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Zheng Fan Affiliated Taihe Hospital of Chinese Medicine, Anhui University of Chinese Medicine, Taihe 236600, China

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Yuling Xu School of Pharmacy, Anhui University of Chinese Medicine, Hefei 230012, China

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Mengya Lu Affiliated Taihe Hospital of Chinese Medicine, Anhui University of Chinese Medicine, Taihe 236600, China

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Lanmeng Yan School of Pharmacy, Anhui University of Chinese Medicine, Hefei 230012, China

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Fangping Du Jinzhai County Jinshanzhai Edible and Pharmaceutical Fungi Plantation Co. Ltd., Jinzhai 237300, China

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Jing Xu School of Life Sciences, Anhui University of Chinese Medicine, Hefei 230012, China

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Xiaohui Tong School of Life Sciences, Anhui University of Chinese Medicine, Hefei 230012, China
Functional Activity and Resource Utilization on Edible and Medicinal Fungi Joint Laboratory of Anhui Province, Jinzhai 237300, China

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Rongchun Han School of Pharmacy, Anhui University of Chinese Medicine, Hefei 230012, China
Joint Research Center for Chinese Herbal Medicine of Anhui of IHM, Anhui University of Chinese Medicine, Hefei 230012, China

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Open access

Abstract

Based on chromatographic fingerprinting combined with quantitative analysis on characteristic chemical constituents as well as hierarchical cluster analysis, an easy and sensitive approach utilizing high performance liquid chromatography (HPLC) was developed for the identification and quality evaluation of Rutongshu oral liquid (ROL). What is more, nontargeted metabolomic analysis was conducted to gain a global view in terms of its chemical profile. In this study, 16 peaks from different batches (S1–S10) of ROL samples produced by Taihe Hospital of Chinese Medicine were selected as common peaks for the evaluation of their similarity whose values of all tested 10 batches exceeded 0.90 when compared with the control fingerprints. Meanwhile, simultaneous quantification of five markers in the oral solution, including albiflorin, paeoniflorin, chlorogenic acid, quercetin and ferulic acid was performed, and standard curves established for respective reference substances showed good regression in the linear range (r2 > 0.999) with recoveries in the range of 98.96–102.35%. The ultra-high performance liquid chromatography (UHPLC) combined with Orbitrap Exploris 120 mass spectrometer resulted in 88 identified compounds. The results of validation showed that the established method was reproducible, precise and stable. This study offers an effective, dependable and useful approach for the systematic evaluation of the hospital formulation ROL.

Abstract

Based on chromatographic fingerprinting combined with quantitative analysis on characteristic chemical constituents as well as hierarchical cluster analysis, an easy and sensitive approach utilizing high performance liquid chromatography (HPLC) was developed for the identification and quality evaluation of Rutongshu oral liquid (ROL). What is more, nontargeted metabolomic analysis was conducted to gain a global view in terms of its chemical profile. In this study, 16 peaks from different batches (S1–S10) of ROL samples produced by Taihe Hospital of Chinese Medicine were selected as common peaks for the evaluation of their similarity whose values of all tested 10 batches exceeded 0.90 when compared with the control fingerprints. Meanwhile, simultaneous quantification of five markers in the oral solution, including albiflorin, paeoniflorin, chlorogenic acid, quercetin and ferulic acid was performed, and standard curves established for respective reference substances showed good regression in the linear range (r2 > 0.999) with recoveries in the range of 98.96–102.35%. The ultra-high performance liquid chromatography (UHPLC) combined with Orbitrap Exploris 120 mass spectrometer resulted in 88 identified compounds. The results of validation showed that the established method was reproducible, precise and stable. This study offers an effective, dependable and useful approach for the systematic evaluation of the hospital formulation ROL.

