Evaluation of the batch consistency of herbal chromatographic fingerprints using a comprehensive and easy-to-compute method inspired by h index

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Although many indices have been proposed to evaluate the similarity of herbal fingerprints, most of them are designated to evaluate pair similarity and the methods to measure batch consistency have been rarely discussed. Inspired by the popular h index, which has achieved great success in bibliometrics, this paper suggested a novel h multiple similarity index (HMSI) to evaluate the batch consistency of herbal fingerprints. HMSI was defined as: based on the pairwise similarity (ranged from [0, 1]) of all objects in the batch, if M% of all the pairwise similarity values is no less than M%, then the value of HMSI is M%. For applications, HMSI was used to evaluate the batch consistency of different herbal fingerprints, and the results were compared with those obtained by average similarity and median similarity. The results demonstrated that compared with average similarity and median similarity, HMSI is more reasonable to evaluate batch consistency of fingerprints and the herbal quality control system behind them. Similar to the original h index, HMSI not only includes the similarity intensity of objects in a batch, but also considered the quantity of objects with high similarity. HMSI was a simple, robust, easy-to-compute and yet comprehensive index to evaluate batch consistency of herbal fingerprints and herbal quality control system. Drug Delivery Herbal chromatographic fingerprints Batch consistency h multiple similarity index (HMSI) Quality control Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Herbal fingerprint refers to the chemical, biological or other characteristics of herbs, which can be obtained by certain analytical techniques and methods after proper treatment of medicinal materials, decoction pieces, extracts, or traditional Chinese medicine (TCM) prescriptions [ 1 – 3 ]. Being able to analyze or characterize the multiple chemical components in herbs, herbal fingerprint technology has been widely used to compare different batches of herbs [ 4 ], identify their authenticity [ 5 ], distinguish herbal species [ 6 ], and check the quality consistency and stability [ 7 , 8 ]. Due to its high sensitivity, good reproducibility, and high resolution, chromatographic fingerprints [ 9 – 11 ], including thin layer chromatography [ 12 , 13 ], liquid chromatography [ 14 , 15 ], gas chromatography [ 16 , 17 ] and high performance capillary electrophoresis [ 18 , 19 ], has become the most widely used fingerprint technology of TCM herbs. Evaluation of chromatographic fingerprints usually involves measurement of similarity among a batch or comparing some fingerprints with the reference [ 20 , 21 ]. The commonly used similarity calculation methods include correlation coefficient method [ 22 , 23 ], congruence coefficient [ 22 ], Euclidean distance [ 24 ], linear correlation coefficient [ 22 ], Mahalanobis distance method [ 25 ], and so on. However, most of the above methods focus on the evaluation of pairwise similarity. In practical applications, it is often necessary to evaluate the internal similarity of multiple fingerprints in a batch or to study the consistency of multiple fingerprints in a batch with that of reference fingerprints. Therefore, in order to better evaluate the consistency of batch quality, it is necessary to develop multiple similarity index based on herbal fingerprints. The h-index was originally proposed to evaluate individuals’ scientific output in 2005 [ 26 , 27 ], which has achieved great success in bibliometrics [ 28 – 30 ]. The advantage of h-index is that it combines the numbers of citations and high-citation papers in a single simple index. For quality evaluation of TCM herbs, we are not only concerned about the intensity of similarity, but also interested in the number of objects with high similarity. Inspired by the idea of h-index, in this work, a new h-multiple similarity index (HMSI) evaluation method was proposed to evaluate the consistency of herbal batch quality based on chromatographic fingerprints. The usefulness and effectiveness of HMSI will be demonstrated by evaluation of two real herbal fingerprints. 2. Experimental and methods 2.1. Chemicals, materials, and instruments 2.1.1 Shiduqing Capsules Shiduqing is a traditional Chinese herbal formula consisting of the herbs Rehmannia glutinosa, Angelica sinensis (Oliv.) Diels., Salvia miltiorrhiza Bunge., Sophora flavescens Aiton., Periostracum Cicadae , Radix Scutellariae, Cortex Dictamni , Rhizoma Smilacis Glabrae and Glycyrrhiza uralensis Fisch.. It removes the toxins of dampness and heat from the body and relieves skin, liver and gallbladder related problems brought about by dampness and heat. Methanol, acetonitrile and phosphoric acid were of chromatographic grade and purchased from Hubei Foden Science and Technology Co. (Wuhan, China). A total of 11 batches of Shiduqing capsules were purchased from Xuzhou Enhua Unified Pharmaceutical Chain Sales Co. (Xuzhou, China), including 10 regular batches with the following batch numbers: 2312107, 2312108, 2312110, 2312111, 2312116, 2312117, 2312118, 2312119, 2312120 and 2312121; as well as 1 irregular batch (2312109) produced in the same production cycle. Reference substance baicalin (Batch number: Z28S11X125952, Purity > 98%) was purchased from Shanghai Yuanye Biotechnology Co. (Shanghai, China). The high-performance liquid chromatography (HPLC) fingerprints were measured using the Agilent 1260 Infinity HPLC SYSTEM (Agilent Corporation, Santa Clara, California, USA) that included a quadruple high-pressure gradient pump, an in-line degasser, an autosampler, a column oven, a diode array detector (DAD), and the Agilent OpenLAB CDS 2.0 data management system. 