Rapid quality assessment of Gentianae Macrophyllae Radix based on near‐infrared spectroscopy and capillary electrophoresis

Abstract

The aim of this study was to establish a rapid quality assessment method for Gentianae Macrophyllae Radix (RGM) using near-infrared (NIR) spectra combined with chemometric analysis. The NIR spectra were acquired using an integrating sphere diffuse reflectance module, using air as the reference. Capillary electrophoresis (CE) analyses were performed on a model P/ACE MDQ Plus system. Partial least squares-discriminant analysis qualitative model was developed to distinguish different species of RGM samples, and the prediction accuracy for all samples was 91%. The CE response values at each retention time were predicted by building a partial least squares regression (PLSR) calibration model with the CE data set as the Y matrix and the NIR spectra data set as the X matrix. The converted CE fingerprints basically match the real ones, and the six main peaks can be accurately predicted. Transforming NIR spectra fingerprints into the form of CE fingerprints increases its interpretability and more intuitively demonstrates the components that cause diversity among samples of different species and origins. Loganic acid, gentiopicroside, and roburic acid were considered quality indicators of RGM and calibration models were built using PLSR algorithm. The developed models gave root mean square error of prediction of 0.2592% for loganic acid, 0.5341% for gentiopicroside, and 0.0846% for roburic acid. The overall results demonstrate that the rapid quality assessment system can be used for quality control of RGM.

A comprehensive strategy for quality marker discovery using chemical profiling combined with chemometrics, machine learning and network pharmacology analysis: taking Sinomenii Caulis as an example

“…By integrating the peak areas of all compounds and building a modelling, the cause of the differences between samples is demonstrated in terms of variable importance in projection (VIP) values or fold change (FC) values of differences. Zhang et al 28 established the OPLS-DA qualitative model for distinguishing different species of Gentianae Macrophyllae Radix. Li et al 29 used an untargeted metabolomics combined OPLS-DA model for discrimination of navel oranges from different geographical origins of China.…”