To establish an HPLC fingerprint of Zuoyu’an San and conduct quality control by combining chemometrics and quantitative analysis of multi-components with a single-marker（QAMS）.

HPLC was used to establish the fingerprint spectra of 12 batches of Zuoyu’an San and perform similarity evaluation. Cluster analysis (CA)，principal component analysis (PCA)，and orthogonal partial least squares discriminant analysis (OPLS-DA) were combined to perform chemical pattern recognition analysis on different batches of Zuoyu’an San. At the same time，QAMS method was established using rhein as the internal standard reference. The relative correction factors between the internal reference and jatrorrhizine，palmatine，berberine，aloe-emodin，emodin，chrysophanol，and physcion were established. The external standard method (ESM) and QAMS method were used to determine jatrorrhizine，palmatine，berberine，aloe-emodin，rhein，emodin，chrysophanol，and physcion in Zuoyu’an San，respectively. To verify the reliability of QAMS metod results，the contents of 8 components of physcion were determined.

A total of 23 common peaks were calibrated in the fingerprint，and 8 components were identified by comparing with the mixed reference solution. The similarity between the samples and the control spectrum was between 0.952 and 0.994；CA divided 12 batches of Zuoyu’an San samples into 3 categories；PCA showed that there were certain differences in the chemical composition of different batches of Zuoyu’an San，and extracted 5 principal components that affected the quality evaluation of Zuoyu’an San；OPLS-DA screened out 13 potential marker components (VIP>1) among 23 common peaks that caused quality differences between different batches of Zuoyu’an San samples；There was no significant difference between the QAMS method calculated value and the ESM measured value.

The established HPLC fingerprint and multi-index component determination method was accurate and simple，and combined with chemometric methods，could provide reference for the quality control and evaluation of Zuoyu’an San.

To conduct odor analysis of the main effective components, namely volatile oils, contained in two varieties, Notopterygium incisum Ting ex H.T.Chang and Notopterygium franchetii H.de Boiss, to provide a feasible method for promptly and accurately distinguishing between the differences in volatile oils of these two varieties of Notopterygii Rhizoma et Radix. This enriches the traditional evaluation content and serves as a reference for assessing the quality of extracts predominantly governed by volatile oils.

The flavors of two samples of Notopterygii Rhizoma et Radix volatile oil were analyzed using electronic nose technology and sensory evaluation. The electronic nose data obtained were subjected to analysis and identification through principal component analysis (PCA) and linear discriminant analysis (LDA). Additionally，two nondestructive testing models-Fisher discrimination and multilayer perceptron (MLP) neural network discrimination were established for sample differentiation.

Sensory evaluation results indicated that pine resin flavor，cool flavor and woody flavor were the primary odor characteristics of both Notopterygii Rhizoma et Radix volatile oils. Additionally，the key flavor attribute influencing acceptance and differentiation was identified as spoiled yuba flavor，with the Notopterygium franchetii H. de Boiss volatile oil exhibiting a stronger presence of this attribute than the Notopterygium incisum Ting ex H. T. Chang volatile oil. The electronic nose results revealed that the nitrogen oxides’ response values in Notopterygium franchetii H. de Boiss volatile oil were significantly higher than those in Notopterygium incisum Ting ex H. T. Chang volatile oil. Meanwhile，the response values of hydrides，alcohol ether aldehydes，and ketones were slightly lower in Notopterygium franchetii H. de Boiss volatile oil compared to Notopterygium incisum Ting ex H. T. Chang volatile oil. The Fisher discriminant model demonstrated overall discrimination rates of 93.8% for the training set and 87.5% for the prediction set of the two volatile oils. In contrast，the MLP model achieved discrimination rates of 89.3% for the training set and 91.7% for the prediction set. Notably，the MLP model proved effective for identifying volatile oils，while the Fisher model exhibited greater suitability for discriminating volatile oils with broad-leaved characteristics.

The combination of artificial senses and intelligent senses can be characterized from both subjective and objective perspectives，elucidating the flavor differences between the two kinds of Notopterygii Rhizoma et Radix volatile oils. The established Fisher discriminant function and MLP discriminant models can rapidly and accurately distinguish between the two kinds of Notopterygii Rhizoma et Radix volatiles. This lays a preliminary foundation for quality control in Notopterygii Rhizoma et Radix volatiles and offers new ideas and directions.

To establish the substances benchmarks fingerprint of Daqinglong decoction and determine the content and transfer rate of 6 index components，and study the transfer rule of Daqinglong decoction substances benchmarks value，which laid the foundation for the study of Daqinglong decoction compound preparation and quality standard.

