This study was aimed to establish an HPLC method for the determination of liquiritin, isoliquiritin, liquiritigenin and glycyrrhizic acid in roots and knotty rhizome of Glycyrrhiza uralensis. The analysis was performed on a Diamonsil C18 column (250 mm í 4.6 mm, 5 μm) by using a gradient elution with mobile phase of water, phosphoric acid, acetonitrile at the flow rate of 1.0 mL·min-1. The detection wavelength was 276 nm (0~18 min), 360 nm (18~24 min), 276 nm(24~30 min), and 250 nm (30~65 min). The column temperature was set at 30℃. The results showed that the linear range of iquiritin, isoliquiritin, liquiritigenin, glycyrrhizic acid was 0 . 108 5~1 . 085、0 . 016 8~0 . 168、0 . 0049 4~0 . 049 4、0 . 407~4 . 07μg , respectively . The average recoveries of four constituents were 96.61%~100.89%, with RSD ≤ 0.81%. The contents of four constituents in roots of five batches were 0.513%, 0.072 9%, 0.048 4%, and 1.945%, respectively. Contents of four constituents in knotty rhizome from two batches were 0.456%, 0.063 6%, 0.036 2%, and 1.630%, respectively. It was concluded that there was good linear relationship between the response and concentration. Contents of four constituents in knotty rhizome were basically the same as those in the roots. The knotty rhizome can be used as raw material for the extraction of active components.

Normal bacteria purification method was used in this study. Pseudomonas trivialis and Neurospora te-trasperma were screened from residues of traditional Chinese medicine (TCM). The fermentation conditions and flocculating function were also studied. The results showed that the selected strains grow well in the residues of TCM and their products can flocculate kaolin suspension solution. Based on these, we explored effects of various factors on the flocculated results in order to reveal the best fermentation condition among different combinations.

For solving the problem that common similarity algorithms are insensitive to the relative differences of different chromatographic fingerprints data, this paper improved the algorithms of improved extent similarity), which named new improved extent similarity. It was confirmed that new improved extent similarity could distinguish) the samples with the same total difference and different distribution of relative differences. by applying) the similarity algorithm to simulated data and the chromatographic fingerprints of nine batches Rukuaixiao tablets. New improved extent similarity could reflect the relative differences of peak areas more sensitively when the relative differences of common peak areas was less than 100%. The result indicates that new improved extent similarity can reflect the large deviation from the established proportion of components, which can be applied to the control of the fluctuation of the established proportion of effective components and the process quality control of Traditional Chinese Medicine .

Objective:To establish a method for determination of total flavonoids,eriodictyol-7-methylether and isosakuranetin in Artemisia halodendron seeds.Method:The content of total flavonoids was determined by ultraviolet spectrophotometry at 506 nm,and compared with eriodictyol-7-methylether as a reference substance;the content of eriodictyol-7-methylether and isosakuranetin was determined by RP-HPLC,the analytical column was YMC-Pack ODS-A column(5?m,4.6 mm?250 mm),the mobile phase consisted of acetonitrile-0.2% phosphoric acid aqueous(35:65) at a ow rate of 1.0 ml?min-1,and the detection wavelength was 288 nm.Results:The linear range of total avonoids was 0.1228--0.7368mg(r=0.9991) and the average recovery(n=6) was 100.5%(RSD=1.41%);the linear ranges of eriodictyol-7-methylether and isosakuranetin were 3.016--30.16?g/ml(r=0.9999)and 3.304--33.04?g/ml(r=0.9999),respectively;the average recoveries(n=6) were 98.9%(RSD=1.20%)and 96.8%(RSD=1.38%),respectively.Conclusion:The methods of UV and HPLC are simple,accurate with good reproducibility and suitable for determination of total avonoids,eriodictyol-7-methylether and isosakuranetin.

Near infrared chemical imaging (NIR-CI) is an emerging technology for rapidly analyzing the critical quality attribute of Chinese materia medica (CMM). It integrates NIR spectroscopy with chemical imaging. In this paper, it provided a systematic introduction to NIR-CI, such as the core part of instrument, the reliability, transformation, analysis and application of high-dimensional data acquisition. In addition, current studies of NIR-CI application in pharmaceutical field were analyzed. Finally, future opportunities and challenges of NIR -CI applications in the quality control of CMM preparation were prospected.

Abstract: The design space of the high shear wet granulation process was established and validated within the framework of quality by design (QbD). The system of microcrystalline cellulose-de-ioned water was used in this study. The median granule size and bulk density of granules were identified as critical quality attributes. Plackeet-Burmann experimental design was used to screen these factors as follows: dry mixing time, the impeller and chopper speed of dry mixing, water amount, water addition time, wet massing time, the impeller and chopper speed of wet massing and drying time. And the optimization was implemented with the central composite experimental design based on screened critical process parameters. The design space of the high shear wet granulation process was established based on the quadratic polynomial regression model. Since the P-values of both models were less than 0.05 and values of lack of fit were more than 0.1, the relationship between critical quality attributes and critical process parameters could be well described by the two models. The reliability of design space, illustrated by overlay plot, was improved with the addition of 95% confidence interval. For those granules whose process parameters were in the design space, the granule size could be controlled within 250 to 355 μm, and the bulk density could be controlled within a range of 0.4 to 0.6 g x cm(-3). The robustness and flexibility of the high shear wet granulation process have been enhanced via the establishment of the design space based on the QbD concept.