Introduction

Mammary gland hyperplasia (MGH), also known as hyperplastic breast disease, is a lesion of the ducts and lobules of the breast. patients with MGH can experience breast pain with the formation of breast lumps [1]. As a common disease, more susceptible in younger and middle-aged women, MGH accounts for more than 78.6% of all cases of breast disease and seriously affects the normal life of these patients [2]. MGH causes excessive proliferation of breast epithelial cells and hyperplasia of breast tissue, which mainly manifests as breast lumps, breast pain, irritability and irregular menstruation [34]. Rutongshu oral liquid (ROL), a hospital formulation of Taihe Hospital of Chinese Medicine affiliated to Anhui University of Chinese Medicine consists of ten medicinal herbs, which are Angelica sinensis (Oliv.) Diels, Paeonia lactiflora Pall., Ecklonia kurome, Sargassum pallidum (Turn.) C. Ag, Taraxacum mongolicum Hand.-Mazz., Prunella vulgaris L., Epimedium brevicornu Maxim., Cyperus rotundus L., Hordeum vulgare L., Citrus reticulata Blanco and has been prescribed in the hospital for 8 years to treat MGH. Every year, approximately 1700 MGH patients took ROL.

Traditional Chinese medicine (TCM) has been practiced in China for thousands of years and is gaining recognition by an increasing number of people around the world [5]. In contrast to Western medicine which has always been more concerned with single chemical entities, TCM takes into consideration a variety of complex compounds, with a herbal medicine often consisting of over hundreds of chemical components. Curing diseases using TCM often relies on the synergistic effects of its multi-target, multi-component formulations [6]. As an unfavorable result, quality control becomes a challenge that limits its development and commercial acceptance. Therefore, the establishment of a scientific and rational quality control system is of great importance for TCM [7].

In recent years, the quality of herbal medicine as well as their related products have been evaluated adopting chromatographic fingerprinting which emphasizes the characteristics of overall sample composition [8–10]. Among the many techniques used to characterize chemical profiles including Fourier-transform infrared (FT-IR) [11–13], mass spectrometry (MS) [14–16], nuclear magnetic resonance (NMR) [17–19], gas chromatography (GC) [20–22], high performance thin layer chromatography (HPTLC) [23–25] and thin-layer chromatography (TLC) [26–28], ultra-high performance liquid chromatography (UHPLC) [29–31] and high-performance liquid chromatography (HPLC) [32–34] are the most popularly used approaches used for fingerprinting due to its high sensitivity, high selectivity and low cost [35, 36].

Although ROL is an experienced formula of famous Chinese medicine practitioners with definite clinical efficacy, its clinical efficacy can be compromised by the unstable quality among different batches. In fact, as a common knowledge, whether the decoction and administration method of Chinese medicine compound prescriptions are standardized and correct is the key factor concerning the effect of Chinese medicine clinical treatment and the safety of Chinese medicine compound prescription use [37]. In the study of compounded preparations, it is important to pay attention to the source of each peak on the preparation fingerprint and to study the variation of the peaks. The assessment of dependence is fundamental and is the basis for quality control of the preparation. Here, we report the first systematic evaluation on the components of ROL using fingerprint analysis. The characterization profile of ROL was established by HPLC. Qualitatively and quantitative analyses were conducted for the five characteristic compounds, namely, albiflorin, paeoniflorin, chlorogenic acid, quercetin and ferulic acid. Regarding chromatographic fingerprinting, 10 batches of ROL samples were collected and assessed to provide a basis for the overall quality control of ROL using multi-indicator components. What is more, 88 compounds were identified by UHPLC combined with Orbitrap Exploris 120 mass spectrometer.

Experiential

Materials and reagents

Albiflorin (purity 98%), paeoniflorin (purity 99%), chlorogenic acid (purity 98%), quercetin (purity 99%) and ferulic acid (purity 98%) were all obtained from Desite Bio-Technology (Chengdu, China). Absorption profiles for the 5 compounds were determined by the UV–Vis spectrophotometer (SPECORD S600, Analytik Jena AG, Germany) within the range of 200–600 nm. ROL of 10 different batches were provided by Taihe Hospital of Traditional Chinese Medicine (batch No. 21060901, 21071201, 21081601, 21082301, 22072801, 22062901, 21081801, 22093001, 22112501, 22121701). HPLC grade acetonitrile and methanol were supplied by OCEANPAK, and phosphoric acid by Shanghai Runjie Chemical Reagent Co. The AS series ultrasonic cleaners were obtained from Tianjin Aotearn Instruments Co.

Preparation of standard solutions

Five standards were weighed precisely and dissolved in HPLC grade methanol at the following concentrations: albiflorin 1.021 mg mL−1, paeoniflorin 1.003 mg mL−1, chlorogenic acid 1.016 mg mL−1, quercetin 0.999 mg mL−1 and ferulic acid 1.013 mg mL−1 to obtain standard solutions. The solutions were stored at 4 °C and protected from light.

