Comprehensive analysis strategy of quality for traditional Chinese medicine compound based on fingerprint technology and quantitative prediction of spectra

Traditional Chinese medicine compound (TCMC) comes from a wide range of sources, and the complexity of chemical composition leads to differences in quality. Improving quality evaluation standards is the focus as well as the difficulty in its development and application. The traditional fingerprint analysis method has certain limitations for the quality evaluation of TCMC and the judgment of superior and inferior products. Therefore, how to optimize the similarity evaluation method of fingerprint and mine the sample data on the basis of fingerprint to fully reflect the essential differences in the quality of TCMC is the focus of this paper. In this paper, the quantified fingerprint method of integral efficiency (IEQFM) combined with dual control of peak area and retention time was proposed to optimize the similarity evaluation method. The HPLC, GC and UV fingerprints of 35 batches of compound liquorice tablets (CLTs) were evaluated as an example. The offline-wavelength switching method was explored to simplify the multi-wavelength data processing method. Secondly, the 11 components in the CLTs were accurately quantitatively analyzed by the single-linear assay multi-markers method and the standard curve method. Orthogonal partial least squares-discriminant analysis (OPLS-DA) analysis was used to find the signature components affecting the quality difference. For UV spectra, quantum fingerprints were obtained by preprocessing using characteristic concatenation method and quantitatively predicted using the partial least squares regression (PLSR) model ( p  > 0.7). Finally, the three fingerprint profiles were fused and evaluated after assigning weights to the different evaluation methods using the criteria importance through intercriteria correlation (CRITIC) method. In order to adapt to the evolving TCMC, method optimization is of great significance to improve the quality evaluation criteria. This study provides an exploratory method for the quality analysis of TCMC.