Rapid measurement of epimedin A, epimedin B, epimedin C, icariin, and moisture in Herba Epimedii using near infrared spectroscopy.

In this work, near infrared (NIR) spectroscopy was used in combination with chemometrics to determine the epimedin A, epimedin B, epimedin C, icariin, and moisture contents of Herba Epimedii. The variable selection method genetic algorithm (GA) and regression tool support vector machine (SVM) were used to improve the model performance. Four different calibration models, namely Full-PLS, GA-PLS, Full-SVM, and GA-SVM, were established, and their performances in terms of prediction accuracy and model robustness were systemically studied and compared. In conclusion, the performances of the models based on the efficient variables selected through GA were better than those based on full spectra, and the nonlinear models were superior over the linear models. In addition, the GA-SVM model demonstrated the optimal performance in predicting five quality parameters (viz. epimedin A, epimedin B, epimedin C, icariin, and moisture). For GA-SVM, the determination coefficient (Rp2), root-mean-square error (RMSEP), and residual predictive deviation (RPD) for the prediction set were 0.9015, 0.0268%, and 2.20 for epimedin A; 0.9089, 0.0656%, and 3.08 for epimedin B; 0.9056, 0.1787%, and 3.18 for epimedin C; 0.8192, 0.0657%, and 2.26 for icariin; and 0.9367, 0.2062%, and 4.12 for moisture, correspondingly. Results indicated that NIR spectroscopy coupled with GA-SVM calibration can be used as a reliable alternative strategy to measure the epimedin A, epimedin B, epimedin C, icariin, and moisture contents of Herba Epimedii because this technique is fast, economic, and nondestructive compared with traditional chemical methods.