Method for determining content of paeoniflorin in paeoniflorin and liquorice granules

Abstract

The invention relates to the technical field of medicine detection, and particularly discloses a method for determining the content of paeoniflorin in paeonia lactiflora and liquorice granules, which comprises the following steps: identifying liquorice and radix paeoniae alba in the paeonia lactiflora and liquorice granules by a thin-layer chromatography technology; determining the content of paeoniflorin in the Chinese herbaceous peony and liquorice granules based on a high performance liquid chromatography technology; the determination method comprises the following steps: preparing a reference solution; preparation of a test solution: taking paeonia lactiflora and liquorice granules, grinding, taking 0.1 g, precisely weighing, placing in a conical flask with a plug, precisely adding 50 mL of a solvent, sealing the plug, weighing, carrying out ultrasonic treatment for 30 minutes, cooling to 26-34 DEG C, weighing again, complementing the lost weight with the solvent, shaking uniformly, filtering, and taking a subsequent filtrate to obtain the test solution; and precisely sucking 10 microliters of the reference solution and 10 microliters of the test solution respectively, injecting into a liquid chromatograph, and determining. The method is high in specificity, stability, repeatability, intermediate precision, accuracy and the like all meet regulations, and the durability of chromatographic conditions is good.

Description

Technical Field

[0001] The invention relates to the technical field of drug detection, and in particular to a method for determining the content of paeoniflorin in Shaoyaogancao granules. Background Art

[0002] Shaoyao Gancao Granule has multiple effects such as relieving spasm, relieving cough, nourishing yin and liver, clearing away heat and detoxifying, moistening intestines and relieving constipation. Paeoniflorin is one of the main active ingredients in Shaoyao Gancao Granule, which has multiple pharmacological effects such as analgesia, anti-inflammatory, and antioxidant. By accurately measuring the content of paeoniflorin, it can be ensured that each batch of drugs meets the established quality standards, thereby ensuring the efficacy and safety of the drugs. Therefore, the present invention proposes a method for determining the content of paeoniflorin in Shaoyao Gancao Granule.

[0003] In the prior art, the method for determining the content of paeoniflorin has poor stability and high repeatability, and its intermediate precision and accuracy are both low, and it cannot be well promoted and applied. Therefore, the present invention proposes a method for determining the content of paeoniflorin in Shaoyao Gancao granules. Summary of the invention

[0004] In response to the technical problems mentioned in the background technology, the present invention proposes a method for determining the content of paeoniflorin in Shaoyao Gancao granules.

[0005] The present invention adopts the following technical solution: A method for determining the content of paeoniflorin in Shaoyaogancao granules comprises the following steps: S1. Identification of Shaoyaogancao Granules by thin layer chromatography; S2. Determination of the content of paeoniflorin in Shaoyaogancao granules based on high performance liquid chromatography technology; Wherein, step S2 comprises the following steps: Selection of chromatographic operating conditions and parameters: Chromatographic column: C18 column, 4.6mmx250mm, 5μm; Mobile phase A was acetonitrile: mobile phase B was 0.1% phosphoric acid aqueous solution, and the elution conditions were: 0 to 8 min, 13.4% to 16.8% mobile phase A, 86.6% to 83.2% mobile phase B; 8 to 16 min, 16.8% to 22.3% mobile phase A, 83.2% to 77.7% mobile phase B; 16 to 24 min, 22.3% to 25.7% mobile phase A, 77.7% to 74.3% mobile phase B; 24 to 32 min, 25.7% to 19.2% mobile phase A, 74.3% to 80.8% mobile phase B; 32 to 38 min, 19.2% to 13.4% mobile phase A, 80.8% to 86.6% mobile phase B; 38 to 45 min, 13.4% mobile phase A, 86.6% mobile phase B; Flow rate: 0.72mL / min-1.31mL / min; injection volume: 9.45μl-10.55μl; Column temperature intelligent control system: the temperature is set at 28.5℃-31.5℃, and the fluctuation is controlled within ±0.1℃; Dual wavelength detection system: main wavelength 230nm, reference wavelength 270nm, diode array detector combined with mass spectrometer, scanning range: 190-400nm; Preparation of reference solution: Take the paeoniflorin reference substance, weigh accurately, and add methanol to make a solution containing 80 μg per 1 mL. Preparation of the test solution: Take the Shaoyao Gancao granules, grind them into powder, take 0.1g, weigh accurately, and place it in a stoppered conical bottle. Use the temperature gradient ultrasonic extraction method. The power of ultrasonic treatment is 300w and the frequency is 40kHz. The specific steps are as follows: Step 1: Ultrasonic extraction at 25°C for 10 minutes; Step 2: Ultrasonic extraction at 35°C for 10 minutes; Step 3: Ultrasonic extraction at 45°C for 10 minutes; The extraction solvent is a mixed solution of 70% ethanol: methanol: water = 6: 3: 1, 50 mL is accurately added, plugged, weighed, cooled to 26℃-34℃, weighed again, and the lost weight is supplemented with the same solvent, shaken, filtered through a 0.45μm filter membrane, and the filtrate is purified through an ultrafiltration membrane with a molecular weight cutoff of 3000Da to obtain; Determination method: Use an automatic sample injector to control the temperature at 4±0.5℃, accurately aspirate 10μl of the reference solution and the test solution respectively, inject into the liquid chromatograph, measure, record the chromatogram, and use artificial intelligence-assisted peak area automatic integration algorithm for data processing.

[0006] Preferably, the step of using an artificial intelligence-assisted peak area automatic integration algorithm for data processing includes: performing wavelength scanning on the chromatogram at a sampling interval of 0.001 min through a diode array detector, collecting a three-dimensional data matrix in the range of 190-400 nm, and separating and extracting signal data of a main wavelength of 230 nm and a reference wavelength of 270 nm from the data matrix; performing a 9-point sliding average calculation based on the signal data of the main wavelength of 230 nm, performing a weighted average of the signal intensities of the 4 points before and after each data point, and obtaining a main signal baseline characteristic value; using the signal data of the reference wavelength of 270 nm, taking a window of 0.1 min The local variance was calculated based on the width, and the baseline eigenvalues ​​of the main signal were dynamically calibrated to obtain the baseline correction parameters. A cubic spline function was constructed based on the baseline correction parameters, and the main wavelength signal was corrected in sections, with each section being 0.5 min long, to obtain the chromatographic baseline correction data. The first-order and second-order derivatives were calculated based on the chromatographic baseline correction data, and the boundary of the paeoniflorin peak was located based on the maximum point and zero-crossing point of the derivative value to obtain the starting and end point data of the peak. The integration interval was finely divided at intervals of 0.002 min using the starting and end point data of the peak, and the area with a signal intensity greater than twice the baseline was sampled to obtain the integration range of the paeoniflorin peak.

[0007] Preferably, the signal data with a main wavelength of 230 nm are subjected to a 9-point sliding average calculation, and the signal intensities of the 4 points before and after each data point are weighted averaged to obtain the main signal baseline characteristic value, including: setting three signal intensity thresholds of high, medium and low through the signal data with a main wavelength of 230 nm, which are 8 times, 4 times and 2 times of the baseline intensity respectively, and collecting data from different signal areas to obtain a layered signal sequence; assigning a weight coefficient to each data point and the 4 points before and after it in the layered signal sequence to obtain a weighted calculation parameter; performing a moving sum operation on the 9-point data through the weighted calculation parameter, dividing the calculation result by the sum of the weight coefficients to obtain an initial average value; establishing an adaptive adjustment range according to the deviation value between the initial average value and the original data, and performing secondary weighting on the data points with a deviation exceeding 0.2 mAU to obtain a correction coefficient; calibrating the initial average value through the correction coefficient to generate a target weighted average result to obtain a baseline characteristic value; constructing a data index through the baseline characteristic value, recording the weight distribution and calculation process of each data point, and obtaining the main signal baseline characteristic value.

[0008] Preferably, the first-order and second-order derivatives are calculated according to the chromatographic baseline correction data, and the boundary of the paeoniflorin peak is located in combination with the maximum point and the zero-crossing point of the derivative value to obtain the starting point and end point data of the peak, including: setting a calculation interval of 0.002min through the chromatographic baseline correction data, performing a differential operation on adjacent data points, and obtaining a first-order derivative value sequence; performing a secondary differential calculation on the first-order derivative value sequence, quantifying the derivative value change of each data point and its adjacent points, and obtaining a second-order derivative value sequence; finding the point with the largest signal change rate through the first-order derivative value sequence, and performing a differential operation on the adjacent data points; The point where the positive change rate is greater than 0.5mAU / min is marked as the rising edge, and the peak starting range is obtained; the inflection point position is determined according to the second-order derivative value sequence, and the position where the second-order derivative crosses the zero point and the absolute value of the first-order derivative is greater than 0.2mAU / min is marked as the key point to obtain the peak contour feature point; the peak contour feature point is extended to both sides to the point where the signal intensity drops to 105% of the baseline, and the extended interval is evaluated for noise to obtain the boundary positioning result; the starting point and the end point of the integration interval are determined by the boundary positioning result, the peak integration range data is generated, and the starting point and the end point data of the peak are obtained.

[0009] Preferably, the starting point and endpoint data of the peak are used to finely divide the integration interval at intervals of 0.002 min, and the area with a signal intensity greater than 2 times the baseline is sampled to obtain the integration range of the paeoniflorin peak, including: stratifying the integration interval through the starting point and endpoint data of the peak, dividing the signal intensity into three response areas of high, medium and low according to 8 times the baseline, 4 times the baseline and 2 times the baseline, using a sampling interval of 0.001 min for the high response area, a sampling interval of 0.002 min for the medium response area, and a sampling interval of 0.004 min for the low response area to obtain a stratified sampling sequence; performing linear interpolation calculation on adjacent sampling points according to the stratified sampling sequence, inserting encrypted points in the area where the signal change rate is greater than 0.8 mAU / min, and diluting the points in the area where the change rate is less than 0.2 mAU / min to obtain an optimized sampling point distribution; determining the key points of the peak shape through the optimized sampling point distribution, and diluting the key points of the peak shape with an absolute value of the first-order derivative greater than 0.5 mAU / min is marked as a significant change area, and the significant change area is resampled with a minimum sampling interval of 0.001min to obtain a key point sequence; a peak profile feature map is established from the key point sequence, the tangent slope at each sampling point is calculated, and the interval where the slope changes by more than 50% is determined as a peak distortion area, and the sampling density of the distortion area is increased to 0.0005min interval to obtain a corrected sampling scheme; according to the corrected sampling scheme, the boundary area of ​​the peak is finely processed, and within the range of 0.5min before the peak starting point to 0.5min after the peak end point, the sampling interval is adaptively adjusted according to the signal intensity change rate, with a minimum interval of 0.001min and a maximum interval of 0.005min, to obtain boundary sampling data; by integrating the boundary sampling data with the peak main body sampling data, an integral point database is established, the sampling point information is associated with the corresponding signal intensity and stored, an integral interval configuration file of the paeoniflorin peak is generated, and the integral range of the paeoniflorin peak is obtained.

