A detection method of UPLC fingerprint spectrum of a traditional Chinese medicine composition for treating gallstones and promoting bile secretion

Abstract

The application provides a kind of Xiaoshi Lidan traditional Chinese medicine composition UPLC fingerprint detection method, and combines chemical pattern recognition method, 12 common peaks are calibrated, 8 common peaks are identified among them by chemical reference substance, the similarity of 15 batches of Xiaoshi Lidan traditional Chinese medicine composition fingerprint is above 0.932, through HCA, PCA and OPLS-DA etc. Analysis and verification of neural network model, 15 batches of Xiaoshi Lidan traditional Chinese medicine composition samples are divided into 3 categories, and 7 main chemical markers that may cause quality difference are screened out, which are 2, 6 (baicalin), 4, 8, 7, 9 (rhein) and 5 (hesperidin) chromatographic peaks. The fingerprint detection method has the advantages of simplicity, feasibility, short detection time (about 25 minutes), stability and good repeatability, can evaluate the internal quality of Xiaoshi Lidan traditional Chinese medicine composition, further guarantee the internal quality stability of product, and provide reference.

Description

Technical Field

[0001] This invention belongs to the field of quality testing of chemical components of traditional Chinese medicine, and in particular relates to a UPLC fingerprinting method for detecting gallstone-dissolving and choleretic traditional Chinese medicine compositions. Background Technology

[0002] The cholelithiasis-dissolving and choleretic traditional Chinese medicine composition of this invention is a cholelithiasis-dissolving and choleretic capsule produced by Baoding Tianhao Pharmaceutical Co., Ltd. This medicine has the effects of soothing the liver and gallbladder, regulating qi and relieving pain. Its formula is prepared from 13 traditional Chinese medicines: Bupleurum chinense, Citrus reticulata peel, Scutellaria baicalensis, Paeonia lactiflora, Rheum palmatum, Curcuma longa, Lysimachia christinae, Lygodium japonicum, Gallus gallus domesticus gizzard lining, Artemisia capillaris, Curcuma longa, Sparganium stoloniferum (processed with vinegar), and Clematis chinensis. This formula integrates liver-soothing, gallbladder-dissolving, stone-expelling, stone-dissolving, heat-clearing, and blood-stasis-removing effects, with proven clinical efficacy, making it a safe and reliable formula for treating cholelithiasis.

[0003] A systematic review of existing technical literature on this product reveals the following: Sun Yantao, *HPLC Dual-Wavelength Method for Simultaneous Determination of Paeoniflorin, Hesperidin, Baicalin and Emodin in Xiaoshi Lidan Capsules*, *Chinese Pharmacist*, Publication Date: 2016-04-05. This paper uses acetonitrile-0.2% phosphoric acid solution as the mobile phase, gradient elution, detection wavelengths of 230nm and 280nm, column temperature: 40℃. This detection method is used to determine the content of paeoniflorin, hesperidin, baicalin and emodin in Xiaoshi Lidan Capsules. Chen Xisheng, Hu Binxiang; *HPLC Method for Determination of Baicalin Content in Xiaoshi Lidan Capsules*, *China Modern Pharmaceutical Application*, Publication Date: 2009-08-10. This paper uses acetonitrile-0.2% phosphoric acid (27:73) as the mobile phase, detection wavelength of 280nm, to determine the baicalin content in Xiaoshi Lidan Capsules. Ai Weixia, Determination of Baicalin Content in Xiaoshi Lidan Pills, Journal of Jianghan University, Publication Date: 2006-06-25. The content of baicalin in Xiaoshi Lidan Pills was determined using complex extraction-ultraviolet spectrophotometry at a wavelength of 262 nm. Therefore, this study established a fingerprinting method for Xiaoshi Lidan capsules using UPLC and combined it with chemical pattern recognition analysis to provide an analytical method reference for the quality control and standard improvement of this compound preparation, aiming to more comprehensively, scientifically, and effectively control the quality of this compound preparation and ensure its consistent, safe, and effective application in traditional Chinese medicine clinical practice.

[0004] Most traditional Chinese medicine (TCM) preparations originate from ancient TCM formulas, are clinically effective, and represent a core application of TCM's holistic approach, syndrome differentiation and treatment, and drug compatibility. Furthermore, because most TCM compound formulas are made from multiple, even more than ten, herbs, their medicinal components are highly complex. With the continuous deepening of research on the chemical composition, pharmacodynamics, pharmacokinetics, and mechanisms of action of TCM preparations, analytical and testing technologies for these drugs have also shown rapid development, and standards for TCM preparations have made significant progress. Detection methods with weak specificity and poor repeatability, such as thin-layer chromatography and physicochemical identification, are gradually being replaced by advanced and mature modern analytical techniques and methods such as fingerprinting, characteristic spectroscopy, multi-analysis, UPLC, and LC-MS. Xiaoshi Lidan Capsules are included in the "National Compendium of Standards for Traditional Chinese Medicine Preparations: Internal Medicine and Hepatobiliary Section," but this standard only specifies requirements for identification and testing items, which is relatively weak in terms of controlling drug quality at the current stage. Current research on Xiaoshi Lidan capsules mainly focuses on clinical efficacy observation and pharmacodynamic analysis of combined medication, while research on drug quality standards and content determination is very limited and the methods are relatively simple. Summary of the Invention

[0005] This invention provides a UPLC fingerprinting method for detecting gallstone-dissolving and choleretic traditional Chinese medicine compositions. Combined with chemical pattern recognition, 12 common peaks were identified, and 8 of these were further identified using chemical reference standards. The similarity of the fingerprint spectra of 15 batches of gallstone-dissolving and choleretic traditional Chinese medicine compositions was all above 0.932. Through HCA, PCA, and OPLS-DA analyses and validation using a neural network model, the 15 batches of gallstone-dissolving and choleretic traditional Chinese medicine composition samples were divided into 3 categories, and 7 major chemical markers that may lead to quality differences were screened out: peaks 2 and 6 (baicalin), 4, 8, 7, and 9 (rhein), and peak 5 (hesperidin). This fingerprinting method is simple and feasible, with a short detection time (approximately 25 minutes), good stability and repeatability, and can provide a more scientific reference for evaluating the intrinsic quality of gallstone-dissolving and choleretic traditional Chinese medicine compositions.