Preparation of sample solutions

Ten mL of ROL were taken from different batches of samples and left overnight at −20 °C. The frozen material was freeze-dried by a LGJ-10 lyophilizer (Songyuan, China) to get powder for downstream analysis. One g of the resultant powder was precisely weighed and placed in a capped volumetric flask (10 mL) containing 9 mL of extraction solution. The mixture was sonicated for 1 h at 40 °C and centrifuged for the extraction and purification of the characteristic chemicals. The supernatants from all 10 batches were stored in the dark at 4 °C before use and filtered through an organic filter membrane of 0.22 μm prior to HPLC analysis.

Chromatographic conditions for ROL

Concerning fingerprinting, the instrument was a HPLC LC-16 (Shimadzu Instruments, Japan) and all detection steps were performed on a Topsil C18 column (4.6 mm × 250 mm, 5 μm). The mobile phase was 0.1% phosphoric acid aqueous solution (A)-acetonitrile (B) with gradient elution. The flow rate of the mobile phase was maintained at 1 mL min−1 and the following gradient of acetonitrile (B) was applied: 0–15 min, 5–10%; 15–30 min, 10–15%; 30–35 min, 15–20%; 35–40 min, 20–22%; 40–50 min, 22–25%; 50–60 min, 25–30%; 60–75 min, 30–40%; 75–80 min, 5%. The wavelength was adapted to 230 nm during the fingerprinting analysis, which displayed most of the peaks. The column temperature was set at 35 °C and the specimen injection volume was 15 μL.

As far as nontargeted metabolomic analysis is concerned, all ROL samples were separated on a Vanquish UHPLC system (Thermo Fishier Scientific, USA). The column was Poroshell 120 EC-C18 (3.0 mm × 150 mm, 2.7 μm) from Agilent (Agilent Corporation, USA). 0.1% formic acid in water and 0.1% formic acid in acetonitrile were chosen respectively as the elution solution A and B at the flow rate of 0.3 mL min−1. The gradient program was as follows: 0–5 min, 5–10% B; 5–15 min, 10–30% B; 15–28 min, 30–90% B; 28–30 min, 90% B; 30–36 min, 90–5% B; 36–41 min, 5% B. The oven temperature was fixed at 35 °C and the injection volume was 2 μL. The separated samples were analyzed by Orbitrap Exploris 120 mass spectrometer (Thermo Fishier Scientific, USA) equipped with an electrospray ionization (ESI) source. The ESI source was manipulated in both positive ion and negative ion detection mode and its parameters were as follows: ion spray voltage floating at 3500 V (+) or 2500 V (−); sheath gas at 50 arb; aux gas at 12 arb; ion transfer tube temperature at 325 °C; vaporizer temperature at 350 °C. The full MS experiments were run with a scan range from m/z 150 to m/z 1500.

Data analysis

Recommended by the China Food and Drug Administration (CFDA), the chromatographic fingerprint data of the samples were evaluated using the “Chromatographic Fingerprint Similarity Evaluation System for Traditional Chinese Medicine” (version 2012A) [38]. The correlation coefficients of the samples were computed and the similarities between the common chromatograms resulted from the individual chromatograms and the samples examined were analyzed. In terms of limits of detection (LOD) and limits of quantification (LOQ), signal-to-noise (S/N) ratios of 3:1 and 10:1 were used for determination, respectively. Regarding hierarchical cluster analysis, 16 common peaks concluded from 10 batches were uploaded to SPSS 27.0 and squared Euclidean distance for similarity measurement as well as between-groups linkage were adopted for data analysis. For nontargeted metabolomic analysis, Xcalibur software (version 4.1.31) and Compound Discoverer (version 3.3) were utilized according to the instructions (Thermo Fishier Scientific, USA).

Results and discussion

Optimization of chromatographic conditions

The mobile phases with different compositions and ratios (chromatographic grade methanol-ultrapure water, chromatographic grade methanol-0.1% phosphoric acid aqueous solution, chromatographic grade methanol-0.2% phosphoric acid aqueous solution, chromatographic grade acetonitrile-ultrapure water, chromatographic grade acetonitrile-0.1% phosphoric acid aqueous solution and chromatographic grade acetonitrile-0.2% phosphoric acid aqueous solution) were tested first. The experimental results showed that chromatographic grade acetonitrile-0.1% phosphoric acid aqueous solution was the most suitable because it had a more stable baseline than the others, better separated and more characterized peaks on the chromatogram.

