Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics

There are some problems in the quality control of Epimedii Folium (leaves of Epimedium brevicornum Maxim.), such as the mixed use of Epimedii Folium from different harvesting periods and regions, incomplete quality evaluation, and time-consuming analysis methods.

Near-infrared (NIR) spectroscopy was conducted to establish a rapid overall quality evaluation method for Epimedii Folium.

Quantitative models of the total solid, moisture, total flavonoid, and flavonol glycoside (Epimedin A, Epimedin B, Epimedin C, Icariin) contents of Epimedii Folium were established by partial least squares regression (PLSR). The root mean square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of models. The qualitative models of Epimedii Folium from different geographic origins and harvest periods were established based on K-nearest neighbor (KNN), back-propagation neural network (BPNN), and random forest (RF). Accuracy and Kappa values were used to evaluate the performance of models. A new multivariable signal conversion strategy was proposed, which combines NIR spectroscopy with the PLSR model to predict the absorbance values of retention time points in the high-performance liquid chromatography (HPLC) fingerprint to obtain the predicted HPLC fingerprint. The Pearson correlation coefficient and cosine coefficient were used to evaluate the similarity between real and predicted HPLC fingerprints.

Qualitative models, quantitative models, and the similarity between real and predicted HPLC fingerprints are satisfactory.

The method serves as a fast and green analytical quality evaluation method of Epimedii Folium and can replace traditional methods to achieve the overall quality evaluation of Epimedii Folium.