[0006] The technical solution of this invention patent application is as follows:

[0007] A UPLC detection method for a traditional Chinese medicine composition for dissolving stones and promoting bile secretion, characterized in that the detection method includes the following steps:

[0008] (1) Preparation of the test solution: Weigh the contents of the choleretic capsules, add 100% methanol, weigh the contents, sonicate, cool, replenish the lost mass with 100% methanol, shake well, filter, and take the filtrate to obtain the test solution.

[0009] (2) Preparation of mixed reference solution: Weigh each reference standard and add methanol to prepare a mixed solution containing 10-30 ug / ml hesperidin, 20-40 ug / ml baicalin, 20-25 ug / ml curcumin, 20-25 ug / ml narcissin, 20-30 ug / ml gallic acid, 20-30 ug / ml rhein, 20-30 ug / ml emodin, and 15-25 ug / ml rhein per 1 mL.

[0010] (3) Chromatographic conditions:

[0011] Column: C 18 Mobile phase: A-acetonitrile, B-0.1% formic acid aqueous solution; gradient elution: 0–3 min, 10%–13% A; 3–4 min, 13%–19% A; 4–18 min, 19%–30% A; 18–20 min, 30%–45% A; 20–25 min, 45%–100% A; 25–28 min, 100%–10% A; 28–30 min, 10%–10% A; flow rate: 0.1–0.5 mL / min; detection wavelength: 250–300 nm; column temperature: 25–35 °C.

[0012] (4) Fingerprint mapping

[0013] Take multiple batches of choleretic and litholytic powder, take the test solution from step (1), and inject and determine it according to the chromatographic conditions in step (3). Record the chromatograms of each sample and import them into the "Traditional Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System" software for processing. Generate the reference chromatogram R by the median method to obtain the UPLC fingerprint of the choleretic and litholytic traditional Chinese medicine composition.

[0014] Preferably, in step (1) of the detection method, the ultrasonic power is 700-900W, the ultrasonic frequency is 30-50kHz, and the ultrasonic time is 20-40min.

[0015] Preferably, in step (1) of the detection method, the ultrasonic power is 800W, the ultrasonic frequency is 40kHz, and the ultrasonic time is 30min.

[0016] Preferably, in step (2) of the detection method, the concentrations of hesperidin, baicalin, curcumin, naringin, gallic acid, rhein, emodin, and chrysophanol in the mixed reference solution are: 24.00 ug / ml, 29.30 ug / ml, 22.10 ug / ml, 22.80 ug / ml, 25.10 ug / ml, 23.70 ug / ml, 23.40 ug / ml, and 21.60 ug / ml.

[0017] Preferably, the detection method step (3C) 18The model is Waters Acquity UPLC BEH, and the column specifications are 1.7μm and 2.1×100mm.

[0018] Preferably, in step (3) of the detection method, the chromatographic conditions are: flow rate: 0.3 mL / min; detection wavelength: 288 nm; column temperature: 30 °C.

[0019] Preferably, the quantity of multiple batches in step (4) is ≥10.

[0020] Preferably, the detection method is used in the quality testing, intermediate content determination, and identification of effective components of traditional Chinese medicine compositions for dissolving gallstones and promoting bile secretion.

[0021] To further illustrate the inventiveness of the detection method for the traditional Chinese medicine composition for dissolving stones and promoting bile secretion of the present invention, some of the experimental contents of the screening of the technical solution of the present invention are summarized as follows.

[0022] This study optimized and determined the extraction method, number of extractions, extraction solvent, and extraction time of the traditional Chinese medicine composition for dissolving stones and promoting bile secretion through single-factor multi-level experiments.

[0023] 1.1 Screening of extraction methods (static, ultrasonic, reflux)

[0024] Chromatographic column: Waters Acquity UPLC BEH C18 (1.7 μm, 2.1 × 100 mm); mobile phase: A-acetonitrile, B-pure water; gradient elution (0–30 min, 10%–90% A; 30–35 min, 90%–10% A; 35–40 min, 10%–10% A); flow rate: 0.2 mL / min; detection wavelength: 254 nm; column temperature: 30 °C; injection volume: 2 μL.

[0025] 1.1.1 Let stand

[0026] Take 4.0 g of the test sample powder and place it in a stoppered conical flask. Accurately add 120 mL of methanol, tighten the screw, let stand for 24 h, filter, and take the filtrate. Filter through a 0.22 μm microporous membrane to obtain the final sample. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown below. Figure 1 -S1.

[0027] 1.1.2 Ultrasound

[0028] Take 4.0 g of the test sample powder and place it in a stoppered conical flask. Accurately add 120 mL of methanol, tighten the screw, and sonicate (400 W, 40 kHz) for 1 h. Filter the solution and pass the filtrate through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown below. Figure 1 -S2.

[0029] 1.1.3 Reflux

[0030] Take 4.0 g of the test sample powder, place it in a stoppered conical flask, accurately add 120 mL of methanol, tighten, reflux for 1 h, cool, filter, and filter the filtrate through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. Experimental results showed that, by comparing the UPLC chromatograms S1–S3 after standing, sonication, and reflux, the baseline stability, overall peak shape, peak position, peak size, and number of peaks were comparable. The peak positions were basically consistent, but the peak sizes and numbers showed some differences. Specifically, the number of peaks in S2 and S3 was significantly greater than in S1, especially between retention times of 12–15 min; the peak sizes in S2 and S3 were also significantly larger than in S1, especially between retention times of 5–8 min. While there were no significant differences in the above indicators between S2 and S3, the baseline deviation of S3 was higher than that of S2 between retention times of 7–9 min. Furthermore, reflux (S3) is less convenient for sample preparation than ultrasound (S2). Therefore, ultrasound was ultimately chosen as the extraction method.

[0031] 1.3 Screening of extraction solvents

[0032] The chromatographic conditions are the same as in section 1.1.

[0033] 1.3.1 Pure water

[0034] Take 0.5 g of the test sample powder, place it in a capped PVC tube, accurately add 15 mL of pure water, tighten the tube, sonicate (400 W power, 40 kHz frequency) for 1 h, filter, and take the filtrate, filtering it through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown in the appendix to the instruction manual. Figure 11 -S1.