[0010] Preferably, the adaptive adjustment range is established according to the deviation value between the initial average value and the original data, and the data points with deviation exceeding 0.2mAU are weighted twice to obtain the correction coefficient, including: performing difference operation between the initial average value and the original data, grading the deviation value of each data point at intervals of 0.05mAU, assigning a weight coefficient of 0.8 to the points with deviation values ​​within the range of 0.2-0.4mAU, a weight coefficient of 0.6 to the points within the range of 0.4-0.6mAU, and a weight coefficient of 0.4 to the points greater than 0.6mAU, to obtain a deviation grading sequence; calculating the deviation change rate of adjacent points according to the deviation grading sequence, marking the area where the deviation change rate of 5 consecutive points is greater than 20% as the fluctuation interval, and using a time of 0.01min for the data points within the fluctuation interval. The local mean of the window is calculated to obtain the fluctuation characteristic value; the marked points are grouped nearby according to the fluctuation characteristic value, and the fluctuation characteristic values ​​of adjacent groups are compared. When the difference between groups exceeds 30%, the weight coefficient of the area where the difference between groups exceeds 30% is attenuated by 0.1 to obtain the inter-group correction parameter; the weight distribution curve of the data points is established according to the inter-group correction parameter, the curve is fitted in sections, the weights of the points with fitting residuals exceeding 0.05 are recalculated, and the weights are optimized by the local weighted regression method to obtain the weight optimization coefficient; the original deviation value is corrected and calculated according to the weight optimization coefficient, and the corrected deviation value is weighted averaged with the initial weight to generate the target weight distribution scheme to obtain the adjustment coefficient; the adjustment coefficient is combined with the initial weighted result to generate the correction coefficient.Preferably, the weight distribution curve of the data points is established by the inter-group correction parameters, the curve is fitted in sections, the weights of the points whose fitting residuals exceed 0.05 are recalculated, and the weights are optimized by the local weighted regression method to obtain the weight optimization coefficient, including: arranging the data points in time series by the inter-group correction parameters, dividing the data segments with a basic interval of 0.02min, accumulating and counting the weight values ​​of each data point, generating an initial weight distribution sequence, and obtaining weight basic data; constructing a cubic spline fitting function according to the weight basic data, segmenting the data sequence according to a time window of 0.5min, fitting the curve for each data segment by the least squares method, calculating the correlation coefficient of the fitting equation, and obtaining the segmented fitting result; calculating the fitting residual of each data point by the segmented fitting result, and calculating the residual value at an interval of 0.01. The points with residuals exceeding 0.05 are grouped and marked as outliers. The distribution density of outliers on the time axis is counted to obtain the distribution characteristics of outliers. A local weighted regression model is established based on the distribution characteristics of outliers. A calculation window of 0.1 min is set for each outlier. The weight of the data point in the window is inversely proportional to the distance from the center point. The weight of the point with a distance greater than 0.05 min is decayed to 0.5 times the original value to obtain the local weight coefficient. The weight value of the outlier is recalculated according to the local weight coefficient, and the exponential smoothing method is used to perform transition processing on the new and old weights. The smoothing coefficient is set to 0.7, and the weight value of the transition interval is corrected to obtain the optimized weight value. The entire data sequence is updated by the optimized weight value, and the updated weight distribution is compared with the original distribution to generate a weight adjustment record, and a weight optimization database is established to obtain the weight optimization coefficient.

[0011] Preferably, in the step of selecting chromatographic conditions, octadecylsilane bonded silica gel is used as a filler, acetonitrile-0.1% phosphoric acid solution is used as a mobile phase; the detection wavelength is 230 nm, and the theoretical plate number calculated based on the paeoniflorin peak should be no less than 5000.

[0012] Preferably, the peony and liquorice granules are prepared in the following manner: Get 3450g white peony root and 3450g fried licorice, add 5-8 times water, boil hot water to add the materials, extract once, reflux extraction for 30-120 minutes, filter, concentrate to a clear paste with a relative density of 1.08-1.12, dry, take the dry extract powder, add auxiliary materials, mix well, dry granulate, and make 1000g.

[0013] Preferably, step S1 comprises the following steps: Take 2g of each of Shaoyaogancao granules sample, white peony negative sample, licorice negative sample, and auxiliary material sample, add 50mL of water to dissolve, extract with water saturated n-butanol 3 times, 20mL each time, combine the n-butanol solution, wash with water 3 times, 20mL each time, discard the water solution, evaporate the n-butanol solution, and add 5mL of methanol to dissolve the residue as the test solution; take the paeoniflorin reference substance, add methanol to make a solution containing 2mg per 1mL as the reference substance solution; take the ammonium glycyrrhizinate reference substance, add methanol to make a solution containing 2mg per 1mL as the reference substance solution; take 1g of licorice reference medicinal material powder, add 40mL of ether, heat and reflux for 1 hour, filter, discard the ether solution, add 30mL of methanol to the residue, heat and reflux for 1 hour, filter, evaporate the filtrate, add 40mL of water to dissolve the residue, extract with water saturated n-butanol 3 times, 20mL each time, combine the n-butanol solution, wash with water 3 times, 20mL each time mL, discard the water, evaporate the n-butanol solution, add 5 mL of methanol to the residue to dissolve it as the licorice control medicinal material solution; according to the thin layer chromatography test, take 2-4 μl of the test solution, the reference solution and the licorice control medicinal material solution, respectively, and spot them on the same silica gel G thin layer plate prepared with 1% sodium hydroxide solution, use ethyl acetate: formic acid: glacial acetic acid: water with a mass ratio of 15:1:1:2 as the developing solvent, develop, take out, dry, spray with 10% sulfuric acid ethanol solution, heat at 105℃ until the spots are clearly colored, and under sunlight, in the chromatogram of the test sample, at the corresponding position with the chromatogram of the peony glycoside reference substance, the same color spot appears; under ultraviolet light, in the chromatogram of the test sample, at the corresponding position with the chromatogram of the licorice control medicinal material, the same color fluorescent spot appears; at the corresponding position with the chromatogram of the licorice control medicinal material, the same fluorescent spot appears.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The method for determining the content of paeoniflorin in Shaoyaogancao granules provided by the present invention has strong specificity, and the stability, repeatability, intermediate precision, accuracy, etc. all meet the requirements, and the durability of the chromatographic conditions is good, so the liquid chromatography conditions can be used for the determination of the content of paeoniflorin in Shaoyaogancao decoction classic prescription and Shaoyaogancao decoction granules, a new medicine of the classic prescription type. It is recommended to increase the effects related to the chromatographic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is the thin layer identification atlas of Shaoyao Gancao Granules (sunlight).

[0016] Figure 2 This is the thin layer identification spectrum of Shaoyao Gancao Granules (ultraviolet).

[0017] Figure 3 This is the specific HPLC spectrum of paeoniflorin.

[0018] Figure 4 This is the peak purity graph of the test product.

[0019] Figure 5 This is the standard curve of paeoniflorin.

[0020] Figure 6 A comparison chart of different flow rates.

[0021] Figure 7 This is a comparison chart of different column temperatures.

[0022] Figure 8 A comparison chart of different mobile phase ratios.

[0023] Fig. 9 A comparison chart of different chromatographic columns.

[0024] Fig.10 A comparison chart of different acidities. DETAILED DESCRIPTION

[0025] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The present invention provides a method for determining the content of paeoniflorin in Shaoyaogancao granules, comprising the following steps: S1. Identification of Shaoyaogancao Granules by thin layer chromatography; S2. The content of paeoniflorin in Shaoyaogancao granules was determined based on high performance liquid chromatography technology. The high performance liquid chromatography method refers to the general rules of the fourth volume of the 2020 edition of the Chinese Pharmacopoeia 0512; Wherein, step S2 comprises the following steps: Selection of chromatographic operating conditions and parameters: Chromatographic column: C18 column, 4.6mmx250mm, 5μm; Mobile phase A was acetonitrile: mobile phase B was 0.1% phosphoric acid aqueous solution, and the elution conditions were: 0 to 8 min, 13.4% to 16.8% mobile phase A, 86.6% to 83.2% mobile phase B; 8 to 16 min, 16.8% to 22.3% mobile phase A, 83.2% to 77.7% mobile phase B; 16 to 24 min, 22.3% to 25.7% mobile phase A, 77.7% to 74.3% mobile phase B; 24 to 32 min, 25.7% to 19.2% mobile phase A, 74.3% to 80.8% mobile phase B; 32 to 38 min, 19.2% to 13.4% mobile phase A, 80.8% to 86.6% mobile phase B; 38 to 45 min, 13.4% mobile phase A, 86.6% mobile phase B; Flow rate: 0.72mL / min-1.31mL / min, preferably 1.00mL / min; injection volume: 9.45μl-10.55μl, preferably 10.00μl; Column temperature intelligent control system: the temperature is set at 28.5℃-31.5℃, the fluctuation is controlled within ±0.1℃, preferably 30.0℃; Dual wavelength detection system: main wavelength 230nm, reference wavelength 270nm, diode array detector combined with mass spectrometer, scanning range: 190-400nm, detection wavelength is 230nm; Preparation of reference solution: Take the paeoniflorin reference substance, weigh accurately, and add methanol to make a solution containing 80 μg per 1 mL. Preparation of the test solution: Take the Shaoyao Gancao granules, grind them into powder, take 0.1g, weigh accurately, and place it in a stoppered conical bottle. Use the temperature gradient ultrasonic extraction method. The power of ultrasonic treatment is 300w and the frequency is 40kHz. The specific steps are as follows: Step 1: Ultrasonic extraction at 25°C for 10 minutes; Step 2: Ultrasonic extraction at 35°C for 10 minutes; Step 3: Ultrasonic extraction at 45°C for 10 minutes; The extraction solvent is a mixed solution of 70% ethanol: methanol: water = 6: 3: 1, 50 mL is accurately added, plugged, weighed, cooled to 26℃-34℃, weighed again, and the lost weight is supplemented with the same solvent, shaken, filtered through a 0.45μm filter membrane, and the filtrate is purified through an ultrafiltration membrane with a molecular weight cutoff of 3000Da to obtain; Determination method: Use an automatic sample injector to control the temperature at 4±0.5℃, accurately aspirate 10μl of the reference solution and the test solution respectively, inject into the liquid chromatograph, measure, record the chromatogram, and use artificial intelligence-assisted peak area automatic integration algorithm for data processing.