2.1.2. Ligusticum chuanxiong Ligusticum chuanxiong Hort. (LC), under the name of “Chuan Xiong”, is a well-known traditional Chinese medicinal (TCM). LC has the effect of promoting blood circulation, dispelling wind and relieving pain, and is often used for headache, rheumatic pain, menstrual disorders and other symptoms. Methanol, acetonitrile and phosphoric acid were of chromatographic grade and purchased from Hubei Foden Science and Technology Co. (Wuhan, China). The reference substance was ferulic acid (Batch number: 110773–201915, Purity: 99.4%), which was procured from the China Academy of Food and Drug Control (Beijing, China). A total of 48 samples of LC were collected from the harvest of new plantings from 2023 to 2024 in Pengzhou City, Sichuan Province, China. These species were identified as the dried rhizomes of Chuanxiong , family Apiaceae , by Prof Lu Daowang of the Department of Pharmacy, Tongren College. The high-performance liquid chromatography (HPLC) fingerprints were measured using the Agilent 1260 Infinity HPLC SYSTEM (Agilent Corporation, Santa Clara, California, USA) that included a quadruple high-pressure gradient pump, an in-line degasser, an autosampler, a column oven, a diode array detector (DAD), and the Agilent OpenLAB CDS 2.0 data management system. 2.1.3. Gastrodia elata Gastrodia elata BI. (GE), under the name of “Tian Ma”, has been an important and widely used traditional Chinese medicine (TCM). GE is mainly used for calming the liver and restraining wind, dispelling wind and suppressing spasm, relaxing tendons and activating collaterals, and is often used for treating headache, vertigo, after-effects of stroke, epilepsy and other symptoms. A total of 80 GE samples were collected, all purchased from Dejiang County Kangqi Medicinal Plant Development Co.(Tongren, China), and identified as the dried rhizomes of Gastrodia elata of the genus Gastrodia of the family Orchidaceae by Professor Lu Daowang of the Department of Pharmacy, Tongren College. The Near-Infrared(NIR) Spectroscopy fingerprints were measured using the Antaris II Fourier transform-NIR spectrometer(Thermo Electron Co., Waltham, Massachusetts, USA) . 2.2. Chromatographic analysis 2.2.1. Shiduqing capsules Preparation of the test solution: take 2.0g of the content of Shiduqing soft capsule was put into a 150 mL round-bottomed flask, add 80.0 mL of methanol and reflux for 10 min at 66 ℃ in a water bath with heat to obtain the test solution. Chromatographic conditions: Agilent eclipse XDB-C 18 column (4.6 mm × 250 mm, 5 µ m) (Agilent Corporation, Santa Clara, California, USA); mobile phase: 0.1% (w/w) phosphoric acid aqueous solution (A) and acetonitrile (B); gradient elution: (0ཞ20 min, 5%ཞ15%B; 20ཞ50 min, 15%ཞ21% B; 50ཞ60 min, 21%ཞ24%B; 60ཞ65 min, 24%ཞ25% B; 65ཞ75 min, 25%ཞ30% B; 75ཞ80 min, 30%ཞ35% B; 80ཞ90 min, 35%ཞ40% B; 90ཞ120 min, 40%ཞ50% B); flow rate: 1.0 mL min − 1 ; column temperature: 30 ℃; detection wavelength: 220 nm; and injection volume: 20 µ L. 2.2.2. Ligusticum chuanxiong Preparation of the test solution: The Chuanxiong samples was ground into powder by ball milling before filtering through a 50-month sieve. 0.25 g of Rhizoma Chuanxiong powder was weighed precisely and placed in a stoppered conical flask, 25 mL of 50% methanol water was added, stoppered tightly, weighed and extracted by ultrasonication (500 W, 40 kHz) for 30 min, then centrifuged for 10 min at 14 000 r·min − 1 , and the supernatant was obtained as the test solution. Chromatographic conditions: Agilent eclipse XDB-C 18 column (4.6 mm × 250 mm, 5 µ m) (Agilent Corporation, Santa Clara, California, USA); mobile phase: 0.1% (w/w) phosphoric acid aqueous solution (A) and acetonitrile (B); gradient elution: (0 ~ 5 min, 10%B; 5 ~ 12 min, 10%~15%B; 12 ~ 17 min, 15%~ 20%B; 17 ~ 33 min, 20% ~41%B; 33 ~ 43 min, 41%~80%B; 43 ~ 48 min, 80%B; 48 ~ 50 min, 80%~10%B; 50 ~ 55 min, 10%B); flow rate: 1.0 mL min − 1 ; column temperature: 30 ℃; detection wavelength: 280 nm; and injection volume: 10 µ L. 2.2.3. Gastrodia elata Preparation of the test solution: All samples of the GE were dried at 50 ℃ in a hot air oven until constant weight and then stored. Before NIR analysis, all samples were dried well in sunlight before being ground into granules using a pulveriser and filtered through a 200 mesh sieve. NIR Spectral Conditions: The Antaris II FT-NIR spectrometer was set to reflectance mode, the spectra were measured using an InGaAs detector, the measurement background was set to internal gold background and calibrated every 1 hour. Each sample was measured in triplicate to obtain an average spectrum. The number of scans for each measurement was 32. The working range of the spectrometer was 4000-10 000 cm − 1 , the resolution of the instrument was 8 cm − 1 , and the scanning interval was 3.857 cm − 1 . During the analysis, the temperature was maintained at about 25°C and the humidity was kept at a stable level. The NIR analyses of all samples were performed in random order. 2.3. Definition and calculation of h-multiple similarity index (HMSI) The definition of h-multiple similarity index (HMSI) proposed in this study is as follows: Based on the pairwise similarity (between [0,1]) of all samples in the batch, if M% of all the pairwise similarity values is not less than M%, then the HMSI value of the batch is M%. When calculating HMSI, there are usually two cases: (1) if the aim is to evaluate the consistency of fingerprints in a batch, one should first calculate the pairwise similarity between all samples in the batch, and then calculate HMSI according to the above definition; (2) If one wants to evaluate the similarity between the samples in a batch and the reference samples, it is necessary to calculate the similarity between all samples in the batch and the reference samples, and then calculate the HMSI according to the above definition. In the two examples of this study, the calculation of HMSI belongs to the first case. In this study, Pearson’s correlation coefficient method was used to evaluate pairwise similarity and HMSI. However, no matter what method is used to calculate pairwise similarity, as long as the value of pairwise similarity is between [0, 1], one can calculate HMSI according to the above definition. In this study, MATLAB 2013b (Mathworks, Sherborn, MA) was used to calculate pairwise similarity and HMSI, and the corresponding MATLAB code could be request from the corresponding author of this study. 