[0035] 1.3.2 Ethanol

[0036] Take 0.5 g of the test sample powder, place it in a capped PVC tube, accurately add 15 mL of ethanol, tighten the tube, sonicate (400 W, 40 kHz) for 1 h, filter, and take the filtrate, filtering it through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown in the appendix to the instruction manual. Figure 11 -S2.

[0037] 1.3.3 Acetonitrile

[0038] Take 0.5 g of the test sample powder, place it in a capped PVC tube, accurately add 15 mL of acetonitrile, tighten the tube, sonicate (400 W, 40 kHz) for 1 h, filter, and take the filtrate, filtering it through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown in the appendix to the instruction manual. Figure 11 -S3.

[0039] 1.3.4 Methanol

[0040] Take 0.5 g of the test sample powder, place it in a capped PVC tube, accurately add 15 mL of methanol, tighten the tube, sonicate (400 W, 40 kHz) for 1 h, filter, and take the filtrate, filtering it through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown in the appendix to the instruction manual. Figure 11 -S4.

[0041] 1.3.5 90% Methanol

[0042] Take 0.5 g of the test sample powder, place it in a capped PVC tube, accurately add 15 mL of 90% methanol, tighten the tube, sonicate (400 W, 40 kHz) for 1 h, filter, and take the filtrate, filtering it through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown in the appendix to the instruction manual. Figure 11 -S5.

[0043] 1.3.6 70% Methanol

[0044] Take 0.5 g of the test sample powder, place it in a capped PVC tube, accurately add 15 mL of 70% methanol, tighten the tube, sonicate (400 W, 40 kHz) for 1 h, filter, and take the filtrate, filtering it through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown below. Figure 3 -S6.

[0045] 1.3.7 50% Methanol

[0046] Take 0.5 g of the test sample powder, place it in a capped PVC tube, accurately add 15 mL of 50% methanol, tighten the tube, sonicate (400 W, 40 kHz) for 1 h, filter, and take the filtrate, filtering it through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown in the appendix to the instruction manual. Figure 11 -S7.

[0047] 1.3.8 30% Methanol

[0048] Take 0.5 g of the test sample powder, place it in a capped PVC tube, accurately add 15 mL of 30% methanol, tighten the tube, sonicate (400 W, 40 kHz) for 1 h, filter, and take the filtrate, filtering it through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions in section 1.1. The UPLC chromatogram is shown in the appendix to the instruction manual. Figure 11 -S8.

[0049] 1.3.9 10% Methanol

[0050] Take 0.5 g of the test sample powder, place it in a capped PVC tube, accurately add 15 mL of 10% methanol, tighten the tube, sonicate (400 W, 40 kHz) for 1 h, filter, and take the filtrate, filtering it through a 0.22 μm microporous membrane. Inject the sample according to the chromatographic conditions described in section 1.1. Experimental results can be found in the appendix to the instruction manual. Figure 11 As shown, by comparing UPLC chromatograms S1–S9 of nine different extraction solvents (pure water, ethanol, acetonitrile, methanol, 90% methanol, 70% methanol, 50% methanol, 30% methanol, and 10% methanol), the baseline stability was comparable, but there were differences in peak position, peak size, and peak number. S2 had the fewest peaks, which does not meet the requirements for fingerprinting method development. S3 had more peaks than S2, but with a retention time of 7–8 min, it lacked several peaks with larger peak areas compared to S1 and S4–S9, and was therefore excluded. S1, S8, and S9 were similar, but with a retention time of 22–23 min, they lacked one peak with a better shape compared to S4–S7, and were thus excluded. Among the remaining S4–S7, S4 showed better performance in terms of stability, number of peaks, and peak size. Meanwhile, it can also be seen that when the extraction solvent is methanol (S4), the number of impurity peaks in the fingerprint spectrum is less than when the extraction solvents are 90% methanol (S5), 70% methanol (S6), and 50% methanol (S7). In conclusion, methanol was ultimately determined to be the extraction solvent.

[0051] 1.4 Extraction Time Filtering

[0052] By comparing UPLC chromatograms S1–S4 obtained at four different extraction times (0.5h, 1h, 1.5h, and 2h), the baseline stability, overall peak shape, peak position, peak size, and peak number remained largely consistent. Therefore, considering that increasing the extraction time would reduce the fingerprint detection efficiency, the extraction time was ultimately determined to be 0.5h.

[0053] 1.5 Ultrasonic Power Screening

[0054] By comparing UPLC chromatograms S1–S4 obtained at four different ultrasonic powers (800W, 400W, 200W, and 100W), the baseline stability, overall peak shape, elution position, and peak size remained largely consistent. However, the number of peaks differed slightly; S1 (800W) had more peaks in the retention time range of 6–10 min than S2–S4. Furthermore, the experiment revealed that lower ultrasonic powers may cause the sample in the PVC tube to adhere to the bottom during ultrasonication and be difficult to mix. Therefore, considering the feasibility and wide applicability of the sample preparation method, the extraction power was ultimately determined to be 800W.

[0055] 2. Optimization of chromatographic conditions

[0056] Preparation method of test sample: Take 0.5g of test sample powder, place it in a capped PVC tube, accurately add 15mL of methanol, tighten, sonicate (power 800W, frequency 40kHz) for 0.5h, filter, take the filtrate and filter it through a 0.22μm microporous membrane to obtain the test sample.

[0057] 2.1 Selection of chromatographic column

[0058] Four commonly available UPLC columns (C18) of different brands, models, and parameters were selected for evaluation. The different columns were: Waters Acquity UPLC BEH C18 (1.7 μm, 2.1 × 100 mm), Agilent Zrobax RRHDSB-C18 (1.8 μm, 2.1 × 100 mm), SHIMADZU Shim-pack GIST-HP C18-AQ (3 μm, 2.1 × 100 mm), and Dikma Endeavorsil. Effect of C18-A (1.8 μm, 2.1 × 100 mm) on separation performance. Mobile phase: A-acetonitrile, B-pure water; gradient elution (0–30 min, 10%–90% A; 30–35 min, 90%–10% A; 35–40 min, 10%–10% A); flow rate: 0.2 mL / min; detection wavelength: 254 nm; column temperature: 25 °C; injection volume: 2 μL.