[0027] In this embodiment, in the step of selecting chromatographic conditions, octadecylsilane bonded silica gel is used as a filler, and reference is made to the "White Peony" content determination method in the 2020 edition of the "Chinese Pharmacopoeia" and the mobile phase in the previous material benchmark paeoniflorin content determination, and acetonitrile-0.1% phosphoric acid solution is used as the mobile phase; the detection wavelength is 230nm, and the number of theoretical plates calculated according to the paeoniflorin peak should be no less than 5000.

[0028] In this embodiment, the preparation method of the peony root and licorice granules comprises the following steps: Get 3450g white peony root and 3450g fried licorice, add 5-8 times water, boil hot water, extract once, reflux extraction for 30-120 minutes, filter, concentrate to a clear paste with a relative density of 1.08-1.12 (60°C), dry, take the dry extract powder, add appropriate amount of auxiliary materials (dextrin), mix well, dry granulate, and make 1000g.

[0029] In this embodiment, the step of identifying Shaoyao Gancao granules by thin layer chromatography technology includes: Take 2g of each of the granule sample, the negative sample lacking white peony root, the negative sample lacking licorice root, and the auxiliary material (dextrin) sample, add 50mL of water to dissolve, extract with water-saturated n-butanol 3 times, 20mL each time, and combine the n-butanol solution. Wash with water 3 times, 20mL each time, discard the water solution, evaporate the n-butanol solution to dryness, and add 5mL of methanol to dissolve the residue as the test solution. Take the paeoniflorin reference substance, add methanol to make a solution containing 2 mg per 1 mL, as the reference substance solution; take the ammonium glycyrrhizinate reference substance, add methanol to make a solution containing 2 mg per 1 mL, as the reference substance solution; then take 1g of licorice reference medicinal material powder, add 40mL of ether, heat and reflux for 1 hour, filter, discard the ether solution, add 30mL of methanol to the medicinal residue, heat and reflux for 1 hour, filter, evaporate the filtrate to dryness, add 40mL of water to the residue to dissolve it, extract it with saturated n-butanol with water for 3 times, 20 mL each time, combine the n-butanol solution, wash it with water for 3 times, 20 mL each time, discard the water solution, evaporate the n-butanol solution to dryness, and add 5mL of methanol to the residue to dissolve it as the licorice reference medicinal material solution. According to the thin layer chromatography method (General Rules 0502 of Part IV of the 2015 edition of the Chinese Pharmacopoeia), 2-4 μl of each of the four solutions were taken and spotted on the same silica gel G thin layer plate prepared with 1% sodium hydroxide solution, ethyl acetate-formic acid-glacial acetic acid-water (15:1:1:2) was used as the developing solvent, and the plate was developed, taken out, dried, sprayed with 10% ethanolic sulfuric acid solution, and heated at 105°C until the spots were clearly colored. Under sunlight, spots of the same color appeared in the chromatogram of the test sample at the corresponding position of the chromatogram of the paeoniflorin reference substance; under ultraviolet light (365 nm), fluorescent spots of the same color appeared in the chromatogram of the test sample at the corresponding position of the chromatogram of the licorice reference medicinal material; and the same fluorescent spots appeared in the corresponding position of the chromatogram of the licorice reference substance.

[0030] Example 1: The present invention provides a method for determining the content of paeoniflorin in Shaoyaogancao granules, comprising the following steps: S1. Identification of Shaoyaogancao Granules by thin layer chromatography; The step of identifying Shaoyao Gancao granules by thin layer chromatography technology comprises: Take 2g of each of the granule sample, the negative sample lacking white peony root, the negative sample lacking licorice root, and the auxiliary material (dextrin) sample, add 50mL of water to dissolve, extract with water-saturated n-butanol 3 times, 20mL each time, and combine the n-butanol solution. Wash with water 3 times, 20mL each time, discard the water solution, evaporate the n-butanol solution to dryness, and add 5mL of methanol to dissolve the residue as the test solution. Take the paeoniflorin reference substance, add methanol to make a solution containing 2 mg per 1 mL, as the reference substance solution; take the ammonium glycyrrhizinate reference substance, add methanol to make a solution containing 2 mg per 1 mL, as the reference substance solution; then take 1g of licorice reference medicinal material powder, add 40mL of ether, heat and reflux for 1 hour, filter, discard the ether solution, add 30mL of methanol to the medicinal residue, heat and reflux for 1 hour, filter, evaporate the filtrate to dryness, add 40mL of water to the residue to dissolve it, extract it with saturated n-butanol with water for 3 times, 20mL each time, combine the n-butanol solution, wash it with water for 3 times, 20mL each time, discard the water solution, evaporate the n-butanol solution to dryness, and add 5mL of methanol to the residue to dissolve it as the licorice reference medicinal material solution. According to the thin layer chromatography method (General Rules 0502 of Part IV of the 2015 edition of the Chinese Pharmacopoeia), 2~4μl of each of the above four solutions were taken and spotted on the same silica gel G thin layer plate prepared with 1% sodium hydroxide solution, ethyl acetate-formic acid-glacial acetic acid-water (15:1:1:2) was used as the developing agent, and the plate was developed, taken out, dried, sprayed with 10% sulfuric acid ethanol solution, and heated at 105°C until the spots were clearly colored. Under sunlight, the same color spots appeared in the chromatogram of the test sample at the corresponding position of the chromatogram of the paeoniflorin reference substance; under ultraviolet light (365nm), the same color fluorescent spots appeared in the chromatogram of the test sample at the corresponding position of the chromatogram of the licorice reference medicinal material; the same fluorescent spots appeared in the corresponding position of the chromatogram of the licorice reference substance. Results are shown in Figure 1-2 .

[0031] The results of thin layer identification are as follows: Figure 1-2 S1-peonidin reference substance, S2-liquiritigenin reference substance, S3-licorice reference medicinal material, S4-peonidin and licorice test sample 1, S5-peonidin and licorice test sample 2, S6-white peony negative test sample, S7-licorice negative test sample, S8-excipient (dextrin) test sample, Figure 1 It was inspected under sunlight, where the Rf value of paeoniflorin was 0.4, and there was no interference in the negative; Figure 2 It was inspected under ultraviolet light, and the Rf value of liquiritin was 0.5, and there was no interference in the negative.

[0032] S2. Determination of the content of paeoniflorin in Shaoyaogancao granules based on high performance liquid chromatography technology; Wherein, step S2 comprises the following steps: Selection of chromatographic conditions: octadecylsilane bonded silica gel as filler, acetonitrile-0.1% phosphoric acid solution (14:86) as mobile phase; detection wavelength is 230nm, and the theoretical plate number calculated based on the paeoniflorin peak should be no less than 5000; Preparation of reference solution: Take the paeoniflorin reference substance, weigh accurately, and add methanol to make a solution containing 80 μg per 1 mL. Preparation of the test solution: Take the Shaoyao Gancao granules, grind them into powder, take 0.1g, weigh accurately, and place it in a stoppered conical bottle. Use the temperature gradient ultrasonic extraction method. The power of ultrasonic treatment is 300w and the frequency is 40kHz. The specific steps are as follows: Step 1: Ultrasonic extraction at 25°C for 10 minutes; Step 2: Ultrasonic extraction at 35°C for 10 minutes; Step 3: Ultrasonic extraction at 45°C for 10 minutes; The extraction solvent is a mixed solution of 70% ethanol: methanol: water = 6: 3: 1, 50 mL is accurately added, plugged, weighed, cooled to 26℃-34℃, weighed again, and the lost weight is supplemented with the same solvent, shaken, filtered through a 0.45μm filter membrane, and the filtrate is purified through an ultrafiltration membrane with a molecular weight cutoff of 3000Da to obtain; Determination method: Use an automatic sample injector to control the temperature at 4±0.5℃, accurately aspirate 10μl of the reference solution and the test solution respectively, inject into the liquid chromatograph, measure, record the chromatogram, and use artificial intelligence-assisted peak area automatic integration algorithm for data processing.

[0033] In this embodiment, in the step of preparing the test solution, the solvent is a mixed solution of 70% ethanol: methanol: water = 6:3:1.

[0034] Example 2: The difference between Example 2 and Example 1 is that in the preparation step of the test solution in this example, the solvent is: ethanol with a mass concentration of 50%, and the rest are the same.

[0035] Example 3: The difference between Example 3 and Example 1 is that in the preparation step of the test solution in this example, the solvent is methanol with a mass concentration of 70%, and the rest are the same.

[0036] The measurement results of Examples 1-3 are shown in Table 1: Table 1

[0037] Example 4: The difference between Example 4 and Example 1 is that in the preparation step of the test solution, ultrasonic treatment is performed for 20 minutes, and the rest are the same.

[0038] Example 5: The difference between Example 5 and Example 1 is that in the preparation step of the test solution of this example, ultrasonic treatment is performed for 45 minutes, and the rest are the same.

[0039] The measurement results of Example 1 and Examples 4-5 are shown in Table 2: Table 2

[0040] In summary: Example 1 of the present invention is a preferred embodiment, and the following test is performed on it according to the determination method.

[0041] Specificity test: Paeoniflorin is an index component of the classic Shaoyao Gancao Decoction. Negative sample solution lacking white peony root, blank solvent, and excipient (dextrin) sample solution were prepared according to the test sample treatment method for injection. The results showed that there was no chromatographic peak at the retention time corresponding to paeoniflorin in the negative sample chromatogram, indicating that the negative sample lacking white peony root, blank solvent, and excipient (dextrin) sample solution had no interference with the determination of paeoniflorin. This method has specificity for determining the content of paeoniflorin in this product (see Figure 3 ).