3. Results and discussions 3.1. Data preparation of chromatographic fingerprints For the HPLC data of LC and Shiduqing capsules, the original data were imported into Agilent OpenLAB CDS 2.0 (Agilent Corporation, Santa Clara, California, USA) for data extraction, chromatographic peak identification, and noise elimination. For LC data, as shown in Fig. 1 , the chromatograms were calibrated with 13 common peaks after Mank peak matching, and the relative retention time and relative peak area of each peak in the chromatograms were calculated using ferulic acid (peak 4) as the reference peak[ 31 ], and used for further data analysis. For Shiduqing capsules data, as shown in Fig. 2 , the chromatograms were calibrated with 22 common peaks after Mank peak matching, and the relative retention time and relative peak area of each peak in the chromatograms were calculated using baicalin (peak 11) as the reference peak[ 32 ], and used for further data analysis. For GE data, as shown in Fig. 3 , In order to eliminate possible scattering effects due to powder inhomogeneity, and also considering the stability of different pre-processing methods, the NIR spectra in this study were pre-processed using Standard Normal Variate (SNV)[ 33 ]. The obtained SNV-NIR spectra were used for further data analysis. [Figures 1 , 2 and 3 ] 3.2. Computation of HMSI, its geometrical meanings, and comparisons To demonstrate the usefulness of HMSI, 3 indices, the average correlation coefficient, median correlation coefficient, and HMSI were used to evaluate the batch consistency of the chromatographic fingerprints of LC and Shiduqing capsules, as well as the NIR fingerprints of GE. These specific results are displayed in Table 1 . Table 1 Batch similarity of HPLC fingerprints by different indices. Data (Batch number) Average r Median r HMSI Ligusticum chuanxiong (48) 0.883 0.892 0.826 Shiduqing (11) 0.971 0.987 0.895 Gastrodia elata (80) 0.992 0.992 0.992 [Table 1 ] [Figure 4 ] For the LC data, the peak areas of 13 compounds in 48 samples were recorded for pairwise similarity and consistency analysis. The pairwise correlation coefficients between the 48 samples were in the range of 0.647–0.991. Figure 4 reveals the calculation method and geometric meanings of HMSI. Firstly, all pairwise similarity1 TT.1 in a batch and their percentiles were computed. Secondly, take the percents/100 as the horizontal ordinate and the corresponding percentiles as the longitudinal coordinates to make a scatter plot of all percentiles. The scatter plot can show the distribution of all pairwise similarity. According to the definition of HMSI, HMSI is the side length of the largest square under the curve formed by the scattered points of percentiles. For the LC data (Fig. 4 ), as the side length of the square is 0.826, the value of HMSI is also 0.826. It is obvious that HMSI takes into account both the level of pairwise similarity (by percentiles) and the number of samples covered by high pair similarity (by percents/100), and endows them with equal importance. As shown in Fig. 4 , only the bulk of all pairwise similarity has a high value can HMSI has a high value. The HMSI of LC is 0.826, which means that 82.6% of the pairwise similarity is larger than or equal to 82.6% (0.826). The median of pairwise similarity of this dataset is 0.892, indicating that 50% of pairwise similarity is greater than or equal to 0.892, and 50% of pairwise similarity is less than or equal to 0.892, but little else. The average value of pairwise similarity is 0.883. Because high similarity and low similarity can offset each other, actually, the average value provides the least information for all pairwise similarity. Compared with the median and average, HMSI attaches importance to the number of samples with high similarity; median tends to evaluate the overall pairwise similarity robustly; while mean is not robust and provides the least information. For Shiduqing capsules data, the peak areas of 22 compounds in 10 regular batches and 1 unqualified batch of products were recorded for pairwise similarity and consistency analysis (Table 2 ). The pairwise correlation coefficients between the 10 regular batchewhoss were in the range of 0.985–0.993, indicating the 10 regular batches had high pairwise similarity. On the other hand, the pairwise correlation coefficients between the 11 batches (including the irregular batch) ranged from 0.886 to 0.993, indicating that the pairwise similarity between the irregular batch and the other 10 regular batches was relatively low and the lowest value was 0.886. Table 2 Pairwise correlation coefficients of 11 batches of Shiduqing capsules. Batches 1 2 3 4 5 6 7 8 9 10 11 1 1.000 2 0.988 1.000 3 0.986 0.988 1.000 4 0.991 0.986 0.989 1.000 5 0.986 0.992 0.987 0.987 1.000 6 0.985 0.992 0.989 0.988 0.985 1.000 7 0.986 0.988 0.990 0.989 0.992 0.986 1.000 8 0.993 0.985 0.986 0.985 0.990 0.988 0.989 1.000 9 0.988 0.987 0.985 0.987 0.988 0.992 0.986 0.993 1.000 10 0.987 0.988 0.987 0.991 0.989 0.989 0.988 0.990 0.988 1.000 11 0.897 0.894 0.896 0.891 0.891 0.896 0.887 0.888 0.898 0.894 1.000 [Table 2 ] In order to further evaluate the whole consistency of the 11 batches, the HMSI (Fig. 5 ), the median correlation coefficient and the average correlation coefficient were calculated, which were 0.987, and 0.971, respectively. Although the similarity between the irregular batch and the 10 regular batches was low, the average correlation coefficient was still as high as 0.971 due to the counting of both high and low pair similarity values, indicating that the average correlation coefficient is insensitive to a small number of irregular samples and it cannot evaluate the batch consistency very well. Because value of median reflects the level of the bulk of data, the median correlation coefficient in this study was as high as 0.987, which was also insensitive to the presence of the irregular batch. In comparison, HMSI had a much lower value (0.895). As shown in Fig. 5 , the inclusion of an irregular batch did cause a considerable decrease in some pairwise similarity. Actually, HMSI not only considered the pairwise similarity, but also examined the number of samples covered by high pair similarity, which is more reasonable than the other two indicators. [Figure 5 ] For the GE powder data, based on the SNV-NIR spectral data (Fig. 3 ), the correlation coefficients between two-by-two samples were calculated, which ranged from 0.981 to 0.999, and the corresponding values of the HMSI, the median correlation coefficient, and the average correlation coefficient were all 0.992. Since the two-by-two similarity between samples was relatively high in all the batches (0.981–0.999), the values of the three indexes all had a value of 0.992, indicating that the HMSI is also a reasonable index for evaluating batch consistency in general. 