Acousto-optic tunable filter near-infrared spectroscopy for in-line monitoring liquid-liquid extraction of Gardenia jasminoides Ellis based on statistical analysis.

This study aimed to monitor liquid-liquid extraction of Gardenia jasminoides Ellis (Zhizi in Chinese) using in-line near-infrared spectroscopy. Shanzhiside (SZS), deacetyl asperulosidic acid methyl ester (DAAME), genipin-1-ß-D-gentiobioside (GG), geniposide (GS), total acids (TA) and soluble solid content (SSC) were selected as quality control indicators, and measured by reference methods. Both partial least-squares regression (PLSR) and back propagation artificial neural networks (BP-ANN) were applied to create models to predict the content of above indicators. Paired-samples t-test and nonparametric test were used to compare differences in predictive values between two models of each indicator. Relative standard error of prediction (RSEP) and mean absolute percentage error (MAPE) were used to evaluate the predictive accuracy of the established models. The results showed that there was no significant difference in predicting DAAME, GS and TA between two models. However, PLSR model gave better accuracy in predicting GG and SZS than BP-ANN model. The BP-ANN model of SSC was better than PLSR model. This study shows that NIR spectroscopy can be used for rapid and accurate analysis of quality control indicators in the liquid-liquid extraction of Zhizi. Simultaneously, this study can serve as technical support for the application of NIR spectroscopy in the industrial production process.

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

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.

[Determination of eleven major components and fingerprint chromatography for Reduning injection by UPLC].

A reliable method for simultaneous determinition of eleven representative components (neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C, shanzhiside, geniposidic acid, genipin-1-ß-D-gentiobioside, geniposide and secoxyloganin) in combination of chromatographic fingerpint analysis for Reduning injection was developed by ultra high-performance liquid chromatography (UPLC). The method was performed on an Agilent ZORBAX SB-C18 anlytical column (3. 0 mm x 100 mm, 1. 8 µm) with a guard column of Agilent UPLC Guard ZORBAX SB-C18 (3.0 mm x 5 mm) at the column temperature of 30 °C. The gradient mobile phase consisted of acetonitrile (A)-0. 1% phosphoric acid (B) with a flow rate of 0. 4 mL . min-1. The injection volumn was 2 µL. The detection wavelengths were set at 324 nm and 238 nm for quantit tive analysis and 225 nm for fingerpint chromatography. Neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C, shanzhiside, geniposidic acid, genipin-1-ß-D-gentiobioside, geniposide and secoxyloganin were baseline seperated with good linearity relationships (r >0. 999) between concentration and peak areas over the linear ranges. The average recoverys of the investigated compounds were 103.5%, 100. 2%, 103. 3%, 102. 8%, 101. 3%, 102. 8%, 97. 36%, 99. 62%, 98. 16%, 102. 8%, 99. 27%, respectively. Reduning injection of forty-five batches was analyzed by UPLC finge print chromatography. Thirty batches were selected to generate the reference fringerprint chromatography with fourteen common peaks. The similarity values between the reference fringerprint chromatography and the remaining fifteen batches were higher than 0. 99. The developed method was fast, accurate and sensitive. It could be used as a reference for the quality control of multiple components determination and fingerprint chromatography for Reduning injection in future.

On-line quantitative monitoring of liquid-liquid extraction of Lonicera japonica and Artemisia annua using near-infrared spectroscopy and chemometrics.

BACKGROUND: Liquid-liquid extraction of Lonicera japonica and Artemisia annua (JQ) plays a significant role in manufacturing Reduning injection. Many process parameters may influence liquid-liquid extraction and cause fluctuations in product quality. OBJECTIVE: To develop a near-infrared (NIR) spectroscopy method for on-line monitoring of liquid-liquid extraction of JQ. MATERIALS AND METHODS: Eleven batches of JQ extraction solution were obtained, ten for building quantitative models and one for assessing the predictive accuracy of established models. Neochlorogenic acid (NCA), chlorogenic acid (CA), cryptochlorogenic acid (CCA), isochlorogenic acid B (ICAB), isochlorogenic acid A (ICAA), isochlorogenic acid C (ICAC) and soluble solid content (SSC) were selected as quality control indicators, and measured by reference methods. NIR spectra were collected in transmittance mode. After selecting the spectral sub-ranges, optimizing the spectral pretreatment and neglecting outliers, partial least squares regression models were built to predict the content of indicators. The model performance was evaluated by the coefficients of determination (R (2)), the root mean square errors of prediction (RMSEP) and the relative standard error of prediction (RSEP). RESULTS: For NCA, CA, CCA, ICAB, ICAA, ICAC and SSC, R (2) was 0.9674, 0.9704, 0.9641, 0.9514, 0.9436, 0.9640, 0.9809, RMSEP was 0.0280, 0.2913, 0.0710, 0.0590, 0.0815, 0.1506, 1.167, and RSEP was 2.32%, 4.14%, 3.86%, 5.65%, 7.29%, 6.95% and 4.18%, respectively. CONCLUSION: This study demonstrated that NIR spectroscopy could provide good predictive ability in monitoring of the content of quality control indicators in liquid-liquid extraction of JQ.