[0042] Peak purity check: Take the above test solution, inject it into the liquid chromatograph, use the above chromatographic conditions to perform full wavelength detection with the DAD detector, calculate the peak purity, and the result shows that no impurity peak is detected in the peony indole peak of the sample chromatogram; this indicates that under this chromatographic condition, the purity of the peony indole chromatographic peak meets the requirements (see Figure 4 ).

[0043] Investigation of linear relationship: Accurately weigh 10.26 mg of paeoniflorin reference substance (batch number: 110736-202246, purity: 96.7%), place it in a 25 mL volumetric flask, add methanol to dissolve and dilute to the scale, shake well, and obtain a mother solution of reference substance solution with a concentration of 396.8568 μg / mL. Accurately measure 5 mL, place it in 10 mL and 25 mL volumetric flasks, add methanol to dilute to the scale, shake well, and obtain reference substance solutions with concentrations of 198.4284 and 79.37136 ug / mL, respectively; then take the above concentration of 79.37136 ug / mL, accurately measure 5 mL, place it in 10 mL and 20 mL volumetric flasks, add methanol to dilute to the scale, shake well, and obtain concentrations of 39.68568 and 19.84284 ug / mL, respectively. Accurately pipette 10 μl of each of the above reference solutions, inject into the liquid chromatograph, and determine according to the liquid chromatography method (General Rules 0512 of Part IV of the 2020 edition of the Chinese Pharmacopoeia). Draw a standard curve with the injection volume (μg) as the horizontal axis and the peak area as the vertical axis. See Table 3. Figure 5 .

[0044] Table 3-Linear relationship of paeoniflorin

[0045] Regression equation: y=16.457x-0.4591 Correlation coefficient: r=0.9999 The results showed that the linearity of paeoniflorin injection amount was good in the range of 0.1984284μg to 3.968568μg.

[0046] Precision test: Take the paeoniflorin reference solution and inject it continuously 6 times according to the determined determination method. The determination results are shown in Table 4.

[0047] Table 4 - Precision test results (n=6)

[0048] The results showed that the RSD was less than 1.5%, indicating that the instrument had good precision.

[0049] Repeatability test: The experimenter (A) took an appropriate amount of Shaoyao Gancao granules, ground them into powder, took about 0.1g (a total of 6 portions), weighed them accurately, and operated according to the determined test solution preparation and determination method. The results are shown in Table 5.

[0050] Table 5 - Repeatability test results (n=6)

[0051] The results showed that the RSD was less than 1.5%, indicating that the method had good repeatability.

[0052] Stability test: Take an appropriate amount of Peony and Licorice Granules, grind them into powder, take about 0.1g, weigh it accurately, and operate according to the preparation and determination method of the test solution. Samples are injected and determined at 0, 2, 4, 8, 12, 22, 30, and 45 hours after preparation. The determination results are shown in Table 6.

[0053] Table 6 - Stability test results

[0054] The results showed that the RSD was less than 1.5% and the test solution had good stability within 45 hours.

[0055] Accuracy test: Using the sample recovery method, accurately weigh 19.79 mg of paeoniflorin reference substance (batch number: 110736-202246, purity: 96.7%) and place it in a 100 mL volumetric flask, add methanol to dilute to the scale, shake well, and obtain a reference substance solution with a concentration of 19.13693 μg / mL.

[0056] Take an appropriate amount of Shaoyao Gancao granules, grind them into powder, take about 0.05g, accurately weigh 9 portions, put them in a 50mL volumetric flask, accurately add 5mL, 10mL, and 15mL (3 portions each) of the above-mentioned paeoniflorin reference solution, add an appropriate amount of 70% ethanol to dissolve, seal, ultrasonically treat (power 250W, frequency 40kHz) for 30 minutes, cool to 26℃-34℃, add 70% ethanol to the scale, shake well, filter, accurately draw 10μl of the filtrate, inject it into the liquid chromatograph, and determine it. The results are shown in Table 7.

[0057] The content of paeoniflorin in the non-dried product of Shaoyao Gancao Granules (batch number: JDC2407001) is 38.2 mg / g.

[0058] Table 7- Results of the accuracy test of paeoniflorin (n=9)

[0059] The results showed that the average recovery rate of paeoniflorin was 96.52% and the RSD was less than 0.9%, indicating that the method had good accuracy.

[0060] Intermediate precision test: Select different measurement time, different experimenters (B), different HPLC, take appropriate amount of Shaoyao Gancao Granule (batch number: JDC2407001), grind it into powder, take about 0.1g, weigh it accurately, and operate according to the determined test solution preparation and measurement items. The results are shown in Tables 8-10.

[0061] Table 8 - Results of intermediate precision test of paeoniflorin (different personnel)

[0062] As a result, the average content of paeoniflorin in samples A and B was 38.3 mg / g, and the RSD was less than 2%.

[0063] Table 9- Results of intermediate precision test of paeoniflorin (different dates)

[0064] As a result, the average content of paeoniflorin in samples measured by different instruments was 38.0 mg / g, and the RSD was less than 2%.

[0065] Table 10- Results of intermediate precision test of paeoniflorin (different instruments)

[0066] As a result, the average content of paeoniflorin in samples measured by different instruments was 37.1 mg / g, and the RSD was less than 2%.

[0067] In summary, the RSD of samples tested by different personnel, different instruments and different dates was less than 2%, indicating that the intermediate precision of this method was good.

[0068] Durability test: The durability of the chromatographic conditions under different flow rates, column temperatures, mobile phase ratios, chromatographic columns, and acidity was investigated. The results are shown in Table 11. Figures 6 to 10 .

[0069] Table 11 - Durability test results

[0070] The results showed that the results of the method were basically consistent under different flow rates, column temperatures, mobile phase ratios, acidities and chromatographic columns, indicating that the method had good durability for different flow rates, column temperatures, mobile phase ratios, acidities and chromatographic columns.

[0071] The above research results show that the method for determining the content of paeoniflorin in the classic prescription of Shaoyao Gancao Decoction has strong specificity, and its stability, repeatability, intermediate precision, accuracy, etc. all meet the requirements, and the durability of the chromatographic conditions is good. Therefore, this liquid chromatography condition can be used for the determination of the content of paeoniflorin in the classic prescription of Shaoyao Gancao Decoction.

[0072] Embodiment 6: The step of using artificial intelligence assisted peak area automatic integration algorithm to process data comprises: (1) The wavelength of the chromatogram was scanned by a diode array detector at a sampling interval of 0.001 min, and a three-dimensional data matrix in the range of 190-400 nm was collected. The signal data of the main wavelength of 230 nm and the reference wavelength of 270 nm were separated and extracted from the data matrix; (2) Perform a 9-point sliding average calculation based on the signal data with a main wavelength of 230 nm, and perform a weighted average of the signal intensities of the 4 points before and after each data point to obtain the main signal baseline characteristic value; (3) Using the signal data at a reference wavelength of 270 nm and a window width of 0.1 min, the local variance is calculated and the baseline characteristic value of the main signal is dynamically calibrated to obtain the baseline correction parameters; (4) A cubic spline function is constructed based on the baseline correction parameters, and the main wavelength signal is corrected in sections, with each section being 0.5 min long, to obtain the chromatographic baseline correction data; (5) Calculate the first-order and second-order derivatives based on the chromatographic baseline correction data, and locate the boundary of the paeoniflorin peak by combining the maximum point and zero-crossing point of the derivative value to obtain the starting point and end point data of the peak; (6) The integration interval was finely divided into 0.002-min intervals using the peak start and end data. The area with a signal intensity greater than twice the baseline was sampled to obtain the integration range of the paeoniflorin peak.

[0073] Among them, in the data processing process of the determination of the content of paeoniflorin in Shaoyaogancao granules, it is first necessary to collect data from the chromatogram through a diode array detector. The diode array detector is a spectral detector that detects multiple wavelengths simultaneously and is widely used in high performance liquid chromatography analysis. The diode array detector continuously collects spectral data in the range of 190-400nm with a sampling interval of 0.001min to form a three-dimensional data matrix. Each data point in the three-dimensional data matrix contains three parameters: time coordinate, wavelength coordinate and absorbance value. The signal data of the main wavelength 230nm and the reference wavelength 270nm are extracted from the data matrix. The main wavelength 230nm is the maximum absorption wavelength of paeoniflorin and is used for quantitative analysis; the reference wavelength 270nm is used for baseline correction, and there is no obvious absorption of paeoniflorin at this wavelength. When the signal data of the main wavelength 230nm is calculated by a 9-point sliding average, each data point and the 4 points before and after it are weighted averaged. In the weighted average process, different weight coefficients are assigned to the 9 points in turn. In the determination of peonygin content, the weight of the center point is the largest, and the weight coefficient gradually decreases as the distance from the center point increases. This weight distribution method fully considers the influence of neighboring points on the target point, which is conducive to reducing noise while retaining the main characteristics of the signal.

[0074] The signal data of the reference wavelength of 270nm plays a key role in the correction of baseline drift. By setting a time window of 0.1min, the local variance of the signal in the window is calculated to reflect the fluctuation characteristics of the signal in the window. The larger the local variance value, the more violent the signal fluctuation in this area. The local variance calculation result is used as a correction parameter to dynamically calibrate the baseline characteristic value of the main signal. This correction method can adapt to the dynamic change characteristics of baseline drift during the entire analysis process. When constructing a cubic spline function with baseline correction parameters, the chromatogram is segmented according to a length of 0.5min. Each segment of data is fitted by a cubic spline function to ensure the continuity of the function value and derivative value at the segmentation point. The cubic spline function has good smoothness and continuity, and can accurately describe the local change characteristics of the baseline. In practical applications, the selection of segment length needs to comprehensively consider data processing efficiency and fitting accuracy.

[0075] After obtaining the chromatographic baseline correction data, the peak boundary is identified by calculating the first-order derivative and the second-order derivative. The first-order derivative value reflects the rate of change of the signal, while the second-order derivative value reflects the trend of the rate of change. On the rising edge of the chromatographic peak, the first-order derivative is positive and reaches a local maximum; at the top of the peak, the first-order derivative is zero and the second-order derivative is negative; on the falling edge, the first-order derivative is negative. By finding these characteristic points, the precise boundary position of the paeoniflorin peak is determined. After determining the boundary of the peak, the integration interval is finely divided. The integration interval is sampled at intervals of 0.002 min, focusing on areas where the signal intensity exceeds 2 times the baseline. This sampling strategy ensures that there are sufficiently dense sampling points in areas with strong signal response, improving the integration accuracy.