[0059] Experimental Results: By comparing the chromatograms of four commercially available ultra-high performance liquid chromatography (UHPLC) columns—Waters BEH, Agilent RRHD, Shimadzu GIST-HP, and Dikma Endeavorsil—differences were observed in terms of baseline stability, overall peak shape, peak position, peak size, and peak quantity. Specifically, S2 exhibited poor peak shapes for its two main peaks eluting between retention times of 23 and 26 min, and its baseline was uneven between retention times of 8 and 12 min, thus it was excluded. S3 (Shimadzu GIST-HP) had the fewest peaks before retention time of 7 min, and was also excluded. S4 (Dikma Endeavorsil) showed poor peak shapes between retention times of 6 and 10 min, while S1 (Waters BEH) had significantly more peaks than S2-S4 before retention time of 13 min, and its baseline remained generally stable. Therefore, in summary, the chromatographic column was finally determined to be Waters Acquity UPLC BEH C18 (1.7 μm, 2.1 × 100 mm).

[0060] 2.2 Selection of Mobile Phase

[0061] The effects of methanol-water, acetonitrile-water, methanol-0.05% phosphoric acid solution, methanol-0.1% phosphoric acid solution, methanol-0.05% formic acid solution, methanol-0.1% formic acid solution, acetonitrile-0.05% phosphoric acid solution, acetonitrile-0.1% phosphoric acid solution, acetonitrile-0.05% formic acid solution, and acetonitrile-0.1% formic acid solution on the separation efficiency were investigated. Chromatographic column: WatersAcquity UPLC BEH C18 (1.7 μm, 2.1 × 100 mm); flow rate: 0.2 mL / min; detection wavelength: 254 nm; column temperature: 25℃; injection volume: 2 μL.

[0062] 2.2.1 Methanol-Water

[0063] Samples were prepared according to the test sample preparation method described in section 2, and injected under the chromatographic conditions described in section 2.2, using the same mobile phase system and elution program. A gradient elution program was used, with the mobile phase being methanol A-water B, 0-30 min, A; methanol (10%), water B (90%), 35 min, A; methanol (100%), water B (0%), 40 min, A; methanol (10%), water B (90%). Other mobile phases were: acetonitrile-water, methanol-0.05% phosphoric acid water, methanol-0.1% phosphoric acid water, methanol-0.05% formic acid water, methanol-0.1% formic acid water, acetonitrile-0.05% phosphoric acid water, acetonitrile-0.1% phosphoric acid water, acetonitrile-0.05% formic acid water, and acetonitrile-0.1% formic acid water, with all mobile phase ratios being the same.

[0064] Experimental results instructions attached Figure 12 As shown, there are certain differences in terms of baseline stability, overall peak shape, elution position, peak size, and number of peaks. Specifically, S2, lacking a major peak between retention times of 16 and 18 min, was excluded. S1 and S3-S6 showed little difference in baseline stability, overall peak shape, elution position, peak size, and number of peaks, but exhibited smaller peak areas and no obvious peaks between retention times of 3 and 12 min. S7-S10 shifted all peaks forward, effectively saving time. S7 and S10 separated significantly more peaks between retention times of 8 and 10 than S8-S9. Furthermore, between retention times of 10 and 12 min, S10 showed better peak separation than S7. In conclusion, the final mobile phase system was determined to be acetonitrile-0.1% formic acid water.

[0065] 2.3 Selection of detection wavelength

[0066] By comparing UPLC chromatograms at five different detection wavelengths (210nm, 230nm, 265nm, 280nm, and 288nm), differences were observed in terms of baseline stability, overall peak shape, peak position, peak size, and peak number. Specifically, S1 (210nm) and S2 (230nm) had unstable baselines and were therefore excluded. S3 (265nm) showed a low peak response between 7 and 9 minutes of retention time and was also excluded. The chromatograms of S4 (280nm) and S5 (288nm) were essentially equivalent, but S5 (288nm) showed a slightly higher peak response between 19 and 25 minutes. Therefore, the final detection wavelength was determined to be 288nm.

[0067] 2.4 Column Temperature Selection

[0068] By comparing UPLC chromatograms at four different column temperatures (25℃, 30℃, 35℃, and 40℃), the baseline stability, overall peak shape, peak size, and number of peaks were found to be largely consistent, with only slight differences in peak position. As the column temperature increased, the overall peak position shifted forward. This forward peak position saves time and mobile phase consumption, but also reduces column life. Considering mobile phase consumption, the actual operating environment of the equipment, and column durability, a final column temperature of 30℃ was determined.

[0069] 2.5 Selection of Flow Rate

[0070] By comparing UPLC chromatograms S1–S5 at five different flow rates (0.1 mL / min, 0.2 mL / min, 0.3 mL / min, 0.4 mL / min, and 0.5 mL / min), differences were observed in baseline stability, overall peak shape, peak size, elution position, and number of peaks. As the flow rate increased, the overall peak elution position shifted forward, and the peak area gradually decreased. Meanwhile, although the S1 (0.1 mL / min) chromatogram had a larger peak area and higher response, the baseline was relatively uneven, and the elution time was too long, affecting efficiency and increasing mobile phase consumption. Therefore, considering both the overall chromatogram stability and mobile phase consumption, a flow rate of 0.2 mL / min was ultimately determined.

[0071] 2.6 Selection of elution conditions

[0072] Chromatographic column: Waters Acquity UPLC BEH C18 (1.7 μm, 2.1 × 100 mm); detection wavelength: 288 nm; column temperature: 30 degrees Celsius; injection volume: 2 μL.

[0073] 2.6.1 Isocratic elution

[0074] By comparing the UPLC chromatograms (S1–S5) of isocratic elution under five different mobile phase ratios—acetonitrile:0.1% formic acid solution (10:90) for 40 min, acetonitrile:0.1% formic acid solution (30:70) for 40 min, acetonitrile:0.1% formic acid solution (50:50) for 40 min, acetonitrile:0.1% formic acid solution (70:30) for 40 min, and acetonitrile:0.1% formic acid solution (90:10) for 40 min—the results (baseline stability, overall peak shape, peak size, elution position, and number of peaks) were all poor. Therefore, after comprehensive consideration, it was determined that this test solution is not suitable for elution separation using an isocratic elution system.

[0075] Table 1. Partial screening process data for the elution ratio conditions of the chromatographic mobile phase in this invention.