This study was aimed to optimize bio-solid bidirectional fermentation conditions of Trametes robiniophia Murr. for rhubarb. Sulphuric acid-phenol colorimetry, magnesium-methanol colorimetry, and HPLC were used in the
content determination of polysaccharide, total anthraquinone, and 4 free anthraquinones. The drying rate and con-sumption rate were combined as indicators for the optimization of technical parameters such as medicinal dosage, temperature and amount of water. The results showed that when using 500 mL conical flask, the best fermentation conditions were medicinal dosage of 10 g, the temperature of 34℃, adding water of 120%. It was concluded that bidirectional fermentation of rhubarb increased the content of free anthraquinones. Among them, the content of chrysophanol with anti-oxidation effect increased significantly. The decreasing of combined anthraquinone can relieve the severe laxative effect of rhubarb.

ToprovidethemethodologyforrapidqualityevaluationofLonicerajaponica,wehaveestablished the stable quantitative model of near infrared spectroscopy ( NIR) . The performance of Bagging partial least squares (Bagging-PLS) model and Boosting partial least squares (Boosting-PLS) model was compared with that partial least squares ( PLS ) model based on the NIR data of ethanol precipitation process of Lonicera japonica. On this basis, the performance of these two models after variables selection was also studied by the methods of siPLS ( synergy interval partial least squares ) and CARS ( competitive adaptive reweighted sampling) . The experimental results showed that the prediction performance of Bagging-PLS and Boosting-PLS models was superior to PLS model with the latent factor of 10 . The band of 820-1029 . 5 nm and 1030-1239. 5 nm for the first batch was selected by the method of siPLS. In addition, the band of 820-1029. 5 nm and 1030-1239. 5 nm was selected for the second batch sample in the same method. Furthermore, the method of CARS was taken to select variables for the two batches samples with 5-fold cross-validation and 10-fold cross-validation. And the lowest RMSECV( root mean square error of cross-validation) values were used to take subset. Compared to the model performance without the method of CARS, the RMSEP value of the Bagging-PLS model and Boosting-PLS model for the concentration of chlorogenic acid reduced by 0 . 02-0 . 04 g/L and rp(correlation coefficient of prediction)value increased by 4%-5%. Generally, Bagging-PLS and Boosting-PLS could be regarded as rapid prediction methodsfor NIR quantitative models of ethanol precipitation process of Lonicera japonica.

This study was aimed to optimize the near infrared (NIR) variable selection method based on multivariate detection limit (MDL). Using Qing-Kai-Ling (QKL) injection as object, three variable selection methods (interval par-tial least-squares, iPLS; backward interval partial least squares, BiPLS; moving window interval partial least squares, mwPLS) were used to establish the PLS models of baicalin in QKL injection, respectively. The prediction ability of different variable selection method was compared. MDL of all models were calculated in contrast to the MDL value of full spectra PLS model, to select optimal variable selection method. The results showed that different variable selec-tion methods had different prediction ability. Among them, iPLS had the best performance which determination coef-ficient of prediction (Rpre2) and the root mean square errors of prediction (SEP) were 0.996 5 and 602.3 μg·mL-1, re-spectively. All MDLs of different variable selection methods were reduced compared with the full spectra PLS model. The value of iPLS was the lowest comes to be 1.19 μg·mL-1. The results above indicated that the best variable se-lection method for baicalin in QKL injection was iPLS. MDL theory took the error of calibration and validation set and the leverage of external sample into account, which can comprehensively evaluate model detection performance compared to the classic chemical indicator parameters. This method was particularly suitable for the variable selec-tion method optimization of NIR quantitative model of low concentration sample such as Chinese herbal medicine.

A new non-destructive and rapid method was developed to discriminate the coating process of Rukuaixiao tablets mainly based the support vector machine (SVM) with the near-infrared spectroscopy (NIRs). After the samples that differ in the sugar-coat were acquired, the sample set partitioning based on joint x-y distance (SPXY) method was used to select the training sets and internal principal component analysis (internal PCA) was to select the optimal wavelength. The discrimination model was developed based on support vector machine (SVM), and varieties of pre-processing methods were compared. The results showed that the accuracy of the prediction set was 98.81%. It is concluded that the accuracy of the method is high to use for the quality evaluation of tablet's coating process.
Algorithms ; Clinical Laboratory Techniques ; standards ; Clinical Protocols ; standards ; Drugs, Chinese Herbal ; analysis ; Medicine, Chinese Traditional ; standards ; Medicine, East Asian Traditional ; methods ; Principal Component Analysis ; Spectrophotometry, Infrared ; methods ; Spectroscopy, Near-Infrared ; methods ; Tablets ; analysis