4. Conclusions In this study, a new h multiple similarity index (HMSI) was proposed to evaluate the consistency of TCM herbal fingerprints. Based on the HPLC fingerprints of Shiduqing soft capsules and LC, as well as the NIR fingerprints of GE, the consistency of fingerprints in a batch was evaluated by HMSI, average pairwise similarity and median pairwise similarity. The results showed that HMSI was suitable for consistency evaluation of chromatographic fingerprints, and it was more reasonable than average similarity and median similarity in practical applications. HMSI not only includes the similarity intensity of samples, but also considers the number of samples with high similarity. In conclusion, HMSI is a simple, robust and comprehensive index for consistency evaluation of herbal fingerprints. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Lu Xu: Conceptualization, Methodology, Investigation, Writing original draft, Resources, Supervision. Hai-Yan Fu: Writing original draft, Resources, Supervision. Rui Liu: Methodology, Writing original draft. Qin Yang: Conceptualization, Methodology, Investigation, Writing original draft. 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Anal Methods 9:1897–1904. 10.1039/c7ay00153c Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Liu","suffix":""},{"id":379066882,"identity":"e9e70d9a-8ccd-4e2a-b3e6-8b2f39570701","order_by":2,"name":"Rongling Gu","email":"","orcid":"","institution":"Zhejiang Hisun Pharmaceutical Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Rongling","middleName":"","lastName":"Gu","suffix":""},{"id":379066883,"identity":"99ccdf57-6d87-429b-8e02-0115e9483b0b","order_by":3,"name":"Hai-Yan Fu","email":"","orcid":"","institution":"South-Central University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Hai-Yan","middleName":"","lastName":"Fu","suffix":""},{"id":379066884,"identity":"ba35da40-4183-4f33-a135-21f23f890a53","order_by":4,"name":"Qin Yang","email":"","orcid":"","institution":"Yangtze University","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Yang","suffix":""},{"id":379066885,"identity":"26dcb5c9-84ff-48fa-92cb-5c1594f04c1a","order_by":5,"name":"Lu Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBACPgYGNhAtx8befoA4LWxQLcZ8PGcSSNOSOE/CwYBILRLJzx58zLFJb5NgSGD4UbGNCC08x8wNZ25Ly22TbjzA2HPmNhFa2HvYpHm3Hc5tkzmQwMzYRowWZh426b/bDqezSSQYEKkFZAvjtsMJJGjhOWYm2bstzbANGMgHifILPzDEJH5us5GXb28/+OBHBRFaUMABEtWPglEwCkbBKMAFANpiNEHRy2gZAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4742-5623","institution":"Tongren University","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-11-17 13:36:20","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5470098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5470098/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69325778,"identity":"25ee502c-90c9-4a57-8869-6315ce72ac0d","added_by":"auto","created_at":"2024-11-19 07:55:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83980,"visible":true,"origin":"","legend":"\u003cp\u003eHPLC fingerprint of Ligusticum chuanxiong\u003c/p\u003e","description":"","filename":"LC.png","url":"https://assets-eu.researchsquare.com/files/rs-5470098/v1/79d260eb337ce95e1d85e15a.png"},{"id":69324623,"identity":"afd461e2-9fcb-4df8-9544-291881b083b6","added_by":"auto","created_at":"2024-11-19 07:47:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44784,"visible":true,"origin":"","legend":"\u003cp\u003eHPLC fingerprint of Shiduqing capsule with 22 common peaks\u003c/p\u003e","description":"","filename":"SDQ.png","url":"https://assets-eu.researchsquare.com/files/rs-5470098/v1/816367fd9819198075c0d246.png"},{"id":69324625,"identity":"9a12a86d-e187-4985-bd3d-adb256bf1622","added_by":"auto","created_at":"2024-11-19 07:47:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132189,"visible":true,"origin":"","legend":"\u003cp\u003eSNV-NIR spectra of 80 Gastrodia elata samples\u003c/p\u003e","description":"","filename":"GE.png","url":"https://assets-eu.researchsquare.com/files/rs-5470098/v1/e6af5c8a86e94b88f47e214c.png"},{"id":69326140,"identity":"8f313ded-a288-4f30-a63b-ac32a64e0084","added_by":"auto","created_at":"2024-11-19 08:03:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73915,"visible":true,"origin":"","legend":"\u003cp\u003eHMSI of HPLC fingerprints of 48 Ligusticum chuanxiong samples\u003c/p\u003e","description":"","filename":"HMSI.LC.png","url":"https://assets-eu.researchsquare.com/files/rs-5470098/v1/119d689e11cc6508fc3566c4.png"},{"id":69326139,"identity":"2ec4b47e-4216-44f8-8f21-5bfb21a737e0","added_by":"auto","created_at":"2024-11-19 08:03:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61378,"visible":true,"origin":"","legend":"\u003cp\u003eHMSI of HPLC fingerprints of 11 samples of Shiduqing capsules\u003c/p\u003e","description":"","filename":"HMSI.SDQ.png","url":"https://assets-eu.researchsquare.com/files/rs-5470098/v1/136785a4fc2cba3baf87fc95.png"},{"id":69326141,"identity":"efb961b7-a892-4d5d-abf1-8e2ceae887a5","added_by":"auto","created_at":"2024-11-19 08:03:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1043154,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5470098/v1/52d6c231-7ef6-43ab-b051-a0e48b55a12b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEvaluation of the batch consistency of herbal chromatographic fingerprints using a comprehensive and easy-to-compute method inspired by h index\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHerbal fingerprint refers to the chemical, biological or other characteristics of herbs, which can be obtained by certain analytical techniques and methods after proper treatment of medicinal materials, decoction pieces, extracts, or traditional Chinese medicine (TCM) prescriptions [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Being able to analyze or characterize the multiple chemical components in herbs, herbal fingerprint technology has been widely used to compare different batches of herbs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], identify their authenticity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], distinguish herbal species [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and check the quality consistency and stability [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Due to its high sensitivity, good reproducibility, and high resolution, chromatographic fingerprints [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], including thin layer chromatography [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], liquid chromatography [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], gas chromatography [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and high performance capillary electrophoresis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], has become the most widely used fingerprint technology of TCM herbs.