Quality control in liquid-liquid extraction of Reduning injection using near-infrared spectroscopy technology

Abstract

Quantitative models were established to analyze the content of chlorogenic acid and soluble solid content in the liquid-liquid extraction of Reduning injection by near-infrared (NIR) spectroscopy. Seven batches of extraction solution from the liquid-liquid extraction of Lonicerae Japonicae Flos and Artemisiae Annuae Herba were collected and NIR off-line spectra were acquired. The content of chlorogenic acid and soluble solid content were determined by the reference methods. The partial least square (PLS) and artificial neural networks (ANN) were used to build models to predict the content of chlorogenic acid and soluble solid content in the unknown samples. For PLS models, the R2 of calibration set were 0.9872, 0.9812, RMSEC were 0.1533, 0.7943, the R2 of prediction set were 0.9837, 0.9733, RMSEP were 0.2464, 1.2594, RSEP were 3.25%, 3.31%, for chlorogenic acid and soluble solid content, respectively. For ANN models, the R2 of calibration set were 0.9903, 0.9882, RMSEC were 0.0974, 0.4543, the R2 of prediction set were 0.9868, 0.9699, RMSEP were 0.1920, 0.9427, RSEP were 2.61%, 2.75%, for chlorogenic acid and soluble solid content, respectively. Both the RSEP values of chlorogenic acid and soluble solid content were lower than 6%, which can satisfy the quality control standard in the traditional Chinese medicine production process. The RSEP values of ANN models were lower than PLS models, which indicated the ANN models had better predictive performance for chlorogenic acid and soluble solid content. The established method can rapidly measure the content of chlorogenic acid and soluble solid content. The method is simple, accurate anc reliable, thus can be used for quality control of the liquid-liquid extraction of Reduning injection.