Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

Quality Evaluation of Tripterygium Glycoside Tablets Based on Quantitative Band-Selective 2D 1H–13C HSQC and 1H NMR Fingerprinting

“…The data were divided into 7 folds for cross-validation, R 2 is 0.97, and Q 2 is 0.96 when only component 1 and component 2 are considered, indicating that the OPLS-DA model for TGTs from the 3 manufacturers has good prediction . Additionally, we collected 1 H NMR of another 27 batches of TGTs and used these spectral data sets as the prediction set to validate the OPLS-DA model. , By importing the prediction set ( x 1– x 27) into the OPLS-DA model and setting these samples to no class, the Y Pred value, the probability of the prediction set being classified into the specified class, can then be read from the “Classification List” module of SIMCA 14.1. A sample is considered to belong to a specified class when the Y Pred value is greater than 0.65, and when the Y Pred value is less than 0.3, the sample is considered not to belong to that class.…”

Analysis of GC × GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin

“…Over the past two decades, the fingerprinting strategy has emerged as a practical approach for assessing the quality of medicinal materials [ [11] , [12] , [13] , [14] , [15] , [16] ]. Nonetheless, relying solely on one-dimensional (1D) separation techniques for natural products leads to issues such as co-elution of similar compounds and challenges in detecting trace compounds.…”