Digital fingerprinting and quantitative analysis: A systematic strategy for differentiating bear bile powder from pig...

Bile-derived medicinal materials have been used for medicinal purposes for thousands of years, with bear bile powder (BBP) and pig bile powder (PBP) being two of the most commonly used bile powders. However, BBP is a precious and expensive medicinal material, while PBP is relatively inexpensive, leading to the risk of adulteration or substitution of BBP with PBP in the market. This study employed UPLC-QTOF-MS to separate 18 common bile acids and to collect samples of bear bile BBP and PBP. The resulting data were converted into a data matrix. A "characteristic ion set" for BBP and PBP was then obtained through feature algorithm extraction, and the top 50 ions from this set were output based on ion intensity. Using these "characteristic ion set" as a benchmark for sample matching and identification, efficient and rapid identification of BBP and PBP was achieved. The matching confidence (MC) for BBP was consistently above 80%, while for PBP it reached 100%. The method demonstrated high sensitivity, reliably detecting adulteration at levels as low as 1% PBP. Based on this performance, a detection threshold of MC>50% against the PBP ion set was established to identify adulteration, while a confirmation threshold of MC>60% against the BBP ion set was applied to verify sample authenticity. Validation using market blind samples of BBP showed no detection of PBP. Additionally, specific components of PBP, Taurohyodeoxycholic acid (THDCA) and Glycochenodeoxycholic acid (GHDCA), were identified. Using UPLC-QQQ-MS, a linear relationship was established between different proportions of PBP-adulterated samples and the concentrations of THDCA and GHDCA. This linear relationship allows for accurate quantification of the adulteration ratio. In summary, this study constructed a "characteristic ion set" for BBP and PBP based on UPLC-QTOF-MS technology, enabling rapid and accurate identification of BBP, PBP, and adulterated samples. Combined with UPLC-QQQ-MS for quantitative analysis of different adulteration ratios, this research provides a systematic solution for the precise identification of BBP and PBP and the detection of adulteration.