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] The beneficial effects of the patented technical solution of this invention are as follows:

[0085] (1) Through extensive experimentation and trial, the optimal chromatographic conditions were finally found: A-acetonitrile, B-0.1% formic acid aqueous solution, gradient elution: 0-3 min, 10%-13% A; 3-4 min, 13%-19% A; 4-18 min, 19%-30% A; 18-20 min, 30%-45% A; 20-25 min, 45%-100% A; 25-28 min, 100%-10% A; 28-30 min, 10%-10% A.

[0086] (2) This invention also utilizes similarity evaluation, hierarchical cluster analysis (HCA), principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and neural network models to analyze and evaluate the fingerprint spectra of 15 batches of traditional Chinese medicine compositions for dissolving gallstones and promoting bile secretion. A total of 12 common peaks were identified in the HPLC fingerprint spectra of the gallstone-dissolving and bile secretion traditional Chinese medicine compositions. Eight of these common peaks were identified using chemical reference standards. The similarity of the fingerprint spectra of the 15 batches of gallstone-dissolving and bile secretion traditional Chinese medicine compositions was all above 0.932. Through HCA, PCA, and OPLS-DA analyses, and verification using the neural network model, the 15 batches of gallstone-dissolving and bile secretion traditional Chinese medicine composition samples were divided into 3 categories, and 7 major chemical markers that may lead to quality differences were screened out, namely chromatographic peaks 2 and 6 (baicalin), 4, 8, 7, 9 (rhein), and 5 (hesperidin).

[0087] (3) Methodological investigation revealed that the precision test results showed that the relative retention time RSD of each common peak was 0.002%–0.158%, and the relative peak area RSD was 0.235%–2.154%, indicating good instrument precision. The stability test results showed that the relative retention time RSD of each common peak was 0.002%–0.189%, and the relative peak area RSD was 0.453%–3.014%, indicating good stability of the test solution within 24 hours. The repeatability test results showed that the relative retention time RSD of each common peak was 0.002%–0.165%, and the relative peak area RSD was 0.463%–3.023%, indicating good repeatability of the method.

[0088] (4) After establishing HPLC fingerprint chromatograms of 15 batches of Xiaoshi Lidan capsules and evaluating their similarity, this invention uses the peak areas of 12 common peaks obtained from the HPLC fingerprint chromatograms as variables. Chemical pattern recognition in chemometrics is then used to further explain the quality differences between different batches of Xiaoshi Lidan capsules. Furthermore, major biomarkers with significant influence on the quality of Xiaoshi Lidan capsules are screened to explain the main factors contributing to the quality differences. Analysis using similarity evaluation software shows that the similarity of the fingerprint chromatograms of the 15 batches of Xiaoshi Lidan traditional Chinese medicine compositions ranges from 0.932 to 1.000. A total of 12 common peaks were identified, and 8 of these common peaks were identified using reference standards, namely gallic acid, naringin, hesperidin, baicalin, rhein, curcumin, emodin, and chrysophanol. Hesperidin can relax the sphincter of Oddi, contract the gallbladder, and promote bile excretion. It can also reduce plasma and liver cholesterol, liver triglyceride levels, and the activities of 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase and acyl-CoA cholesterol transferase (ACAT) in rats, while significantly reducing the content of neutral cholesterol in animal excrement. Baicalin has pharmacological effects such as antiviral, anti-inflammatory, antibacterial, antitumor, neuroprotective, antioxidant, hemostatic, and antitumor properties. In terms of liver protection, emodin can treat carbon tetrachloride-induced liver fibrosis in rats by inhibiting the synthesis of connective tissue growth factor (CTGF) and matrix metalloproteinase inhibitor-1 (TIMP-1) and promoting the expression of matrix metalloproteinase 9 (MMP-9).

[0089] Cluster analysis using unsupervised pattern recognition was used to preliminarily evaluate the quality consistency of the choleretic and litholytic traditional Chinese medicine composition. Principal component analysis was used to compare the quality of each group, initially identifying components that influence quality differences. Orthogonal partial least squares discriminant analysis was further employed to screen components with significant impact on sample grouping. The main components contributing relatively large to quality differences were identified as peak 1 (gallic acid), peak 5 (hesperidin), peak 6 (baicalin), peak 9 (rhein), peak 12 (chrysophanol), peak 3 (narcissin), and peak 11 (emodin). This indicates that the pharmacological effect of the choleretic and litholytic capsule is the result of the combined action of multiple components, which is basically consistent with the holistic treatment concept of traditional Chinese medicine. By paying attention to the quality differences that may be caused by the above seven components and tracking the changes in the content of quality markers during the production process, the quality uniformity of the choleretic and litholytic traditional Chinese medicine composition of this invention can be better guaranteed. Attached Figure Description

[0090] Figure 1 HPLC fingerprints of 15 batches of traditional Chinese medicine compositions for dissolving stones and promoting bile secretion (S1-S15);

[0091] Figure 2Fingerprint (R) of the Chinese herbal composition for dissolving stones and promoting bile secretion; among which, chromatographic peaks 2 and 6 are baicalin, chromatographic peaks 4, 8, 7 and 9 are rhein, and chromatographic peak 5 is hesperidin;

[0092] Figure 3 HPLC fingerprint of mixed reference standards;

[0093] Figure 4 HPLC fingerprint of a traditional Chinese medicine composition for dissolving stones and promoting bile secretion;

[0094] Figure 5 Dendrogram of cluster analysis of 15 batches of traditional Chinese medicine compositions for dissolving stones and promoting bile secretion;

[0095] Figure 6 Factor analysis chart of 15 batches of traditional Chinese medicine compositions for dissolving stones and promoting bile secretion;

[0096] Figure 7 PCA score chart of 15 batches of traditional Chinese medicine compositions for dissolving stones and promoting bile secretion;

[0097] Figure 8 The dispersion plot of OPLS-DA for 15 batches of traditional Chinese medicine compositions for dissolving stones and promoting bile secretion was obtained, where G1-classification group 1; G2-classification group 2; G3-classification group;

[0098] Figure 9 OPLS-DAVIP values ​​of 15 batches of traditional Chinese medicine compositions for dissolving stones and promoting bile secretion;

[0099] Figure 10 Scatter plot of OPLS-DA loadings for 15 batches of traditional Chinese medicine compositions for dissolving stones and promoting bile secretion;

[0100] Figure 11 Chromatograms S1-S9 represent the chromatograms of pure water (S1), ethanol (S2), acetonitrile (S3), methanol (S4), 90% methanol (S5), 70% methanol (S6), 50% methanol (S7), 30% methanol (S8), and 10% methanol (S9).