[0076] In the specific practice of determining the content of paeoniflorin in Shaoyaogancao granules, the data processing process reflects strict logic and correlation. First, the raw data collected by the diode array detector is wavelength selected to separate the signals of the main wavelength and the reference wavelength. After the main wavelength signal is processed by sliding average, the data curve is smoother, which is convenient for subsequent processing. The reference wavelength data is analyzed by local variance to generate baseline correction parameters, which directly reflect the degree of baseline drift at different time points. The baseline-corrected data is subjected to derivative analysis, and the peak boundary is identified by the characteristic changes of the derivative value. Finally, the quantitative calculation is completed within the determined integration interval.

[0077] For example, when determining the content of peonidin, the raw data is first collected through a diode array detector. After wavelength selection, the signal data of the main wavelength 230nm and the reference wavelength 270nm are obtained respectively. When the main wavelength data is calculated by a 9-point sliding average, the signal intensity of 9 consecutive points is weighted to obtain a smoothed signal curve. The reference wavelength data is used to calculate the local variance with a window of 0.1min to obtain the dynamic change characteristics of the baseline drift. The baseline-corrected data is analyzed by derivative to accurately mark the start and end positions of the peonidin peak on the chromatogram. Finally, within the integral interval of the peak, sampling and calculation are performed at intervals of 0.002min.

[0078] Example 7: A 9-point sliding average calculation is performed based on the signal data of the main wavelength of 230 nm, and the signal intensity of the 4 points before and after each data point is weighted averaged to obtain the main signal baseline characteristic value, including: (1) Using the signal data of the main wavelength of 230 nm, three signal intensity thresholds of high, medium, and low were set, which were 8 times, 4 times, and 2 times the baseline intensity, respectively. Data were collected in different signal areas to obtain a layered signal sequence. (2) Assign a weight coefficient to each data point in the layered signal sequence and the four points before and after it to obtain the weighted calculation parameters; (3) Perform a moving sum operation on the 9-point data using the weighted calculation parameters, and divide the calculation result by the sum of the weight coefficients to obtain the initial average value; (4) Establish an adaptive adjustment range based on the deviation between the initial average value and the original data, and perform secondary weighting on the data points with a deviation exceeding 0.2 mAU to obtain the correction coefficient; (5) Calibrate the initial average value using the correction coefficient to generate the target weighted average result and obtain the baseline characteristic value; (6) Build a data index through the baseline eigenvalue, record the weight distribution and calculation process of each data point, and obtain the baseline eigenvalue of the main signal.

[0079] Among them, the signal intensity is layered and three signal intensity thresholds of high, medium and low are set. The high intensity threshold is 8 times the baseline intensity, the medium intensity threshold is 4 times the baseline intensity, and the low intensity threshold is 2 times the baseline intensity. The baseline intensity refers to the signal intensity of the area in the chromatogram that does not contain the analyte, and the unit is milli-absorbance unit (mAU). Through this layered processing method, the chromatographic data is divided into different intensity areas, and data collection and processing are carried out in a targeted manner. When performing a 9-point sliding average calculation on the layered signal sequence, it is necessary to assign weights to each data point and the 4 points before and after it. The weight allocation adopts the principle of symmetry, that is, the closer the data point is to the center point, the larger the weight coefficient is. This weight allocation method takes into account the correlation between data points and can effectively suppress noise while retaining the main characteristics of the signal.

[0080] After the weighted calculation parameters are determined, a moving summation operation is performed on the 9-point data. During the moving summation process, the signal strength of each point is multiplied by its corresponding weight coefficient, and then the 9 products are added. In order to ensure the standardization of the calculation results, it is necessary to divide the summation result by the sum of the weight coefficients to obtain the initial average value. This process is essentially a local weighted average process, which smoothes the data through reasonable weight distribution. After the initial average value is calculated, it is necessary to compare it with the original data to calculate the deviation value of each point. For data points with a deviation of more than 0.2mAU, secondary weighting processing is required. Secondary weighting processing is an adaptive process that dynamically adjusts the weight coefficient according to the size of the deviation value. The larger the deviation value, the smaller the weight coefficient assigned during secondary weighting, which can effectively reduce the impact of abnormal data points on the final result.

[0081] After the correction coefficient is obtained, the initial average value is calibrated. The calibration process takes into account the characteristics of the original data and the results of the secondary weighting to generate a more accurate weighted average result. The baseline characteristic values ​​generated by this process better reflect the actual baseline trend. Finally, the baseline characteristic values ​​are associated with the key information in the data processing process to establish a data index. The data index contains the weight distribution of each data point and the calculation process record to ensure the traceability of the data processing process and provide a basis for subsequent data analysis.

[0082] For example, after obtaining the original signal data with a main wavelength of 230nm, first determine the three signal intensity thresholds according to the average signal intensity of the baseline area. For a section of measurement data, assuming that the average signal intensity of the baseline area is 0.5mAU, the high intensity threshold is set to 4mAU (8 times), the medium intensity threshold is set to 2mAU (4 times), and the low intensity threshold is set to 1mAU (2 times). According to these thresholds, the data points are divided into different levels, and the data points above 4mAU are classified into the high intensity area, the data points between 2-4mAU are classified into the medium intensity area, and the data points between 1-2mAU are classified into the low intensity area. When the stratified data is calculated by a 9-point sliding average, the center point is taken as the reference, and 4 points are taken before and after. When setting the weight coefficient, the weight of the center point is the largest, and the weight of the points on both sides decreases as the distance increases. The signal strength of the 9 points is multiplied by the corresponding weight coefficient and summed, and then divided by the sum of the weight coefficients to obtain the initial average value of the point. Repeat this process for each data point to obtain a series of initial average values. Compare these initial average values ​​with the original data points to calculate the deviation value. When a point with a deviation exceeding 0.2mAU is found, secondary weighting processing is required. The weight coefficient of the secondary weighting is dynamically adjusted according to the size of the deviation value. The larger the deviation, the smaller the weight coefficient, thereby reducing the impact of the abnormal point. After the secondary weighting processing, the weighted average is recalculated to obtain the baseline characteristic value. The baseline characteristic values ​​are connected to form a smooth baseline curve.

[0083] Example 8: Calculate the first and second order derivatives according to the chromatographic baseline correction data, and locate the boundary of the paeoniflorin peak in combination with the maximum point and the zero crossing point of the derivative value to obtain the starting point and end point data of the peak, including: (1) The calculation interval of 0.002 min was set by chromatographic baseline correction data, and the adjacent data points were differentially calculated to obtain the first-order derivative value sequence; (2) Performing secondary difference calculation on the first-order derivative value sequence, quantifying the derivative value change between each data point and its adjacent points, and obtaining the second-order derivative value sequence; (3) Find the point with the largest signal change rate through the first-order derivative value sequence, mark the point with a positive change rate greater than 0.5 mAU / min as the rising edge, and obtain the peak starting range; (4) Determine the inflection point position according to the second-order derivative value sequence, mark the position where the second-order derivative crosses zero and the absolute value of the first-order derivative is greater than 0.2 mAU / min as the key point, and obtain the peak profile feature point; (5) Extend from the peak profile feature point to both sides until the signal intensity drops to 105% of the baseline, perform noise evaluation on the extended interval, and obtain the boundary positioning result; (6) Determine the starting point and end point of the integration interval through the boundary positioning results, generate the peak integration range data, and obtain the starting point and end point data of the peak.

[0084] Among them, the baseline correction data is differentially calculated at a fixed time interval of 0.002min to calculate the change in signal intensity between adjacent points. The differential operation results form a first-order derivative value sequence, which reflects the rate of change of signal intensity over time. A positive first-order derivative value indicates that the signal is increasing, a negative first-order derivative value indicates that the signal is decreasing, and a zero first-order derivative value indicates that the signal has reached an extreme point. The first-order derivative value sequence is differentially calculated again to obtain a second-order derivative value sequence. The second-order derivative value reflects the rate of change of the first-order derivative, that is, the change in the rate of change of the signal. The second-order derivative crosses the zero point at the inflection point, and this feature is of great significance for determining the shape and boundary position of the chromatographic peak. On the rising edge of the chromatographic peak, the second-order derivative changes from positive to negative; on the falling edge, the second-order derivative changes from negative to positive.

[0085] The determination of the signal change rate is crucial for the identification of the chromatographic peak boundary. The first-order derivative value sequence is used to find the point with the largest signal change rate, and the point with a positive change rate greater than 0.5mAU / min is marked as the rising edge. The mAU here represents milli-absorbance units, which is a commonly used signal intensity unit in spectral detection. By setting the change rate threshold, the chromatographic peak can be effectively distinguished from the baseline noise. The determination of the inflection point position depends on the analysis of the second-order derivative value sequence. On the chromatographic peak profile, the inflection point is the position where the slope of the curve changes most dramatically, corresponding to the zero-crossing point of the second-order derivative. In order to accurately identify the true inflection point, it is required that the point is not only the zero-crossing point of the second-order derivative, but also that the absolute value of the first-order derivative is greater than 0.2mAU / min. This dual condition can effectively avoid mistaking noise fluctuations for the boundary points of the peak.

[0086] After determining the peak profile feature points, it is necessary to extend to both sides to find the true peak boundary. The termination condition for the extension is that the signal intensity drops to 105% of the baseline. This standard takes into account the influence of baseline drift and noise in actual analysis. The noise of the extension interval is evaluated to ensure the accuracy of boundary positioning. The boundary positioning results are ultimately used to determine the start and end points of the integration interval. These data points constitute the peak integration range, which directly affects the accuracy of quantitative analysis. The start and end point data of the peak not only contain time coordinates, but also the corresponding signal intensity values.