\u003c/p\u003e \u003cp\u003eEvaluation of chromatographic fingerprints usually involves measurement of similarity among a batch or comparing some fingerprints with the reference [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The commonly used similarity calculation methods include correlation coefficient method [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], congruence coefficient [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Euclidean distance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], linear correlation coefficient [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Mahalanobis distance method [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and so on. However, most of the above methods focus on the evaluation of pairwise similarity. In practical applications, it is often necessary to evaluate the internal similarity of multiple fingerprints in a batch or to study the consistency of multiple fingerprints in a batch with that of reference fingerprints. Therefore, in order to better evaluate the consistency of batch quality, it is necessary to develop multiple similarity index based on herbal fingerprints.\u003c/p\u003e \u003cp\u003eThe h-index was originally proposed to evaluate individuals\u0026rsquo; scientific output in 2005 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which has achieved great success in bibliometrics [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The advantage of h-index is that it combines the numbers of citations and high-citation papers in a single simple index. For quality evaluation of TCM herbs, we are not only concerned about the intensity of similarity, but also interested in the number of objects with high similarity. Inspired by the idea of h-index, in this work, a new h-multiple similarity index (HMSI) evaluation method was proposed to evaluate the consistency of herbal batch quality based on chromatographic fingerprints. The usefulness and effectiveness of HMSI will be demonstrated by evaluation of two real herbal fingerprints.\u003c/p\u003e"},{"header":"2. Experimental and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Chemicals, materials, and instruments\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Shiduqing Capsules\u003c/h2\u003e \u003cp\u003eShiduqing is a traditional Chinese herbal formula consisting of the herbs \u003cem\u003eRehmannia glutinosa, Angelica sinensis\u003c/em\u003e (Oliv.) Diels., \u003cem\u003eSalvia miltiorrhiza\u003c/em\u003e Bunge., \u003cem\u003eSophora flavescens\u003c/em\u003e Aiton., \u003cem\u003ePeriostracum Cicadae\u003c/em\u003e, \u003cem\u003eRadix Scutellariae, Cortex Dictamni\u003c/em\u003e, \u003cem\u003eRhizoma Smilacis Glabrae and Glycyrrhiza uralensis\u003c/em\u003e Fisch.. It removes the toxins of dampness and heat from the body and relieves skin, liver and gallbladder related problems brought about by dampness and heat.\u003c/p\u003e \u003cp\u003eMethanol, acetonitrile and phosphoric acid were of chromatographic grade and purchased from Hubei Foden Science and Technology Co. (Wuhan, China). A total of 11 batches of Shiduqing capsules were purchased from Xuzhou Enhua Unified Pharmaceutical Chain Sales Co. (Xuzhou, China), including 10 regular batches with the following batch numbers: 2312107, 2312108, 2312110, 2312111, 2312116, 2312117, 2312118, 2312119, 2312120 and 2312121; as well as 1 irregular batch (2312109) produced in the same production cycle. Reference substance baicalin (Batch number: Z28S11X125952, Purity\u0026thinsp;\u0026gt;\u0026thinsp;98%) was purchased from Shanghai Yuanye Biotechnology Co. (Shanghai, China).\u003c/p\u003e \u003cp\u003eThe high-performance liquid chromatography (HPLC) fingerprints were measured using the Agilent 1260 Infinity HPLC SYSTEM (Agilent Corporation, Santa Clara, California, USA) that included a quadruple high-pressure gradient pump, an in-line degasser, an autosampler, a column oven, a diode array detector (DAD), and the Agilent OpenLAB CDS 2.0 data management system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Ligusticum chuanxiong\u003c/h2\u003e \u003cp\u003e \u003cem\u003eLigusticum chuanxiong\u003c/em\u003e Hort. (LC), under the name of \u0026ldquo;Chuan Xiong\u0026rdquo;, is a well-known traditional Chinese medicinal (TCM). LC has the effect of promoting blood circulation, dispelling wind and relieving pain, and is often used for headache, rheumatic pain, menstrual disorders and other symptoms.\u003c/p\u003e \u003cp\u003eMethanol, acetonitrile and phosphoric acid were of chromatographic grade and purchased from Hubei Foden Science and Technology Co. (Wuhan, China). The reference substance was ferulic acid (Batch number: 110773\u0026ndash;201915, Purity: 99.4%), which was procured from the China Academy of Food and Drug Control (Beijing, China). A total of 48 samples of LC were collected from the harvest of new plantings from 2023 to 2024 in Pengzhou City, Sichuan Province, China. These species were identified as the dried rhizomes of \u003cem\u003eChuanxiong\u003c/em\u003e, family \u003cem\u003eApiaceae\u003c/em\u003e, by Prof Lu Daowang of the Department of Pharmacy, Tongren College.