In addition, four column temperatures of 30, 35 40 and 45 °C were examined. The best situation was determined to be 35 °C. Regarding detection wavelength, not only more peaks could be detected at 230 nm, but the five key compounds showed better absorption than that of 254, 270, 320 and 380 nm. Therefore, to reflect better chemical properties of ROL, 230 nm was used as the optimal wavelength.

Using the same strategy, UHPLC conditions were also determined for ROL.

Optimization of sample extraction

The solutions used for extraction and the extraction method were studied. The samples were subjected to a freeze dryer and after 72 h of lyophilization, different solvent percentages of methanol (60, 70, 80 and 100%) were examined and 70% methanol was chosen because it achieved the highest extraction rate. Ultrasonic extraction approaches (45, 60 and 75 min) at different temperatures (40, 50, 60 °C) were assessed. The results suggested that 60 min of sonication at 40 °C was most suitable in terms of the peak domains of the analytes in the HPLC/UHPLC chromatograms. As a result, freeze-dried samples were mixed with 70% methanol and sonicated for 60 min at 40 °C for the downstream analyses.

Method validation of quantitative analysis

We prepared methanol stock solutions for the five standards whose structures were shown in Supplementary Fig. 1. Different concentrations of the five standard solutions were analyzed, and then standard curves were established based on the peak area (y) and the injection volume (x) of each standard. The standard curves and ranges for the five analytes were demonstrated in Table 1. Standard curves established for respective reference substances exhibited good linearity (r2 > 0.999) over the detection range.

Table 1.

Data for quantification and linear regression assessment (n = 6)

ChemicalsRegression equationLinear range (μg)Correlation coefficient (r2)LOD (μg)LOQ (μg)
quercetiny = 7.2285x – 2.47553–110.99970.250.83
ferulic acidy = 19.549x – 6.34761–110.99920.200.65
paeonifloriny = 42.2x – 45.3583–120.99910.331.21
chlorogenic acidy = 14.761x – 63.6555–120.99930.411.34
alibifloriny = 29.327x – 7.96121–110.99920.120.41

The instrumental precision results for the five standards and their relative standard deviation (RSD) values were less than 1.57% (n = 6). What's more, the reproducibility study of the method showed that the assay was reproducible with RSD less than 2.44% (n = 6) for the five analytes. The stability of the sample solutions was evaluated at 0, 2, 4, 8, 16, 24, 36, 48 and 72 h stored at 20 °C. The test results showed that the solutions were stable for at least 48 h (RSD <1.91%).

Recovery test was also conducted to evaluate accuracy of this method. Five standards at high, medium, and low levels were added to 10 mL of ROL (22072801). Subsequently, the 2mixtures were processed and studied according to the chosen method, and each level of experiment was conducted in triplicate. The formula for average recovery rate was: [(discovered amount - original amount)/added amount] × 100%. It was found that the average recovery rates for the 5 standards ranged from 98.96 to 102.35%, with RSD <2.13%. Hence, the experimental data indicated that the method was reliable for simultaneous quantification of five standard substances.

Quantitative determination of chemicals in ROL

To evaluate the contents of five compounds in 10 batches of ROL, retention times and online spectra of the samples were compared with those of the 5 standards whose UV absorption features were also considered (Fig. 1). Concentrations of the 5 characteristic compounds in ROL were then calculated. The results, as indicated in Table 2, showed relatively small variation in terms of the tested contents. The concentration of albiflorin ranged from 0.0008 to 0.0023 μg μL−1, paeoniflorin 0.0011–0.0032 μg μL−1, chlorogenic acid 0.0027–0.0060 μg μL−1, quercetin 0.0043–0.0129 μg μL−1 and ferulic acid 0.0005–0.0045 μg μL−1.

Fig. 1.
Fig. 1.

The UV spectra of five markers concerning quality of ROL

Citation: Acta Chromatographica 2024; 10.1556/1326.2023.01180

Table 2.