[0087] For example, the chromatographic data after baseline correction is differentially calculated with a calculation interval of 0.002 min. The first-order derivative value sequence is obtained by differential calculation, which reflects the trend of signal change over time. In the rising edge of the peak, the first-order derivative value is continuously positive, and at some points it exceeds the threshold of 0.5 mAU / min. These points are marked as potential peak starting ranges. The first-order derivative value sequence is differentiated again to obtain the second-order derivative value sequence. On the contour of the chromatographic peak, the inflection point position of the peak is determined by finding the zero-crossing point of the second-order derivative and checking whether the absolute value of the first-order derivative at the corresponding position is greater than 0.2 mAU / min. These inflection points are the key feature points for defining the peak shape. Extend from these feature points to both sides until the signal intensity drops to 105% of the baseline. During this period, the noise level of the extended interval is continuously monitored to ensure the reliability of boundary positioning.

[0088] Example 9: The integration interval is finely divided at 0.002 min intervals through the peak starting point and end point data, and the area with a signal intensity greater than 2 times the baseline is sampled to obtain the integration range of the paeoniflorin peak, including: (1) The integration interval is stratified by the peak start and end data, and the signal intensity is divided into three response areas: high, medium, and low according to 8 times the baseline, 4 times the baseline, and 2 times the baseline. The high response area is sampled at a 0.001-min interval, the medium response area is sampled at a 0.002-min interval, and the low response area is sampled at a 0.004-min interval to obtain a stratified sampling sequence; (2) Linear interpolation calculations were performed on adjacent sampling points according to the stratified sampling sequence. In the area where the signal change rate was greater than 0.8 mAU / min, points were inserted and points were diluted in the area where the change rate was less than 0.2 mAU / min to obtain the optimized sampling point distribution. (3) The key points of the peak shape were determined by optimizing the sampling point distribution, and the points with the absolute value of the first-order derivative greater than 0.5 mAU / min were marked as significant change areas. The significant change areas were resampled with a minimum sampling interval of 0.001 min to obtain a key point sequence; (4) A peak profile characteristic diagram was established based on the key point sequence, and the tangent slope at each sampling point was calculated. The interval where the slope changed by more than 50% was determined as the peak distortion area. The sampling density of the distortion area was increased to 0.0005 min interval to obtain a corrected sampling plan. (5) According to the modified sampling scheme, the peak boundary area is finely processed. In the range of 0.5 min before the peak start point to 0.5 min after the peak end point, the sampling interval is adaptively adjusted according to the signal intensity change rate. The minimum interval is 0.001 min and the maximum interval is 0.005 min to obtain the boundary sampling data; (6) By integrating the boundary sampling data with the peak body sampling data, an integration point database is established, and the sampling point information is associated with the corresponding signal intensity and stored to generate an integration interval configuration file for the paeoniflorin peak, thereby obtaining the integration range of the paeoniflorin peak.

[0089] Among them, the integration interval is stratified. The basis for stratification is the ratio of signal intensity to baseline, and the entire chromatographic peak area is divided into three response areas: high, medium and low. The high response area refers to the part where the signal intensity exceeds 8 times the baseline. This area usually contains the top area of ​​the peak and requires the most intensive sampling; the medium response area refers to the part where the signal intensity is between 4-8 times the baseline, including the main rising and falling edges of the peak; the low response area refers to the part where the signal intensity is between 2-4 times the baseline, mainly the edge area of ​​the peak. Using different sampling intervals for different response areas is to balance the sampling accuracy and data volume. The high response area uses a minimum sampling interval of 0.001min to ensure that the shape of the peak top can be accurately described; the medium response area uses a sampling interval of 0.002min, which is sufficient to capture the main changes in the signal; the low response area uses a sampling interval of 0.004min because the signal changes in this area are relatively gentle. The sampling sequence obtained by this stratified sampling strategy not only ensures the sampling density of the key area, but also avoids oversampling.

[0090] After the stratified sampling sequence is formed, the sampling point distribution needs to be optimized through linear interpolation calculation. Linear interpolation is a method of estimating new data points between two known data points. In areas where the signal change rate is greater than 0.8mAU / min, additional sampling points need to be inserted to increase the sampling density. These areas usually correspond to parts where the peak shape changes dramatically. On the contrary, in areas where the signal change rate is less than 0.2mAU / min, the number of sampling points can be appropriately reduced because the signal changes in these areas are relatively gentle. The optimized sampling point distribution is used to determine the key points of the peak shape. By calculating the absolute value of the first-order derivative, the points greater than 0.5mAU / min are identified as areas of significant change. These areas are the most obvious parts of the peak shape features and need to be resampled using a minimum sampling interval of 0.001min to ensure that the peak shape features are fully retained.

[0091] After obtaining the sequence of key points, the peak distortion area is identified by calculating the tangent slope at each sampling point. The change in the tangent slope reflects the local change characteristics of the peak shape. When the slope of adjacent sampling points changes by more than 50%, it indicates that there is obvious peak distortion in the area, and the sampling density needs to be increased to an interval of 0.0005min to accurately describe the peak shape characteristics of the distorted area. The processing of the boundary area is equally important. An adaptive sampling interval strategy is adopted in the range of 0.5min before the peak start point to 0.5min after the peak end point. The size of the sampling interval is dynamically adjusted according to the rate of change of the signal intensity. Smaller intervals are used in areas with large change rates, and larger intervals are used in areas with small change rates, but they are always kept within the range of 0.001-0.005min.

[0092] Finally, the boundary sampling data is integrated with the peak body sampling data to form a complete integration point database. The information of each sampling point includes time coordinates, signal intensity value, sampling area type, etc., which are organized into structured data files.

[0093] In the determination of peony inoside content, when dividing the integral interval of the peony inoside chromatographic peak, the baseline intensity value is determined, and then the peak area is stratified according to the signal intensity multiple. In the high response area near the peak top, a dense sampling point distribution is used to accurately describe the peak shape; in the main part of the peak, the sampling density is adjusted according to the signal change characteristics; in the edge area of ​​the peak, a relatively sparse sampling point distribution is used. At the same time, in areas where the signal changes dramatically, the sampling points are increased through interpolation calculations, and the sampling points are appropriately reduced in areas where the changes are gentle. For the identified peak shape distortion areas, a higher sampling density is used for detailed description. This multi-level sampling strategy ensures the accuracy of the integral calculation while maintaining a reasonable amount of data.

[0094] Embodiment 10: An adaptive adjustment range is established according to the deviation between the initial average value and the original data, and a correction coefficient is obtained by performing secondary weighting on the data points with a deviation exceeding 0.2 mAU, including: (1) By performing a difference operation between the initial average value and the original data, the deviation value of each data point is graded at an interval of 0.05 mAU. A weight coefficient of 0.8 is assigned to the points with a deviation value in the range of 0.2-0.4 mAU, a weight coefficient of 0.6 is assigned to the points in the range of 0.4-0.6 mAU, and a weight coefficient of 0.4 is assigned to the points with a deviation value greater than 0.6 mAU, thus obtaining a deviation graded sequence; (2) Calculate the deviation change rate of adjacent points according to the deviation classification sequence, mark the area where the deviation change rate of 5 consecutive points is greater than 20% as the fluctuation interval, and calculate the local mean of the data points in the fluctuation interval using a 0.01 minute time window to obtain the fluctuation characteristic value; (3) The marked points are grouped according to the fluctuation characteristic values, and the fluctuation characteristic values ​​of adjacent groups are compared. When the difference between groups exceeds 30%, the weight coefficient of the area where the difference between groups exceeds 30% is attenuated by 0.1 to obtain the inter-group correction parameter; (4) The weight distribution curve of the data points is established based on the inter-group correction parameters, and the curve is fitted in sections. The weights of the points where the fitting residual exceeds 0.05 are recalculated, and the weights are optimized using the local weighted regression method to obtain the weight optimization coefficient; (5) Correct the original deviation value according to the weight optimization coefficient, take the weighted average of the corrected deviation value and the initial weight, generate the target weight allocation scheme, and obtain the adjustment coefficient; (6) The correction coefficient is generated by combining the adjustment coefficient with the initial weighted result.

[0095] Among them, the initial average value is subtracted from the original data to calculate the deviation value of each data point. The deviation values ​​are graded at intervals of 0.05mAU (milli-absorbance units), and a grading standard is established: for data points with deviation values ​​in the range of 0.2-0.4mAU, a weight coefficient of 0.8 is assigned, indicating that these points have high credibility; for data points with deviation values ​​in the range of 0.4-0.6mAU, a weight coefficient of 0.6 is assigned, indicating that these points have medium credibility; for data points with deviation values ​​greater than 0.6mAU, a weight coefficient of 0.4 is assigned, indicating that these points have low credibility. The deviation grading sequence obtained by this grading method reflects the quality distribution of the data points. After obtaining the deviation grading sequence, it is necessary to calculate the deviation change rate between adjacent data points. When the deviation change rate of 5 consecutive points exceeds 20%, this area is marked as a fluctuation interval. The fluctuation interval indicates that the data point is highly unstable and requires special treatment. For the data points in these fluctuation intervals, a time window of 0.01min is used for local mean calculation. The selection of the time window takes into account the acquisition frequency and signal change characteristics of the chromatographic data. The fluctuation characteristic value calculated by the local mean reflects the overall fluctuation of the area.

[0096] After the calculation of the fluctuation characteristic value is completed, the marked points are grouped nearby and the fluctuation characteristic values ​​between adjacent groups are compared. When the difference in the fluctuation characteristic values ​​of adjacent groups exceeds 30%, it indicates that there are obvious differences in the data stability of these areas. For these areas with obvious differences, the weight coefficient is attenuated by 0.1 to reduce the impact of unstable data, thereby obtaining the inter-group correction parameters. Based on the inter-group correction parameters, a weight distribution curve of the data points is established. This curve is fitted in sections to identify points where the fitting residual exceeds 0.05. These points indicate anomalies in the local weight distribution and the weights need to be recalculated. The local weighted regression method is used for weight optimization, which takes into account the local correlation of the data points and can obtain a more reasonable weight distribution.

[0097] After the weight optimization coefficient is determined, the original deviation value is corrected and calculated. The corrected deviation value is weighted averaged with the initial weight to generate a new weight distribution scheme. This scheme takes into account the original deviation and the optimized weight, and better reflects the actual importance of the data point. Finally, the adjustment coefficient is combined with the initial weighted result to obtain the final correction coefficient. This correction coefficient will be used in subsequent data processing to ensure the accuracy of the analysis results.