Fifteen batches of substances benchmarks of Daqinglong decoction were prepared，and the paste rate was determined. To determine the contents of ephedrine，pseudoephedrine，amygdalin，and cinnamic acid，HPLC method was performed on a Dikma Platisil C₁₈ column (250 mm×4.6 mm，5 μm). And 0.15% phosphoric acid water (A) and acetonitrile (B) were used as mobile phases with gradient elution at a flow rate of 1 mL·min^-1. The column temperature was 25 ℃，the detection wavelength was 207 nm，and the injection volume was 10 μL. To determine the contents of liquiritin and glycyrrhizic acid，0.05% phosphoric acid water (A) and acetonitrile (B) were used as mobile phases with gradient elution. The detection wavelength was 237 nm. The transfer rate was calculated，and the transfer analysis from the decoction pieces to the substances benchmarks was carried out.

The fingerprint similarities of 15 batches of Daqinglong decoction substances were ≥0.921. Fifteen common peaks were identified，including 8 from Ephedrae Herba，3 from Cinnamomi Ramulus，2 from Glycyrrhizae Radix et Rhizoma(fried)，and 2 from Armeniacae Semen Amarum(here). The contents of ephedrine，pseudoephedrine，amygdalin，liquiritin，cinnamic acid and glycyrrhizic acid were 0.94%-1.30%，0.56%-1.02%，0.70%-1.25%，0.18%-0.32%，0.05%-0.10% and 0.46%-1.23%，respectively. The transfer rates were 41.67%-59.47%，42.66%-59.74%，17.59%-36.34%，21.48%-44.75%，31.06%-48.89% and 12.95%-25.15%，respectively. The paste yields of substances benchmarks were 11.98%-13.38%.

The fingerprint combined with the determination of paste rates and index component contents are used to study the quantitative value transfer of Daqinglong decoction substance benchmarks，and initially establish a scientific and stable substance benchmarks quality evaluation method，which can provide reference for the future research on compound preparation.

To optimize the extraction process of Impatientis Balsaminae Caulis in the treatment of degenerative osteoarthritis based on network pharmacology，mixture weighting and response surface method.

Network pharmacology and LC-MS techniques were used to determine the active ingredients of Impatientis Balsaminae Caulis in the treatment of osteoarthritis，and they were taken as evaluation indexes. The extraction process of Impatientis Balsaminae Caulis was optimized by analytic hierarchy process（AHP）-criteria importance through intercriteria correlation(CRITIC) mixed weighting method and Box-Behnken response surface design.

Scopoletin，quercetin and kaempferol were the active ingredients in the treatment of osteoarthritis. The optimal extraction process was to add 5 times of 82% ethanol and reflux twice，35 min each time.

The treatment of osteoarthritis can be achieved by regulating target genes and related signaling pathways through various active components. The weight coefficient determined by AHP-CRITIC hybrid weighting method is objective and reasonable，and the optimized extraction process is stable and feasible.

To establish a quantitative model to in-line in situ determination of the water content of domperidone tablets particles in fluidized bed granulation drying process.

Using micro near infrared spectrometer，in-line collected near infrared（NIR） spectrum of domperidone tablets particles in granulation drying process，took sample at different drying time to acquire sample in different level of water content. The pretreatment method of first derivative（FD），multiplicative scatter correction (MSC) and Savitzky-Golay smoothing were selected. The modeling band was 1 100-1 600 nm，model was established by partial least square（PLS） method，and validation was executed including accuracy，precision，specificity，linearity and range，robustness.

The root mean square error of cross validation(RMSECV) was 0.079 7，the coefficient of determination(R²) was 0. 994 5，the root mean square error of prediction(RMSEP) was 0.092 3，the R² was 0.986 0. All validation results met predefined criteria.

It is feasible for using micro NIR spectrometer to in-line measure the water content of domperidone tablets particles which provides an experimental basis for the in-line application of process analysis technology in pharmaceutical industry.

To establish a specific polymerase chain reaction(PCR) method based on SNP loci for molecular identification of Eupolyphaga Steleophaga formula granules and its counterfeit.

By analyzing the cytochrome oxidase Ⅰ (COⅠ) sequences of Eupolyphaga Steleophaga and its common forgeries，the SNP mutation sites of Eupolyphaga Steleophaga were searched and specifi cprimers were designed，which were TBC-F(5’-TTCTTGTTGGCAAGCAGTATAAT-3’) and TBC-R(5’-AACTACTGCTCAAACAAATAATGGA-3’). Three-step method was used to amplify specific polymerase chain reaction (PCR) and the optimal PCR reaction system was determined by optimizing the PCR reaction procedure. At the same time，in order to ensure the accuracy of the test results，the PCR amplified products were conducted by generation sequencing.