Chemical concentrations of ROL from different batches

No of batchesQuercetin (μg μL−1)Ferulic acid (μg μL−1)Paeoniflorin (μg μL−1)Chlorogenic acid (μg μL−1)Alibiflorin (μg μL−1)
210609010.00790.00070.00180.00580.0011
210712010.01290.00350.00190.00430.0019
210816010.00670.00260.00110.00490.0013
210823010.00960.00050.00230.00600.0020
220728010.01130.00310.00190.00550.0014
220629010.00710.00160.00240.00270.0017
210818010.00890.00280.00270.00490.0023
220930010.00750.00450.00130.00460.0021
221125010.00430.00230.00320.00470.0008
221217010.00830.00140.00170.00530.0015

Method validation of HPLC fingerprinting

The precision of the instrument was evaluated by analyzing the same solution containing 5 standards 6 times in a row in 24 h. The results were expressed as RSD whose values for relative peak area (RPA) and relative retention time (RRT) were less than 3.72% and 0.99%, respectively. To determine the reproducibility of this approach, six independently prepared samples from the same batch (22072801) were analyzed, and the RSD values for RPA and RRT were less than 4.71% and 0.52%, respectively. For the evaluation of stability, the sample solution stored at 20 °C were analyzed at 0, 2, 4, 8, 16, 24, 36 and 48 h. The RSD values for RPA and RRT were than 2.57% and 0.83%, respectively, suggesting that the sample solution was stable for at least 48 h in room temperature.

HPLC-DAD fingerprinting and hierarchical cluster analysis

To facilitate fingerprinting analysis, chromatograms resulted from different batches of samples must be standardized at first. The process of standardization included selection of “common peaks” in the generated chromatograms and for all common peaks, retention times were to be normalized. In order to standardize the fingerprints, data from 10 batches were subjected to fingerprint chromatography. The peaks that appeared in each batch of samples were designated as the common peaks for ROL. The fingerprint profiles of these specimens were shown in Fig. 2A, and the reference chromatogram of ROL was illustrated in Fig. 2B. Although more than 30 chromatographic peaks could be identified in the fingerprint profile of ROL, only 16 of them were termed as common peaks. Peaks 4, 7, 8, 9 and 14 were determined as chlorogenic acid, albiflorin, paeoniflorin, ferulic acid and quercetin, by comparing to the respective spectra of standards. By applying Chromatographic Similarity Evaluation software, correlation coefficient was calculated for similarity analysis. The chromatograms of each individual sample and the reference were compared pairwise. As demonstrated in Table 3, correlation coefficients ranged from 0.900 to 0.997, suggesting similar chemical constituents in tested batches.

Fig. 2.
Fig. 2.

UHPLC fingerprint of 10 ROL batches A and the reference chromatogram B obtained by using the fingerprint similarity evaluation system of traditional Chinese medicine. The peaks labeled 1–16 in the chromatogram represented the 16 common peaks. The peaks marked with 4, 7, 8, 9, and 14 were chlorogenic acid, albiflorin, paeoniflorin, ferulic acid, and quercetin, respectively

Citation: Acta Chromatographica 2024; 10.1556/1326.2023.01180

Table 3.

Analysis on fingerprint similarities for ROL from different batches

S1S2S3S4S5S6S7S8S9S10Reference
S11.0000.9170.9210.9050.9660.9760.9550.9970.9530.9620.982
S20.9171.0000.9030.9830.9610.9810.9000.9180.9340.9230.953
S30.9210.9031.0000.9240.9400.9390.9040.9270.9720.9880.947
S40.9050.9830.9241.0000.9730.9940.9080.9150.9370.9270.957
S50.9660.9610.9400.9731.0000.9970.9710.9720.9230.9380.969
S60.9760.9810.9390.9940.9971.0000.9750.9820.9390.9500.979
S70.9550.9000.9040.9080.9710.9751.0000.9650.9680.9740.980
S80.9970.9180.9270.9150.9720.9820.9651.0000.9630.9700.988
S90.9530.9340.9720.9370.9230.9390.9680.9631.0000.9920.978
S100.9620.9230.9880.9270.9380.9500.9740.9700.9921.0000.982
Reference0.9820.9530.9470.9570.9690.9790.9800.9880.9780.9821.000

Judging from the 16 common peaks that all ROL batches shared, the result of hierarchical cluster analysis suggested that samples could be divided into 2 groups, sample no. 2 and the rest 9 ones (Fig. 3), underlining the possible cause that raw materials collected to produce different batches influenced the quality of final oral liquids. This is also a good reason why quality control is indispensable for traditional Chinese medicine.