\u003c/p\u003e \u003cp\u003eThe high-performance liquid chromatography (HPLC) fingerprints were measured using the Agilent 1260 Infinity HPLC SYSTEM (Agilent Corporation, Santa Clara, California, USA) that included a quadruple high-pressure gradient pump, an in-line degasser, an autosampler, a column oven, a diode array detector (DAD), and the Agilent OpenLAB CDS 2.0 data management system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Gastrodia elata\u003c/h2\u003e \u003cp\u003e \u003cem\u003eGastrodia elata\u003c/em\u003e BI. (GE), under the name of \u0026ldquo;Tian Ma\u0026rdquo;, has been an important and widely used traditional Chinese medicine (TCM). GE is mainly used for calming the liver and restraining wind, dispelling wind and suppressing spasm, relaxing tendons and activating collaterals, and is often used for treating headache, vertigo, after-effects of stroke, epilepsy and other symptoms.\u003c/p\u003e \u003cp\u003eA total of 80 GE samples were collected, all purchased from Dejiang County Kangqi Medicinal Plant Development Co.(Tongren, China), and identified as the dried rhizomes of \u003cem\u003eGastrodia elata\u003c/em\u003e of the genus \u003cem\u003eGastrodia\u003c/em\u003e of the family \u003cem\u003eOrchidaceae\u003c/em\u003e by Professor Lu Daowang of the Department of Pharmacy, Tongren College.\u003c/p\u003e \u003cp\u003eThe Near-Infrared(NIR) Spectroscopy fingerprints were measured using the Antaris II Fourier transform-NIR spectrometer(Thermo Electron Co., Waltham, Massachusetts, USA) .\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Chromatographic analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e2.2.1.\u003c/em\u003e Shiduqing \u003cem\u003ecapsules\u003c/em\u003e\u003c/h2\u003e \u003cp\u003ePreparation of the test solution: take 2.0g of the content of Shiduqing soft capsule was put into a 150 mL round-bottomed flask, add 80.0 mL of methanol and reflux for 10 min at 66 ℃ in a water bath with heat to obtain the test solution.\u003c/p\u003e \u003cp\u003eChromatographic conditions: Agilent eclipse XDB-C\u003csub\u003e18\u003c/sub\u003e column (4.6 mm \u0026times; 250 mm, 5 \u0026micro; m) (Agilent Corporation, Santa Clara, California, USA); mobile phase: 0.1% (w/w) phosphoric acid aqueous solution (A) and acetonitrile (B); gradient elution: (0ཞ20 min, 5%ཞ15%B; 20ཞ50 min, 15%ཞ21% B; 50ཞ60 min, 21%ཞ24%B; 60ཞ65 min, 24%ཞ25% B; 65ཞ75 min, 25%ཞ30% B; 75ཞ80 min, 30%ཞ35% B; 80ཞ90 min, 35%ཞ40% B; 90ཞ120 min, 40%ཞ50% B); flow rate: 1.0 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; column temperature: 30 ℃; detection wavelength: 220 nm; and injection volume: 20 \u0026micro; L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Ligusticum chuanxiong\u003c/h2\u003e \u003cp\u003ePreparation of the test solution: The Chuanxiong samples was ground into powder by ball milling before filtering through a 50-month sieve. 0.25 g of Rhizoma Chuanxiong powder was weighed precisely and placed in a stoppered conical flask, 25 mL of 50% methanol water was added, stoppered tightly, weighed and extracted by ultrasonication (500 W, 40 kHz) for 30 min, then centrifuged for 10 min at 14 000 r\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the supernatant was obtained as the test solution.\u003c/p\u003e \u003cp\u003eChromatographic conditions: Agilent eclipse XDB-C\u003csub\u003e18\u003c/sub\u003e column (4.6 mm \u0026times; 250 mm, 5 \u0026micro; m) (Agilent Corporation, Santa Clara, California, USA); mobile phase: 0.1% (w/w) phosphoric acid aqueous solution (A) and acetonitrile (B); gradient elution: (0\u0026thinsp;~\u0026thinsp;5 min, 10%B; 5\u0026thinsp;~\u0026thinsp;12 min, 10%~15%B; 12\u0026thinsp;~\u0026thinsp;17 min, 15%~ 20%B; 17\u0026thinsp;~\u0026thinsp;33 min, 20% ~41%B; 33\u0026thinsp;~\u0026thinsp;43 min, 41%~80%B; 43\u0026thinsp;~\u0026thinsp;48 min, 80%B; 48\u0026thinsp;~\u0026thinsp;50 min, 80%~10%B; 50\u0026thinsp;~\u0026thinsp;55 min, 10%B); flow rate: 1.0 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; column temperature: 30 ℃; detection wavelength: 280 nm; and injection volume: 10 \u0026micro; L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Gastrodia elata\u003c/h2\u003e \u003cp\u003ePreparation of the test solution: All samples of the GE were dried at 50 ℃ in a hot air oven until constant weight and then stored. Before NIR analysis, all samples were dried well in sunlight before being ground into granules using a pulveriser and filtered through a 200 mesh sieve.\u003c/p\u003e \u003cp\u003eNIR Spectral Conditions: The Antaris II FT-NIR spectrometer was set to reflectance mode, the spectra were measured using an InGaAs detector, the measurement background was set to internal gold background and calibrated every 1 hour. Each sample was measured in triplicate to obtain an average spectrum. The number of scans for each measurement was 32. The working range of the spectrometer was 4000-10 000 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the resolution of the instrument was 8 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the scanning interval was 3.857 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. During the analysis, the temperature was maintained at about 25\u0026deg;C and the humidity was kept at a stable level. The NIR analyses of all samples were performed in random order.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Definition and calculation of h-multiple similarity index (HMSI)\u003c/h2\u003e \u003cp\u003eThe definition of h-multiple similarity index (HMSI) proposed in this study is as follows:\u003c/p\u003e \u003cp\u003eBased on the pairwise similarity (between [0,1]) of all samples in the batch, if M% of all the pairwise similarity values is not less than M%, then the HMSI value of the batch is M%.\u003c/p\u003e \u003cp\u003eWhen calculating HMSI, there are usually two cases: (1) if the aim is to evaluate the consistency of fingerprints in a batch, one should first calculate the pairwise similarity between all samples in the batch, and then calculate HMSI according to the above definition; (2) If one wants to evaluate the similarity between the samples in a batch and the reference samples, it is necessary to calculate the similarity between all samples in the batch and the reference samples, and then calculate the HMSI according to the above definition. In the two examples of this study, the calculation of HMSI belongs to the first case.