The PCR method with the annealing temperature of 55-57 ℃ and 37 cycles produced a single band at 237 bp for 21 batches of Eupolyphaga Steleophaga，20 batches of standard decoction and 19 batches of its dispensing granules，while it produced no band for the adulterant or the negative control. The experimental result was consistent with the result of Sanger sequencing，which was Eupolyphaga sinensis.

The established specific PCR method can accurately identify the medicinal materials，and standard decoction freeze-dried powder of Eupolyphaga Steleophaga，as well as final products of dispensing granules. It provides a reference for research on the quality standards of Eupolyphaga Steleophaga dispensing granules.

To establish a laser diffraction method for the determination of particle size distribution of budesonide suspension for inhalation.

The Master sizer 3000 laser size analyzer with the Hydro SV wet sampler was used. The sample solution should be immediately measured after ultrasonication. The measurement conditions were as follows：refractive index of budesonide was 1.592，absorbency of budesonide was 0.01，dispersing medium was saturated solution of budesonide containing 0.5% Tween 80 with refractive index of 1.33，stirring rate was 1 000 r·min^-1，and laser obscurations were 6%-12%.

RSD of D₅₀ was lower than 2%，RSDs of D₁₀ and D₉₀ were both lower than 5%．The particle size distribution D₉₀ values of 3 batches of budesonide suspension for inhalation were less than 5 μm.

The method is simple，accurate and producible，which is suitable for the particle size detection of budesonide suspension for inhalation.

To establish an HPLC method for the ratio of the enantiomer in fluralaner.

The chromatographic separation was performed on chiral chromatography column CHIRALPAK AD-H (250 mm×4.6 mm，5 μm). The mobile phase consisted of n-hexane-anhydrous ethanol (60：40) and the flow rate was 1.0 mL·min^-1. The detection wavelength was 265 nm and the column temperature was room temperature.

Under this chromatographic condition，three batches of samples were determined，the proportions of R-fluralaner and S-fluralaner were 1：1. The calibration curves of R-fluralaner and S-fluralaner good linearities at the range of 80.288-187.338 μg·mL^-1(r=0.999 7) and 81.902-191.104 μg·mL^-1 (r=0.999 9)，respectively. The average recoveries were 100.6% and 100.8%.

The method is accurate，reproducible and can be used for the ratio of enantiomers in fluralaner racemate.

To establish an UPLC-MS/MS method to measure the content of 18 characteristic components in Fengshiantai tablets，and conduct chemometric analysis on the measurement results to provide a basis for quality control.

The components were separated on Waters BEH C₁₈ (100 mm×2.1 mm，1.7 μm) column with methanol and 0.1% formic acid aqueous solution as the mobile phase by gradient elution at a flow rate of 0.2 mL·min^-1. The column temperature was set at 40 ℃. The injection volume was 1 μL. The detection was carried out by electrospray ionization(ESI)，and electron spray ionization source was adopted in positive ion detection with multiple reaction monitoring mode(MRM).

The established methods showed a good linear relationship in a certain range (r≥0.998 3)，whose average recovery rates of samples were 90.2%-101.7% with the RSD of 1.1%-4.9%. The content of ephedrine(calculated as ephedrine hydrochloride)，chlorogenic acid，syringin，strychnine，brucine，berberine(calculated as berberine hydrochloride)，naringin，cinnamaldehyde，ginsenoside Re，ginsenoside Rg₁，epimedin C，icariin，asperosaponin Ⅵ，glycyrrhizic acid(calculated as glycyrrhizic acid ammonium salt)，ginsenoside Rb₁，notopterol，isoimperatorin in 39 batches of samples were 20.23-141.75 μg per tablet，10.50-121.69 μg per tablet，215.24-697.34 μg per tablet，8.46-42.42 μg per tablet，135.24-363.32 μg per tablet，91.29-242.87 μg per tablet，19.65-80.31 μg per tablet，22.57-105.05 μg per tablet，4.69-86.88 μg per tablet，10.32-241.15 μg per tablet，3.36-65.07 μg per tablet，10.79-390.69 μg per tablet，6.51-86.29 μg per tablet，4.10-89.75 μg per tablet，195.55-566.37 μg per tablet，96.98-448.95 μg per tablet，0-132.41 μg per tablet，7.09-232.71 μg per tablet. The results of chemometrics showed that 39 batches of samples from 5 production enterprises were classify into three categories，and there were certain differeces in sample quality among different production enterprises，eight differential markers(naringin，glycyrrhizic acid，isoimperatorin，berberine，syringin，ephedrine，pseudoephedrine，strychnine) that caused quality differences were found.

The established method is simple and feasible，combined with chemometric methods，and can be used for quality evaluation of Fengshiantai tablets.