Fig. 3.
Fig. 3.

Hierarchical cluster analysis for 10 batches of ROL

Citation: Acta Chromatographica 2024; 10.1556/1326.2023.01180

Nontargeted metabolomic analysis on ROL

Total ion chromatograms of the two detection modes were presented in Fig. 4. In order to determine the chemical components of ROL, the raw data were acquired and processed using the standard procedure recommended by the mass spectrometer manufacturer. To facilitate the identification of compounds presented in the samples, in addition to the chemical library in Compound Discoverer, we built our own database by integrating information from relevant literature and online databases including China National Knowledge Infrastructure (CNKI), PubMed, PubChem and ChemSpider. By taking into consideration of adduct ions, precise molecular weight, fragment ions, 88 compounds were identified (Table 4) including cynaroside, berberine, crocin, rutin and so forth.

Fig. 4.
Fig. 4.

Total ion chromatograms of the two detection modes. A: positive ion mode; B: negative ion mode

Citation: Acta Chromatographica 2024; 10.1556/1326.2023.01180

Table 4.

The 88 chemicals identified for ROL using nontargeted metabolomic analysis

NotRAdduct IonsMolecular WeightAnnot. Delta Mass [ppm]Fragment IonsFormulaName
10.786[M + H]+1188.152482.71189.16064,130.08676,84.08099,60.08095C9 H20 N2 O2N6,N6,N6-Trimethyl-L-lysine
20.879[M + H]+1167.058241.01168.06526,150.05463,96.04450C8 H9 N O3Pyridoxal
30.930[M + H]+1165.115361.81166.12283,121.06494,93.06996C10 H15 N OHordenine
40.933[M + H]+1193.146661.76194.15422,69.03352,67.05432C12 H19 N O4-Hexyloxyaniline
51.074[M + H]+1181.073891.10182.08087,165.05479,136.07584,119.04927C9 H11 N O32-Hydroxyphenylalanine
61.269[M + H]+1190.110612.81191.11835,160.07596,148.07599C11 H14 N2 OCytisine
71.341[M + H]+1204.126262.42205.13474,160.07588,58.06520C12 H16 N2 OBufotenin
82.495[M − H]−1284.271533.00283.26514C18 H36 O2Stearic Acid
92.546[M − H]−1504.169033.07503.16296,179.05655,89.02473,59.01402C18 H32 O16D-Raffinose
102.547[M − H]−1342.116212.06341.11096,179.05646,59.01400C12 H22 011α,α-Trehalose
112.559[M − H]−1260.029722.06259.01804,96.96960,78.95940C6 H13 O9 PD-Glucose-6-phosphate
122.560[M − H]−1172.013672.44171.00606,96.96959,78.95930C3 H9 O6 PGlycerol 3-phosphate
132.561[M − H]−1155.069483.07154.06268,137.03610,93.04610C6 H9 N3 O2L-Histidine
142.587[M − H]−1196.058300.46195.05153,129.01974,99.00898,75.00900,59.01397C6 H12 O7Gluconic acid
152.612[M − H]−1164.068471.25163.06168,101.02460,85.02968,59.01398C6 H12 O51,5-Anhydro-D-glucitol
162.632[M − H]−1192.063391.42191.05650,173.04631,127.04047,85.02970C7 H12 O6D-(−)-Quinic acid
172.633[M − H]−1180.063392.17179.056650,89.02470,75.00900,59.01400C6 H12 O6D-(−)-Fructose
182.679[M − H]−1174.016440.79173.00958,154.99913,111.00911,85.02976C6 H6 O6trans-Aconitic acid
192.685[M − H]−1168.028342.11167.02147,124.01576,96.02061C5 H4 N4 O3uric acid
202.743[M − H]−1283.091672.11282.08347,150.04251,133.01582C10 H13 N5 O5Guanosine
213.029[M − H]−1363.058001.68362.05072,211.00192,78.95928C10 H14 N5 O8 PGuanosine monophosphate(GMP)
223.106[M − H]−1244.069542.45243.06297,200.05702,110.02507C9 H12 N2 O6uridine