\u003c/p\u003e \u003cp\u003eIn this study, Pearson\u0026rsquo;s correlation coefficient method was used to evaluate pairwise similarity and HMSI. However, no matter what method is used to calculate pairwise similarity, as long as the value of pairwise similarity is between [0, 1], one can calculate HMSI according to the above definition. In this study, MATLAB 2013b (Mathworks, Sherborn, MA) was used to calculate pairwise similarity and HMSI, and the corresponding MATLAB code could be request from the corresponding author of this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussions","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data preparation of chromatographic fingerprints\u003c/h2\u003e \u003cp\u003eFor the HPLC data of LC and Shiduqing capsules, the original data were imported into Agilent OpenLAB CDS 2.0 (Agilent Corporation, Santa Clara, California, USA) for data extraction, chromatographic peak identification, and noise elimination. For LC data, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the chromatograms were calibrated with 13 common peaks after Mank peak matching, and the relative retention time and relative peak area of each peak in the chromatograms were calculated using ferulic acid (peak 4) as the reference peak[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and used for further data analysis. For Shiduqing capsules data, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the chromatograms were calibrated with 22 common peaks after Mank peak matching, and the relative retention time and relative peak area of each peak in the chromatograms were calculated using baicalin (peak 11) as the reference peak[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and used for further data analysis. For GE data, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e, In order to eliminate possible scattering effects due to powder inhomogeneity, and also considering the stability of different pre-processing methods, the NIR spectra in this study were pre-processed using Standard Normal Variate (SNV)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The obtained SNV-NIR spectra were used for further data analysis.\u003c/p\u003e \u003cp\u003e[Figures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Computation of HMSI, its geometrical meanings, and comparisons\u003c/h2\u003e \u003cp\u003eTo demonstrate the usefulness of HMSI, 3 indices, the average correlation coefficient, median correlation coefficient, and HMSI were used to evaluate the batch consistency of the chromatographic fingerprints of LC and Shiduqing capsules, as well as the NIR fingerprints of GE. These specific results are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBatch similarity of HPLC fingerprints by different indices.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData (Batch number)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHMSI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLigusticum chuanxiong (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShiduqing (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrodia elata (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFor the LC data, the peak areas of 13 compounds in 48 samples were recorded for pairwise similarity and consistency analysis. The pairwise correlation coefficients between the 48 samples were in the range of 0.647\u0026ndash;0.991. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals the calculation method and geometric meanings of HMSI. Firstly, all pairwise similarity1 TT.1 in a batch and their percentiles were computed. Secondly, take the percents/100 as the horizontal ordinate and the corresponding percentiles as the longitudinal coordinates to make a scatter plot of all percentiles. The scatter plot can show the distribution of all pairwise similarity. According to the definition of HMSI, HMSI is the side length of the largest square under the curve formed by the scattered points of percentiles. For the LC data (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e), as the side length of the square is 0.826, the value of HMSI is also 0.826. It is obvious that HMSI takes into account both the level of pairwise similarity (by percentiles) and the number of samples covered by high pair similarity (by percents/100), and endows them with equal importance. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e, only the bulk of all pairwise similarity has a high value can HMSI has a high value. The HMSI of LC is 0.826, which means that 82.6% of the pairwise similarity is larger than or equal to 82.6% (0.826). The median of pairwise similarity of this dataset is 0.892, indicating that 50% of pairwise similarity is greater than or equal to 0.892, and 50% of pairwise similarity is less than or equal to 0.892, but little else. The average value of pairwise similarity is 0.883. Because high similarity and low similarity can offset each other, actually, the average value provides the least information for all pairwise similarity. Compared with the median and average, HMSI attaches importance to the number of samples with high similarity; median tends to evaluate the overall pairwise similarity robustly; while mean is not robust and provides the least information.\u003c/p\u003e \u003cp\u003eFor Shiduqing capsules data, the peak areas of 22 compounds in 10 regular batches and 1 unqualified batch of products were recorded for pairwise similarity and consistency analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The pairwise correlation coefficients between the 10 regular batchewhoss were in the range of 0.985\u0026ndash;0.993, indicating the 10 regular batches had high pairwise similarity. On the other hand, the pairwise correlation coefficients between the 11 batches (including the irregular batch) ranged from 0.886 to 0.993, indicating that the pairwise similarity between the irregular batch and the other 10 regular batches was relatively low and the lowest value was 0.886.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise correlation coefficients of 11 batches of Shiduqing capsules.