[0098] For example: in the analysis of Shaoyao Gancao Granules, when processing the data of a certain chromatographic peak area, the deviation between the initial average and the original data is first calculated. The continuous deviation values ​​in a certain segment of data are 0.25mAU, 0.35mAU, 0.45mAU, 0.55mAU, and 0.65mAU. According to the classification standard, the first two points are assigned a weight coefficient of 0.8, the middle point is assigned a weight coefficient of 0.6, and the last two points are assigned a weight coefficient of 0.4. The deviation change rate of these points is calculated, and it is found that the change rate of 5 consecutive points exceeds 20%, so this area is marked as a fluctuation interval. The local mean is calculated for this area, and the fluctuation characteristic values ​​of adjacent data groups are compared. When the difference between groups is found to exceed 30%, the weight coefficient is attenuated. After local weighted regression optimization, a more accurate weight distribution is obtained, and the correction coefficient reflecting the data quality is obtained through weight combination.

[0099] Example 11: A weight distribution curve of data points is established by using inter-group correction parameters, the curve is fitted in sections, the weights of points where the fitting residual exceeds 0.05 are recalculated, and the weights are optimized using a local weighted regression method to obtain a weight optimization coefficient, including: (1) The data points are arranged in time series by using the inter-group correction parameters, and the data segments are divided into 0.02-min intervals. The weight values ​​of each data point are accumulated and counted to generate the initial weight distribution sequence and obtain the weight basic data. (2) Construct a cubic spline fitting function based on the weighted basic data, divide the data sequence into segments according to the time window of 0.5 min, use the least squares method to perform curve fitting on each segment of data, calculate the correlation coefficient of the fitting equation, and obtain the segmented fitting result; (3) Calculate the fitting residual of each data point through the segmented fitting results, group the residual values ​​into groups according to the interval of 0.01, mark the points with residuals exceeding 0.05 as outliers, and count the distribution density of outliers on the time axis to obtain the distribution characteristics of outliers; (4) A local weighted regression model is established based on the distribution characteristics of the outliers. A calculation window of 0.1 min is set for each outlier. The weight of the data point in the window is inversely proportional to its distance to the center point. The weight of the point with a distance greater than 0.05 min is attenuated to 0.5 times the original value to obtain the local weight coefficient. (5) Recalculate the weight value of the outlier point based on the local weight coefficient, use the exponential smoothing method to perform transition processing on the new and old weights, set the smoothing coefficient to 0.7, correct the weight value in the transition interval, and obtain the optimized weight value; (6) The entire data sequence is updated by optimizing the weight value, the updated weight distribution is compared with the original distribution, a weight adjustment record is generated, and a weight optimization database is established to obtain the weight optimization coefficient.

[0100] Among them, according to the inter-group correction parameters, the data points are arranged in chronological order. 0.02min is used as the basic time interval to divide the data into multiple continuous data segments. The weight values ​​in each data segment are cumulatively counted. This statistical method can reflect the distribution characteristics of the weight on the time axis. The initial weight distribution sequence obtained by cumulative statistics constitutes the basic data for weight optimization. After the basic weight data is determined, it is necessary to construct a cubic spline fitting function for data fitting. 0.5min is selected as the time window to segment the data sequence. This window size can not only ensure the continuity of local data, but also reflect the trend of data changes. The least squares method is used for curve fitting for each data segment. The least squares method can minimize the sum of squares of deviations between the fitting curve and the actual data points. The quality of the fitting effect is evaluated by calculating the correlation coefficient of the fitting equation.

[0101] After the segmented fitting is completed, the fitting residual of each data point needs to be calculated. The residual reflects the deviation between the actual data point and the fitting curve. The residual values ​​are grouped at intervals of 0.01. This grouping method facilitates the identification of outliers. Points with residuals exceeding 0.05 are marked as outliers. The distribution density of outliers on the time axis is statistically analyzed to obtain the distribution characteristics of outliers. The distribution characteristics of outliers reflect the spatial distribution law of data quality. For the identified outliers, a local weighted regression model is established for processing. A calculation window of 0.1min is set for each outlier. Within this window, the weight of the data point is inversely proportional to its distance from the center point. Specifically, when the distance between the data point and the center point exceeds 0.05min, its weight decays to 0.5 times the original weight. This weight distribution method takes into account the spatial correlation of data points and can effectively smooth out abnormal data.

[0102] After obtaining the local weight coefficient, the weight value of the outlier is recalculated. The exponential smoothing method is used to transition the old and new weights, and the smoothing coefficient is set to 0.7. The exponential smoothing method can reduce the impact of random fluctuations while maintaining the data trend. The weight value in the transition interval is corrected to ensure the continuous change of the weight. Finally, the optimized weight value is updated to the entire data sequence, and the updated weight distribution is compared with the original distribution to generate detailed weight adjustment records. These records contain not only the changes in the weights, but also the reasons and process of the adjustments. A weight optimization database is established to store and manage all relevant information of the optimization process.

[0103] For example: divide the data points in the 0.5-min time window into segments at intervals of 0.02 minutes, and perform cumulative statistics on the weight of each segment of data. Then fit the weight distribution through the cubic spline function and calculate the fitting residual of each point. Continuous abnormal points are found in a certain time window, and the residual values ​​of these points exceed the threshold of 0.05. Establish a 0.1-min calculation window for these abnormal points, and calculate new weight values ​​based on the distance between the point and the center point. After exponential smoothing, the weight of the abnormal points is optimized, and the continuity and reliability of the overall data are improved.

[0104] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

[0105] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for determining the content of paeoniflorin in Shaoyaogancao granules, characterized in that: The following steps are involved: S1. Identification of Shaoyaogancao Granules by thin layer chromatography; S2. Determination of the content of paeoniflorin in Shaoyaogancao granules based on high performance liquid chromatography technology; Wherein, step S2 comprises the following steps: Selection of chromatographic operating conditions and parameters: Chromatographic column: C18 column, 4.6mmx250mm, 5μm; Mobile phase A was acetonitrile: mobile phase B was 0.1% phosphoric acid aqueous solution, and the elution conditions were: 0 to 8 min, 13.4% to 16.8% mobile phase A, 86.6% to 83.2% mobile phase B; 8 to 16 min, 16.8% to 22.3% mobile phase A, 83.2% to 77.7% mobile phase B; 16 to 24 min, 22.3% to 25.7% mobile phase A, 77.7% to 74.3% mobile phase B; 24 to 32 min, 25.7% to 19.2% mobile phase A, 74.3% to 80.8% mobile phase B; 32 to 38 min, 19.2% to 13.4% mobile phase A, 80.8% to 86.6% mobile phase B; 38 to 45 min, 13.4% mobile phase A, 86.6% mobile phase B; Flow rate: 0.72mL / min-1.31mL / min; injection volume: 9.45μl-10.55μl; Column temperature intelligent control system: the temperature is set at 28.5℃-31.5℃, and the fluctuation is controlled within ±0.1℃; Dual wavelength detection system: main wavelength 230nm, reference wavelength 270nm, diode array detector combined with mass spectrometer, scanning range: 190-400nm; Preparation of reference solution: Take the paeoniflorin reference substance, weigh accurately, and add methanol to make a solution containing 80 μg per 1 mL. Preparation of the test solution: Take the Shaoyao Gancao granules, grind them into powder, take 0.1g, weigh accurately, and place it in a stoppered conical bottle. Use the temperature gradient ultrasonic extraction method. The power of ultrasonic treatment is 300w and the frequency is 40kHz. The specific steps are as follows: Step 1: Ultrasonic extraction at 25°C for 10 minutes; Step 2: Ultrasonic extraction at 35°C for 10 minutes; Step 3: Ultrasonic extraction at 45°C for 10 minutes; The extraction solvent is a mixed solution of 70% ethanol: methanol: water = 6: 3: 1, 50 mL is accurately added, plugged, weighed, cooled to 26℃-34℃, weighed again, and the lost weight is supplemented with the same solvent, shaken, filtered through a 0.45μm filter membrane, and the filtrate is purified through an ultrafiltration membrane with a molecular weight cutoff of 3000Da to obtain; Determination method: Use an automatic sample injector to control the temperature at 4±0.5℃, accurately aspirate 10μl of the reference solution and the test solution respectively, inject into the liquid chromatograph, measure, record the chromatogram, and use artificial intelligence-assisted peak area automatic integration algorithm for data processing.

2. The method for determining the content of paeoniflorin in Shaoyaogancao granules according to claim 1, characterized in that: The steps of using artificial intelligence assisted peak area automatic integration algorithm for data processing include: The wavelength of the chromatogram was scanned by a diode array detector at a sampling interval of 0.001 min, and a three-dimensional data matrix within the range of 190-400 nm was collected, and signal data of the main wavelength of 230 nm and the reference wavelength of 270 nm were separated and extracted from the data matrix; A 9-point sliding average calculation is performed based on the signal data of the main wavelength of 230nm, and the signal intensity of the 4 points before and after each data point is weighted averaged to obtain the main signal baseline characteristic value; By using the signal data of the reference wavelength of 270nm and taking 0.1min as the window width to calculate the local variance, the baseline characteristic value of the main signal is dynamically calibrated to obtain the baseline correction parameters; A cubic spline function is constructed based on the baseline correction parameters, and the main wavelength signal is corrected in sections, with each section length being 0.5 min, to obtain the chromatographic baseline correction data. The first-order and second-order derivatives were calculated based on the chromatographic baseline correction data, and the boundary of the paeoniflorin peak was located by combining the maximum point and the zero-crossing point of the derivative value to obtain the starting point and end point data of the peak; The integration interval was finely divided at 0.002 min intervals using the starting and ending point data of the peak, and the area with a signal intensity greater than twice the baseline was sampled to obtain the integration range of the paeoniflorin peak.

3. The method for determining the content of paeoniflorin in Shaoyaogancao granules according to claim 2, characterized in that: The 9-point sliding average calculation is performed based on the signal data of the main wavelength of 230 nm, and the signal intensity of the 4 points before and after each data point is weighted averaged to obtain the main signal baseline characteristic value, including: The signal data of the main wavelength of 230nm are used to set three signal intensity thresholds: high, medium and low, which are 8 times, 4 times and 2 times of the baseline intensity respectively. Data are collected in different signal areas to obtain a layered signal sequence. Assigning a weight coefficient to each data point in the layered signal sequence and the four points before and after it to obtain a weighted calculation parameter; Perform a moving sum operation on the 9-point data using the weighted calculation parameters, and divide the calculation result by the sum of the weight coefficients to obtain the initial average value; Establishing an adaptive adjustment range according to the deviation between the initial average value and the original data, performing secondary weighting on the data points with a deviation exceeding 0.2 mAU to obtain a correction coefficient; The initial average value is calibrated by the correction coefficient to generate a target weighted average result to obtain a baseline characteristic value; The data index is constructed through the baseline characteristic value, and the weight distribution and calculation process of each data point are recorded to obtain the main signal baseline characteristic value.