233.112[M − H]−1342.116211.39341.10956,179.05638,89.02464,59.01395C12 H22 O11Sucrose
243.173[M − H]−1192.027002.45191.02052,173.00916,129.01981,87.00876,57.03476C6 H8 O7Citric acid
253.165[M + FA-H]−1376.136952.10375.13080,195.06659,151.07695C16 H24 O10Mussaenosidic acid
263.171[M − H]−1267.096752.78266.09070,134.04762,92.02602C10 H13 N5 O4adenosine
273.175[M − H]−1165.052822.64161.04597,99.04527,59.01400C6 H10 O53-Hydroxy-3-methylglutaric acid
283.191[M − H]−1188.032093.24187.02525,125.02480,99.04546C7 H8 O63-Butene-1,2,3-tricarboxylic acid
293.567[M − H]−1170.021522.27169.01426,125.02473,97.02978,81.03485,69.03479C7 H6 O5gallic acid
303.584[M − H]−1186.052821.89185.04462,141.05627,97.06614C8 H10 O5endothal
313.677[M − H]−1210.037572.75209.03046,191.02039,85.02975,71.01405C6 H10 O8D-Saccharic acid
323.925[M − H]−1226.095362.90225.08846,181.09868,163.08801C10 H14 N2 O4Porphobilinogen
334.069[M − H]−1165.078982.67164.07228,147.04568,72.00946C9 H11 N O2L-Phenylalanine
344.209[M − H]−1219.110672.49218.10402,146.08269,88.04067C9 H17 N O5Pantothenic acid
357.611[M − H]−1154.026613.46153.01994,109.02997,108.02217C7 H6 O4Protocatechuic acid
368.699[M + H]+1341.162713.01342.17136,297.11328,58.06529C20 H23 N O4N-Methylhernagine
379.494[M − H]−1204.089883.66203.08372,159.09280,116.05124,74.02507C11 H12 N2 O2DL-tryptophan
389.688[M + H]+1329.162713.80330.17142,192.10268,137.06021C19 H23 N O4Sinomenine
3910.264[M + H]+1224.104862.88207.10188,165.05518,151.03911,57.07008C12 H16 O4Senkyunolide H
4010.879[M − H]−1176.068473.67175.06189,115.04057,113.06131,85.06625C7 H12 O52-Isopropylmalic acid
4111.114[M − H]−1280.240234.64279.23434,61.98835C18 H32 O2Linoleic acid
4211.576[M − H]−1354.095082.75353.08771,191.05687,161.02441,127.0406,85.02975C16 H18 O9Chlorogenic Acid
4311.606[M − H]−1164.047343.83163.04016,119.05083C9 H8 O32-HYdroxycinnamic acid
4412.006[M − H]−1354.095082.59353.08771,191.05673,179.03563,135.04561,93.03459C16 H18 O9Neochlorogrnic acid
4512.893[M − H]−1152.047344.73152.04399,151.04076,109.03012C8 H8 O3Resorcinol monoacetate
4613.340[M − H]−1194.057913.94193.05148,178.02788,149.06140,134.03790C10 H10 O4Ferulic acid
4713.652[M + H]+1180.042263.36163.03964,145.02878,135.04462,117.03394C9 H8 O4caffeic acid
4813.652[M + H]+1312.07218−4.19313.0779,277.05649,267.07208C14 H11 F3 N2 O35-Hydroxyflunixin
4914.376[M − H]−1198.052823.96197.04639,182.02293,153.05637,121.03001C9 H10 O5Syringic acid
5014.517[M − H]−1376.152202.97375.14575,360.12198,257.08209,241.05232C20 H24 O7Cycloolivil
5114.578[M + FA-H]−1480.163162.71479.1666,449.1472,327.1100,165.0564,121.0301 [M + HCOO]-C23 H28 O11Paeoniflorin
5214.706[M − H]−1173.105193.78172.09862,130.08794,128.10844C8 H15 N O3N-Acetyl-D-alloisoleucine
5315.502[M − H]−1610.153383.50609.14819,300.02878,271.02603,255.03108C27 H30 O16Rutin
5415.746[M − H]−1174.089214.24173.08267,111.08207,83.05064C8 H14 O4suberic acid
5515.753[M − H]−1522.210113.88359.15001,329.14081,160.05298,175.07645,71.01385C26 H34 O11Lariciresinol 4-O-glucoside
5615.803[M − H]−1164.047343.64163.04079,120.05421,119.05075C9 H8 O43-coumaric acid