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatches\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn order to further evaluate the whole consistency of the 11 batches, the HMSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the median correlation coefficient and the average correlation coefficient were calculated, which were 0.987, and 0.971, respectively. Although the similarity between the irregular batch and the 10 regular batches was low, the average correlation coefficient was still as high as 0.971 due to the counting of both high and low pair similarity values, indicating that the average correlation coefficient is insensitive to a small number of irregular samples and it cannot evaluate the batch consistency very well. Because value of median reflects the level of the bulk of data, the median correlation coefficient in this study was as high as 0.987, which was also insensitive to the presence of the irregular batch. In comparison, HMSI had a much lower value (0.895). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the inclusion of an irregular batch did cause a considerable decrease in some pairwise similarity. Actually, HMSI not only considered the pairwise similarity, but also examined the number of samples covered by high pair similarity, which is more reasonable than the other two indicators.\u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFor the GE powder data, based on the SNV-NIR spectral data (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the correlation coefficients between two-by-two samples were calculated, which ranged from 0.981 to 0.999, and the corresponding values of the HMSI, the median correlation coefficient, and the average correlation coefficient were all 0.992. Since the two-by-two similarity between samples was relatively high in all the batches (0.981\u0026ndash;0.999), the values of the three indexes all had a value of 0.992, indicating that the HMSI is also a reasonable index for evaluating batch consistency in general.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this study, a new h multiple similarity index (HMSI) was proposed to evaluate the consistency of TCM herbal fingerprints. Based on the HPLC fingerprints of Shiduqing soft capsules and LC, as well as the NIR fingerprints of GE, the consistency of fingerprints in a batch was evaluated by HMSI, average pairwise similarity and median pairwise similarity. The results showed that HMSI was suitable for consistency evaluation of chromatographic fingerprints, and it was more reasonable than average similarity and median similarity in practical applications. HMSI not only includes the similarity intensity of samples, but also considers the number of samples with high similarity. In conclusion, HMSI is a simple, robust and comprehensive index for consistency evaluation of herbal fingerprints.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLu Xu:\u003c/strong\u003e Conceptualization, Methodology, Investigation, Writing original draft, Resources, Supervision. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHai-Yan Fu:\u003c/strong\u003e Writing original draft, Resources, Supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRui Liu:\u0026nbsp;\u003c/strong\u003eMethodology, Writing original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQin Yang:\u003c/strong\u003e Conceptualization, Methodology, Investigation, Writing original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRongling Gu:\u003c/strong\u003e Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaoyu Zhu:\u0026nbsp;\u003c/strong\u003eData processing, Data anaiysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funding from National Natural Science Foundation of China (No. 82260896).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGad HA, El-Ahmady SH, Abou‐Shoer MI, Al‐Azizi MM (2013) Application of chemometrics in authentication of herbal medicines: a review. 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Anal Methods 9:1897\u0026ndash;1904. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1039/c7ay00153c\u003c/span\u003e\u003cspan address=\"10.1039/c7ay00153c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tongren University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Herbal chromatographic fingerprints, Batch consistency, h multiple similarity index (HMSI), Quality control","lastPublishedDoi":"10.21203/rs.3.rs-5470098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5470098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChromatographic fingerprints have been widely used for quality analysis of various herbs. Although many indices have been proposed to evaluate the similarity of herbal fingerprints, most of them are designated to evaluate pair similarity and the methods to measure batch consistency have been rarely discussed. Inspired by the popular h index, which has achieved great success in bibliometrics, this paper suggested a novel h multiple similarity index (HMSI) to evaluate the batch consistency of herbal fingerprints. HMSI was defined as: based on the pairwise similarity (ranged from [0, 1]) of all objects in the batch, if M% of all the pairwise similarity values is no less than M%, then the value of HMSI is M%. For applications, HMSI was used to evaluate the batch consistency of different herbal fingerprints, and the results were compared with those obtained by average similarity and median similarity. The results demonstrated that compared with average similarity and median similarity, HMSI is more reasonable to evaluate batch consistency of fingerprints and the herbal quality control system behind them. Similar to the original h index, HMSI not only includes the similarity intensity of objects in a batch, but also considered the quantity of objects with high similarity. HMSI was a simple, robust, easy-to-compute and yet comprehensive index to evaluate batch consistency of herbal fingerprints and herbal quality control system.\u003c/p\u003e","manuscriptTitle":"Evaluation of the batch consistency of herbal chromatographic fingerprints using a comprehensive and easy-to-compute method inspired by h index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 07:47:18","doi":"10.21203/rs.3.rs-5470098/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15daa51f-b450-4235-b3d1-cd14a03f5e42","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40364572,"name":"Drug Delivery"}],"tags":[],"updatedAt":"2024-11-19T07:47:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 07:47:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5470098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5470098","identity":"rs-5470098","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}