[0101] Figure 12 The UPLC chromatograms S1–S10 of 10 different mobile phase systems were compared: methanol-water, acetonitrile-water, methanol-0.05% phosphoric acid solution, methanol-0.1% phosphoric acid solution, methanol-0.05% formic acid solution, methanol-0.1% formic acid solution, acetonitrile-0.05% phosphoric acid solution, acetonitrile-0.1% phosphoric acid solution, acetonitrile-0.05% formic acid solution, and acetonitrile-0.1% formic acid solution. Detailed Implementation

[0102] Unless otherwise defined, the technical or scientific terms used in the specification and claims of this patent application shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0103] Example 1: A UPLC fingerprint detection method for gallstone-dissolving and choleretic capsules

[0104] 1.1 Instruments and Reagents

[0105] WatersAcquity UPLC H-class ultra-high performance liquid chromatograph (Waters Corporation, USA); TE124S analytical balance (Sartorius GmbH, Germany); KQ-800KDE high-power digital ultrasonic cleaner (Kunshan Ultrasonic Instrument Co., Ltd.); 20μL, 100μL, 1000μL, and 5mL pipettes (Thermo Scientific, USA); Reference ultrapure water system (Merck Millipore, Germany).

[0106] Acetonitrile, methanol, and formic acid were all of chromatographic grade and purchased from Thermo Fisher Scientific, USA. The experimental water was self-prepared ultrapure water. A total of 15 batches of Xiaoshi Lidan Capsules (0.4g / capsule) were provided by Baoding Tianhao Pharmaceutical Co., Ltd., with batch numbers 230403, 230404, 230601, 230602, 230602, 230603, 231001, 231002, 231101, 231102, 231103, 240403, 240404, 240601, 240602, and 240603, and sample numbers S1 to S15. Hesperidin (batch number: 110721-202220), baicalin (batch number: 110715-202223), curcumin (batch number: 110823-202107), narcisin (batch number: 111997-202302), gallic acid (batch number: 110831-202407), rhein (batch number: 110796-202423), emodin (batch number: 110756-202414), and rhein (batch number: 110757-202308) were all purchased from the China National Institutes for Food and Drug Control, and their purity was greater than 98%.

[0107] 2. Methods and Results

[0108] 2.1 Chromatographic conditions

[0109] Column: WatersAcquity UPLC BEH C 18(1.7 μm, 2.1 × 100 mm); Mobile phase: A-acetonitrile, B-0.1% formic acid aqueous solution, gradient elution (0–3 min, 10%–13% A; 3–4 min, 13%–19% A; 4–18 min, 19%–30% A; 18–20 min, 30%–45% A; 20–25 min, 45%–100% A; 25–28 min, 100%–10% A; 28–30 min, 10%–10% A); Flow rate: 0.3 mL / min; Detection wavelength: 288 nm; Column temperature: 30 °C; Injection volume: 2 μL.

[0110] 2.2 Solution Preparation

[0111] 2.2.1 Reference solution

[0112] Accurately weigh appropriate amounts of each reference standard and dissolve in methanol to prepare a mixed solution containing 24.00 ug / ml hesperidin, 29.30 ug / ml baicalin, 22.10 ug / ml curcumin, 22.80 ug / ml naringin, 25.10 ug / ml gallic acid, 23.70 ug / ml rhein, 23.40 ug / ml emodin, and 21.60 ug / ml rhein per 1 mL (visible in chromatogram). Figure 3 ), that is, you get it.

[0113] 2.2.2 Test solution

[0114] Accurately weigh 0.5g of the contents of the choleretic capsules and place them in a stoppered conical flask. Add 10mL of 100% methanol accurately, weigh the contents, and sonicate (800W power, 40kHz frequency) for 30min. Cool the flask, replenish the lost mass with 100% methanol, shake well, filter, and collect the filtrate.

[0115] 2.3 Methodological Examination

[0116] 2.3.1 Precision Test

[0117] The sample of Shilidan capsules (No. 230403) was cancelled. The test solution was prepared according to the method in section "2.2.2". Under the conditions in section "2.1", the sample was injected 6 times consecutively. The relative retention time of each common peak was recorded and the relative peak area was calculated.

[0118] The results showed that the relative retention time RSD of each common peak was 0.002%–0.158%, and the relative peak area RSD was 0.235%–2.154%. The chromatograms were analyzed and processed using the "Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicine" (2012 version). It was found that the similarity between the chromatograms obtained from six consecutive injections and the control chromatograms was ≥0.99, indicating that the instrument precision was good.

[0119] 2.3.2 Stability Test

[0120] Six samples of choleretic capsules (sample number 230403) were cancelled. Following the method described in section "2.2.2", test solutions were prepared in parallel. Under the conditions described in section "2.1", the samples were injected separately, and the relative retention times of each common peak were recorded, and the relative peak areas were calculated. The results showed that the RSD values ​​of the relative retention times of each common peak ranged from 0.002% to 0.189%, and the RSD values ​​of the relative peak areas ranged from 0.453% to 3.014%. These experimental data indicate that the test solutions exhibited good stability within 24 hours.

[0121] 2.3.3 Repeatability Test

[0122] The sample of Lidan capsules (sample number 230403) was cancelled. The test solution was prepared according to the method in section "2.2.2". The sample was injected at 0, 2, 4, 7, 12, and 24 hours under the conditions in section "2.1". The relative retention times of each common peak were recorded, and the relative peak areas were calculated. The results showed that the RSD values ​​of the relative retention times of each common peak were all between 0.002% and 0.165%, and the RSD values ​​of the relative peak areas were all between 0.463% and 3.023%. This indicates that the method has good repeatability.

[0123] 2.4 Establishment of UPLC fingerprint spectrum

[0124] Fifteen batches of choleretic and litholytic powder samples were collected. Test solutions were prepared according to the method described in section "2.2.2". The samples were injected and analyzed under the chromatographic conditions described in section "2.1". Chromatograms of each sample were recorded and imported into the "Traditional Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System" (2012 version) software in CDF format for processing. Using S1 (batch number 230403) as the reference chromatogram, with a time window width of 0.1 min, a multi-point correction method was used to match the chromatograms. A reference chromatogram (R) was generated using the median method. (See the attached chromatogram appendix.) Figure 2 UPLC fingerprints of 15 batches of Xiaoshi Lidan capsules were obtained (see chromatogram appendix). Figure 1 A total of 12 common peaks were identified.