4. The method for determining the content of paeoniflorin in Shaoyaogancao granules according to claim 2, characterized in that: The method of calculating the first-order and second-order derivatives according to the chromatographic baseline correction data, locating the boundary of the paeoniflorin peak in combination with the maximum point and the zero-crossing point of the derivative value, and obtaining the starting point and end point data of the peak includes: A calculation interval of 0.002 min is set by using the chromatographic baseline correction data, and a differential operation is performed on adjacent data points to obtain a first-order derivative value sequence; Performing a secondary difference calculation on the first-order derivative value sequence, quantifying the derivative value change between each data point and its adjacent points, and obtaining a second-order derivative value sequence; Find the point with the largest signal change rate through the first-order derivative value sequence, mark the point with a positive change rate greater than 0.5 mAU / min as a rising edge, and obtain the peak starting range; Determine the inflection point position according to the second-order derivative value sequence, mark the position where the second-order derivative crosses zero and the absolute value of the first-order derivative is greater than 0.2 mAU / min as a key point, and obtain the peak profile feature point; Extending from the peak profile feature point to both sides until the signal intensity drops to 105% of the baseline, performing noise evaluation on the extended interval to obtain a boundary positioning result; The starting point and the end point of the integration interval are determined by the boundary positioning result, the peak integration range data is generated, and the starting point and the end point data of the peak are obtained.

5. The method for determining the content of paeoniflorin in Shaoyaogancao granules according to claim 2, characterized in that: The starting point and end point data of the peak are used to finely divide the integration interval at intervals of 0.002 min, and the area with a signal intensity greater than 2 times the baseline is sampled to obtain the integration range of the paeoniflorin peak, including: The integration interval was stratified by the peak start and end data, and the signal intensity was divided into three response areas: high, medium, and low according to 8 times the baseline, 4 times the baseline, and 2 times the baseline. The high response area was sampled at a 0.001-min interval, the medium response area was sampled at a 0.002-min interval, and the low response area was sampled at a 0.004-min interval, thus obtaining a stratified sampling sequence. According to the stratified sampling sequence, linear interpolation calculation is performed on adjacent sampling points, encrypted points are inserted in areas where the signal change rate is greater than 0.8 mAU / min, and point dilution is performed in areas where the change rate is less than 0.2 mAU / min, so as to obtain an optimized sampling point distribution; The peak key points are determined by optimizing the sampling point distribution, and the points with the absolute value of the first-order derivative greater than 0.5 mAU / min are marked as significant change areas. The significant change areas are resampled with a minimum sampling interval of 0.001 min to obtain a key point sequence; A peak profile characteristic diagram is established from the key point sequence, the tangent slope at each sampling point is calculated, and the interval where the slope changes by more than 50% is determined as the peak distortion area. The sampling density of the distortion area is increased to 0.0005 min interval to obtain a modified sampling plan; According to the modified sampling scheme, the boundary area of ​​the peak is finely processed, and the sampling interval is adaptively adjusted according to the signal intensity change rate within the range of 0.5 min before the peak starting point to 0.5 min after the peak end point, with the minimum interval being 0.001 min and the maximum interval being 0.005 min, to obtain boundary sampling data; By integrating the boundary sampling data with the peak body sampling data, an integration point database is established, the sampling point information is associated with the corresponding signal intensity and stored, an integration interval configuration file of the paeoniflorin peak is generated, and the integration range of the paeoniflorin peak is obtained.

6. The method for determining the content of paeoniflorin in Shaoyaogancao granules according to claim 3, characterized in that: The adaptive adjustment range is established according to the deviation between the initial average value and the original data, and the data points with deviation exceeding 0.2 mAU are weighted twice to obtain the correction coefficient, including: By performing a difference operation between the initial average value and the original data, the deviation value of each data point is graded at an interval of 0.05mAU, and a weight coefficient of 0.8 is assigned to points with a deviation value in the range of 0.2-0.4mAU, a weight coefficient of 0.6 is assigned to points in the range of 0.4-0.6mAU, and a weight coefficient of 0.4 is assigned to points greater than 0.6mAU, to obtain a deviation graded sequence; Calculate the deviation change rate of adjacent points according to the deviation classification sequence, mark the area where the deviation change rate of 5 consecutive points is greater than 20% as the fluctuation interval, and perform local mean calculation on the data points in the fluctuation interval using a 0.01 minute time window to obtain the fluctuation characteristic value; The marked points are grouped according to the fluctuation characteristic values, and the fluctuation characteristic values ​​of adjacent groups are compared. When the difference between groups exceeds 30%, the weight coefficient of the area where the difference between groups exceeds 30% is attenuated by 0.1 to obtain the inter-group correction parameter; A weight distribution curve of the data points is established by the inter-group correction parameters, the curve is fitted in sections, the weights of the points whose fitting residuals exceed 0.05 are recalculated, and the weights are optimized by a local weighted regression method to obtain a weight optimization coefficient; The original deviation value is corrected and calculated according to the weight optimization coefficient, and the corrected deviation value and the initial weight are weighted averaged to generate a target weight distribution scheme to obtain an adjustment coefficient; The correction coefficient is generated by combining the adjustment coefficient with the initial weighted result.

7. The method for determining the content of paeoniflorin in Shaoyaogancao granules according to claim 6, characterized in that: The weight distribution curve of the data points is established by the inter-group correction parameters, the curve is segmentedly fitted, the weights of the points whose fitting residuals exceed 0.05 are recalculated, and the weights are optimized by the local weighted regression method to obtain the weight optimization coefficient, including: The data points are arranged in time series by using the inter-group correction parameters, the data segments are divided into basic intervals of 0.02 minutes, the weight values ​​of each data point are accumulated and counted, an initial weight distribution sequence is generated, and weight basic data is obtained; A cubic spline fitting function is constructed according to the weighted basic data, the data sequence is segmented according to a time window of 0.5 minutes, a least square method is used to perform curve fitting on each segment of data, and the correlation coefficient of the fitting equation is calculated to obtain a segmented fitting result; The fitting residual of each data point is calculated by the segmented fitting result, the residual values ​​are grouped at intervals of 0.01, the points with residuals exceeding 0.05 are marked as abnormal points, and the distribution density of the abnormal points on the time axis is counted to obtain the distribution characteristics of the abnormal points; A local weighted regression model is established based on the distribution characteristics of the outliers. A calculation window of 0.1 min is set for each outlier. The weight of the data point in the window is inversely proportional to the distance from the center point. The weight of the point with a distance greater than 0.05 min is attenuated to 0.5 times the original value to obtain the local weight coefficient. The weight value of the outlier point is recalculated according to the local weight coefficient, and the exponential smoothing method is used to perform transition processing on the new and old weights, the smoothing coefficient is set to 0.7, and the weight value of the transition interval is corrected to obtain the optimized weight value; The entire data sequence is updated by using the optimized weight value, the updated weight distribution is compared with the original distribution, a weight adjustment record is generated, and a weight optimization database is established to obtain a weight optimization coefficient.

8. The method for determining the content of paeoniflorin in Shaoyaogancao granules according to claim 1, characterized in that: In the step of selecting chromatographic conditions, octadecylsilane bonded silica gel is used as filler, and acetonitrile-0.1% phosphoric acid solution is used as mobile phase; the detection wavelength is 230 nm, and the theoretical plate number calculated based on the paeoniflorin peak should be no less than 5000.

9. The method for determining the content of paeoniflorin in Shaoyaogancao granules according to claim 1, characterized in that: The Shaoyao Gancao granules were prepared in the following manner: Get 3450g white peony root and 3450g fried licorice, add 5-8 times water, boil hot water to add the materials, extract once, reflux extraction for 30-120 minutes, filter, concentrate to a clear paste with a relative density of 1.08-1.12, dry, take the dry extract powder, add auxiliary materials, mix well, dry granulate, and make 1000g.

10. The method for determining the content of paeoniflorin in Shaoyaogancao granules according to claim 1, characterized in that: Step S1 includes the following steps: Take 2g of each of Shaoyaogancao granules sample, white peony negative sample, licorice negative sample, and auxiliary material sample, add 50mL of water to dissolve, extract with water saturated n-butanol 3 times, 20mL each time, combine the n-butanol solution, wash with water 3 times, 20mL each time, discard the water solution, evaporate the n-butanol solution to dryness, and add 5mL of methanol to dissolve the residue as the test solution; take the paeoniflorin reference substance, add methanol to make a solution containing 2mg per 1mL as the reference substance solution; take the ammonium glycyrrhizinate reference substance, add methanol to make a solution containing 2mg per 1mL as the reference substance solution; take 1g of licorice reference medicinal material powder, add 40mL of ether, heat and reflux for 1 hour, filter, discard the ether solution, add 30mL of methanol to the residue, heat and reflux for 1 hour, filter, evaporate the filtrate, add 40mL of water to dissolve the residue, extract with water saturated n-butanol 3 times, 20mL each time, combine the n-butanol solution, wash with water 3 times, 20mL each time mL, discard the water, evaporate the n-butanol solution, add 5 mL of methanol to the residue to dissolve it as the licorice control medicinal material solution; according to the thin layer chromatography test, take 2-4 μl of the test solution, the reference solution and the licorice control medicinal material solution, respectively, and spot them on the same silica gel G thin layer plate prepared with 1% sodium hydroxide solution, use ethyl acetate: formic acid: glacial acetic acid: water with a mass ratio of 15:1:1:2 as the developing solvent, develop, take out, dry, spray with 10% sulfuric acid ethanol solution, heat at 105℃ until the spots are clearly colored, and under sunlight, in the chromatogram of the test sample, at the corresponding position with the chromatogram of the peony glycoside reference substance, the same color spot appears; under ultraviolet light, in the chromatogram of the test sample, at the corresponding position with the chromatogram of the licorice control medicinal material, the same color fluorescent spot appears; at the corresponding position with the chromatogram of the licorice control medicinal material, the same fluorescent spot appears.

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