Quality Evaluation of Sanguisorbae Radix via Python Aided Optimization Fingerprint Chromatography Combined with Quantitative Analysis of Multi-components by Single Marker

A simple method was proposed to assess the quality of Sanguisorbae Radix based on Python-aided optimization fingerprint chromatography (FC) combined with quantitative analysis of multi-components by single marker (PA-FC/QAMS). The Python program quickly anticipated the mobile phase conditions for fingerprints, and the result yielded a chromatogram with good peak and resolution. The estimated chromatogram was similar to that obtained from the actual experiment. Furthermore, we used the mobile phase conditions predicted by Python to establish a fingerprint of 15 batches of Sanguisorbae Radix. and optimized it. Compared with the traditional trial-and-error method used to optimize the mobile phase conditions during the experiment, the efficient Python prediction method substantially reduced the number of experiments needed for optimum mobile phase conditions. In addition, exclusive mobile phase composition can be simulated according to different experimental instruments and chromatographic columns. Moreover, 15 batches of samples from different regions were classified by similarity analysis, cluster analysis, and factor analysis. This study shows that PA-FC/QAMS method can provide a simple and efficient approach for the quality evaluation of Sanguisorbae Radix.