[0125] 2.5 Identification of Common Peaks

[0126] Using a Waters Acquity UPLC H-class ultra-high performance liquid chromatograph, and comparing with the mixed reference standard, 8 of the 12 common peaks were identified. The experimental results are shown in the chromatogram. Figure 4 Peaks 1, 3, 5, 6, 9, 10, 11, and 12 were identified as gallic acid, narcissin, hesperidin, baicalin, rhein, curcumin, emodin, and chrysophanol, respectively.

[0127] 2.6 Similarity Evaluation of Fingerprint Maps

[0128] The fingerprint data of 15 batches of Xiaoshi Lidan Capsules were analyzed using the "Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicine" (2012 version). The similarity between samples was calculated, and the results are shown in Table 1. The RSD values ​​of the retention times of all common peaks were <4.00%, and the similarity between the 15 batches of Xiaoshi Lidan Capsules ranged from 0.932 to 1.000, indicating that the quality similarity of different batches of Xiaoshi Lidan Capsules was high, and the chemical components contained were basically the same. This demonstrates that the UPLC fingerprinting method established in this invention is suitable for the qualitative analysis of Xiaoshi Lidan Capsules.

[0129] 2.7 Research on Chemical Pattern Recognition

[0130] 2.5.1 Cluster Analysis

[0131] Hierarchical clustering analysis (HCA) was performed using the peak areas of 12 common peaks from 15 batches of Xiaoshi Lidan capsules as variables to establish an original data matrix. This matrix was imported into SPSS 26.0 software, and the squared Euclidean distance was used as a measure to evaluate the quality differences between different batches of Xiaoshi Lidan capsules. The results are shown in the chromatogram. Figure 3 The results showed that when the classification distance was 20, the 15 batches of Xiaoshi Lidan Capsules samples could be divided into two categories: S1, S2, S5, S6, S7, S10, S11, S12, S13, and S15 belonged to one category (G1), and S3, S4, S8, and S19 belonged to another category (G2). When the classification distance was 15, the 15 batches of Xiaoshi Lidan Capsules samples could be divided into three categories: S1, S2, S5, S6, S7, S10, S11, S12, S1, and S15 belonged to one category (G1), S3, S4, S8, and S9 belonged to another category (G2), and S14 belonged to another category (G3). This indicates that the samples from different batches in the same year had high similarity and low differences. Different production times may be one of the important factors leading to differences in drug quality. It may also be related to different growth years and harvesting seasons of raw materials, thus leading to differences in drug quality between batches.

[0132] 2.5.2 Principal Component Analysis

[0133] Principal component analysis (PCA), a widely used multivariate statistical analysis method, can reduce the dimensionality of data, transforming multiple correlated indicators into a few representative comprehensive indicators, thereby extracting the main component characteristics. Using the peak areas of 12 common peaks from 15 batches of Xiaoshi Lidan capsules as variables, the original data matrix was standardized using SPSS 26.0 software before PCA analysis. The results of the KMO and Bartlett's sphericity tests in the dimensionality reduction factor analysis are shown in Table 2: the KMO sampling suitability measure was 0.676, suitable for principal component analysis; the Bartlett's sphericity test showed a significance p = 0.000 < 0.001, indicating that this variable provides a reasonable basis for principal component analysis. Furthermore, after principal component analysis and weight calculation, the eigenvalues ​​of the first three principal components were all greater than 1, with a cumulative contribution rate of 84.692% (see Table 3). The scree plot from factor analysis also shows an inflection point at the second principal component eigenvalue, after which the trend gradually flattens out. Based on the above analysis results, the first three components were extracted as principal components.

[0134] Using the peak areas of 12 common peaks from 15 batches of gallstone-dissolving and choleretic capsules as variables, PCA was performed using Origin 2024 software. The results are shown below. Figure 5 The results showed that most sample points were within the 95% confidence interval, indicating that the 15 batches of Xiaoshi Lidan capsules samples generally exhibited stable quality. The sum of the contribution rates of the first three principal components extracted was 88.373%, which to a certain extent can basically reflect the main characteristics of the peak areas of the 12 common peaks. Figure 5 The PCA score plot was used to divide the 15 batches of samples into 3 categories. The results were basically consistent with those of HCA and further verified the classification results of the cluster analysis.

[0135] Table 2. KMO and Bartlett's sphericity tests

[0136]

[0137] Table 3. Common peak characteristic values ​​and contribution rates of 15 batches of Xiaoshi Lidan capsules

[0138]

[0139] 2.5.3 OPLS-DA Analysis

[0140] To better explain the common peaks with significant contributions to the differences among different batches of Xiaoshi Lidan capsules, orthogonal partial least squares discriminant analysis (OPLS-DA) was performed. The peak areas of 12 common peaks from 15 batches of samples were imported into SIMCA 14.10 software. Supervised chemical pattern recognition analysis was conducted on the data from the three groups of samples using the OPLS-DA model, which enhances inter-group separation. The resulting scatter plots, variable importance in projection (VIP) value plots, and loading scatter plots are shown in the chromatograms. Figures 5-7 .Depend on Figure 8 It can be seen that most of the 15 batches of Xiaoshi Lidan capsules fell within the 95% confidence interval, and were mainly divided into 3 groups. The results of OPLS-DA and PCA were basically consistent; combined with chromatographic attachments... Figure 9 Using VIP>1 and error bar range within X>0 as screening criteria, seven chromatographic peaks with relatively large contributions were identified, ranked from largest to smallest as peak 2, peak 6 (baicalin), peak 4, peak 8, peak 7, peak 9 (rhein), and peak 5 (hesperidin). Figure 10 The load scatter plot shows that the origins of separation for these seven chemical components are far apart, indicating that these components have a significant impact on the quality of different batches of Xiaoshi Lidan capsules. They can be used as markers of quality differences in Xiaoshi Lidan capsules. This also indicates that the efficacy of Xiaoshi Lidan capsules is formed by the combined action of multiple active ingredients. It is recommended that the manufacturer of this product pay more attention to the above key markers to better ensure the uniformity of drug quality.

