by high‐performance liquid chromatography analysis of 11 batches of samples with diode array detector and fluorescence detector, and then potential illegal adulterants including phosphodiesterase type‐5 inhibitors, androgens, α receptor antagonists and yohimbine, were further separated at multiple wavelengths to reduce or remove interferences from sample matrix for highlight their chromatographic characteristics. Accordingly, a hierarchical screening strategy was designed by first‐order and second‐order fingerprints combined with spectral examination to achieve high accuracy and reliability. The method was successfully applied to screening of illegal adulterants in real samples, and it also exhibited good quantification performance through validation tests. From 16 batches of samples, three suspected samples were confirmed to be positive, containing 9.37μg/g of testosterone, 18.8 μg/g of tadalafil, and 48.5 μg/g of sildenafil, respectively. The recoveries and relative standard deviations were in the range of 83.6–103.1% and 4.2–6.8%, respectively. The proposed method provided a simple, efficient and promising alternative to monitoring functional foods.