[0141] 2.5.4 Medicinal Herb Similarity Prediction Based on Backpropagation Neural Network

[0142] Principal component analysis of common peaks in the HPLC chromatograms of 15 batches of Xiaoshi Lidan capsules was performed. Twelve common peaks were dimensionality-reduced and projected onto three principal components. The factor scores of these three principal components were used as input factors, and the similarity to the control chromatogram was used as the output factor. A neural network similarity fitting model for Xiaoshi Lidan capsules was established using DPS 7.05 software. The root mean square error (RMSE) and percentage absolute error (AE) were used to evaluate the BP neural network model. The calculation formulas are as follows: In the formula, Yi is the predicted value of the network model, Oi is the measured similarity value, and n is the number of samples. After multiple fittings, the neural network model for the similarity of Xiaoshi Lidan capsules has 1 hidden layer, 3 input layer nodes, 3 hidden layer nodes, a minimum training rate of 0.1, a dynamic parameter of 0.6, a parameter SIGMOID of 0.9, an allowable error of 0.0001, a maximum number of iterations of 1000, a model fitting residual of 0.01203, an RMSE of 0.0190, and an AE of 0.052%–1.212%. Therefore, the fingerprint spectrum similarity evaluation model for Xiaoshi Lidan capsules built using a BP neural network can accurately predict the fingerprint spectrum similarity of different Xiaoshi Lidan capsule samples, thereby classifying medicinal material samples with different quality. The classification results are consistent with the system cluster analysis, principal component analysis, and similarity evaluation results. (See Table 4)

[0143] Table 4. Neural Network Similarity Fitting Model for Xiaoshi Lidan Capsules

[0144]

[0145] 4. Conclusion

[0146] Xiaoshi Lidan Capsules are widely used clinically, but their composition is complex, involving multiple medicinal ingredients and exhibiting variations in origin and production location. Currently, research on the quality standards and content determination of Xiaoshi Lidan Capsules is scarce and uses relatively simplistic methods. However, the overall efficacy of Xiaoshi Lidan Capsules is achieved through the synergistic effect of multiple medicinal components; simply testing a single component cannot comprehensively reflect its quality consistency and overall therapeutic effect. Therefore, it is urgent to improve and enhance the quality control standards for Xiaoshi Lidan Capsules from the perspective of the overall active ingredient group to ensure the stability and safety of its clinical efficacy.

[0147] This experiment established UPLC fingerprints for 15 batches of Xiaoshi Lidan Capsules samples using chemical pattern recognition (CPR). A total of 12 common peaks were identified, and 8 of these were further identified. The sample similarity was >0.943. Compared to existing fingerprint studies of Xiaoshi Lidan Capsules, the method established in this study identified a greater number of common peaks, all of which possess significant pharmacological activity. Furthermore, combined with CPR analysis, the 15 batches of Xiaoshi Lidan Capsules samples were classified into three categories, and seven main markers contributing to quality differences between batches were identified. The UPLC fingerprints established in this study contribute to further improving the quality evaluation system for Xiaoshi Lidan Capsules and can provide a reference for quality control.

Claims

1. A UPLC fingerprint detection method for a traditional Chinese medicine composition for dissolving stones and promoting bile secretion, characterized in that, The detection method includes the following steps: (1) Preparation of the test solution: Weigh the contents of the choleretic capsules, add 100% methanol, weigh the contents, sonicate, cool, replenish the lost mass with 100% methanol, shake well, filter, and take the filtrate to obtain the test solution. (2) Preparation of mixed reference solution: Weigh each reference standard and add methanol to prepare a mixed solution containing 10-30 μg / ml hesperidin, 20-40 μg / ml baicalin, 20-25 μg / ml curcumin, 20-25 μg / ml narcissin, 20-30 μg / ml gallic acid, 20-30 μg / ml rhein, 20-30 μg / ml emodin, and 15-25 μg / ml rhein per 1 mL. (3) Chromatographic conditions: Column: C 18 Mobile phase: A-acetonitrile, B-0.1% formic acid aqueous solution; gradient elution: 0~3 min, 10%~13% A; 3~4 min, 13%~19% A; 4~18 min, 19%~30% A; 18~20 min, 30%~45% A; 20~25 min, 45%~100% A; 25~28 min, 100%~10% A; 28~30 min, 10%~10% A; volumetric flow rate: 0.1~0.5 mL / min; detection wavelength: 288 nm; column temperature: 25~35℃. (4) Fingerprint mapping Take multiple batches of the test solution from step (1), inject and determine them according to the chromatographic conditions in step (3), record the chromatograms of each sample, and import them into the "Traditional Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System" software for processing. Generate the reference chromatogram R by the median method to obtain the UPLC fingerprint of the traditional Chinese medicine composition for dissolving stones and promoting bile secretion.

2. The UPLC detection method as described in claim 1, characterized in that, The ultrasonic power in step (1) of the detection method is 700~900W, the ultrasonic frequency is 30~50kHz, and the ultrasonic time is 20~40min.

3. The UPLC detection method as described in claim 2, characterized in that, The ultrasonic power in step (1) of the detection method is 800W, the ultrasonic frequency is 40kHz, and the ultrasonic time is 30min.

4. The UPLC detection method as described in claim 1, characterized in that, The concentrations of hesperidin, baicalin, curcumin, naringin, gallic acid, rhein, and chrysophanol in the mixed reference solution in step (2) of the detection method are as follows: 24.00 μg / ml; 29.30 μg / ml; 22.10 μg / ml; 22.80 μg / ml; 25.10 μg / ml; 23.70 μg / ml; 23.40 μg / ml; and 21.60 μg / ml.

5. The UPLC detection method as described in claim 1, characterized in that, The detection method step (3) C 18 The model is Waters Acquity UPLC BEH, and the column specifications are 1.7μm and 2.1×100mm.

6. The UPLC detection method as described in claim 1, characterized in that, The detection method step (3) chromatographic conditions are as follows: flow rate: 0.3 mL / min; detection wavelength: 288 nm; column temperature: 30 °C.

7. The UPLC detection method as described in claim 1, characterized in that, In step (4), the quantity of multiple batches is ≥10 batches.

8. The UPLC detection method as described in claim 1, characterized in that, The detection method is used in the quality detection, intermediate content determination and identification of effective components of traditional Chinese medicine compositions for dissolving stones and promoting bile secretion.

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