5715.908[M − H]−1976.378753.96975.37439,341.10623,113.02433C44 H64 O24crocin
5815.951[M + H]+1335.115764.32336.12433,320.09283,292.09802,278.08170C20 H17 N O4Berberine
5916.040[M − H]−1578.163562.46577.15778,431.10046,285.04150,283.02557C27 H30 O14kaempferitrin
6016.054[M − H]−1448.100563.66447.09500,285.04147,284.03372,151.00412C21 H20 O11Cynaroside
6116.066[M − H]−1464.095483.81463.09006,301.03665,300.02896,271.02606,151.00420Quercetin-3β-D-glucoside
6216.081[M − H]−1207.089544.26206.08293,164.07237,147.04562,91.05579,58.03007C11 H13 N O3N-Acetyl-L-phemylalanine
6316.197[M + H]+1838.289543.52531.18805,369.13480,313.07205C39 H50 O20Epimedin A
6416.622[M − H]−1580.179213.99579.17352,271.06216,151.00429,119.05075C27 H32 O14Naringin
6516.809[M + H]+1186.031694.27187.03975,131.04970,115.05466C11 H6 O3Psoralen
6616.967[M + H]+1822.294634.09531.18811,369.13464,313.07196C39 H50 O19Epimedin C
6717.209[M − H]−1448.100564.12447.09512,284.03400,255.03116,227.03607C21 H20 O11astragalin
6817.229[M + H]+1676.236723.58531.18823,369.13461,313.07187,85.02866C33 H40 O15Icariin
6917.340[M − H]−1480.163164.35479.15781,327.10959,195.06728,151.07719,121.02999C23 H28 O11albiflorin
7017.635[M − H]−1180.042264.15135.04575,93.03501,59.01405C9 H8 O4Acetylsalicylic acid
7117.827[M − H]−1516.126782.75353.08896,179.03566,173.04619C25 H24 O124,5-Dicaffeoylquinic acid
7218.172[M − H]−1188.104863.23187.09846,125.09773,97.06635C9 H16 O4Azelaic acid
7319.666[M − H]−1264.136163.95263.13019,219.14006,149.09807C15 H20 O4Ambrosic acid
7419.755[M − H]−1162.104474.67161.09801,106.04298C11 H14 O4-Cyclopentylphenol
7519.767[M − H]−1202.120514.63201.11414,183.10349,139.11348C10 H18 O43-tert-butyladipic acid
7619.905[M − H]−1716.137734.40535.09058,311.05750,135.04582C36 H28 O16Schizotenuin A
7719.950[M − H]−1286.047744.61285.04181,175.04059,151.00443,133.03020C15 H10 O6Luteolin
7820.53[M − H]−1302.042654.65301.03656,178.99934,151.00433,107.01436C15 H10 O7Quercetin
7921.621[M + H]+1514.183904.62369.13489,313.07220,85.02866,71.04945C27 H30 O10Baohuoside Ⅰ
8022.699[M − H]−1328.224974.23327.21915,211.13483,171.10329,85.02983C18 H32 O5Corchorifatty acid F
8125.086[M − H]−1780.429614.81779.42590,780.42828,85.03002C41 H64 O14Digoxin
8225.167[M + H]+1296.050934.78148.02580,116.05328,88.02190,59.99061C10 H20 N2 S4Disulfiram
8325.342[M − H]−1504.345094.52503.34009,419.29730,177.12953,119.08731C30 H48 O6Arjungenin
8425.361[M − H]−1230.151814.36229.14490,211.13486,167.14436C12 H22 O4Dodecanedioic acid
8527.926[M − H]−1266.155184.78265.14920,96.96060,79.95790C12 H26 O4 SDodecyl sulfate
8628.421[M + H]+1283.323904.47284.33215,60.08097,57.07021C19 H41 NCetrimonium
8728.634[M + H]+1281.271864.52282.28015,247.24295,83.08580,69.07016C18 H35 N OOleamide
8829.399[M − H]−1270.052824.51270.04910,269.04681,225.05664C15 H10 O5genistein

Conclusions

By using HPLC, 5 phytochemical markers were determined in a total of 10 ROL batches. Similar chemical patterns were observed for all tested batches with relatively small variation concerning concentrations of the 5 phytochemical markers, suggesting quality of ROL, a hospital formulation of Taihe Hospital of Chinese Medicine affiliated to Anhui University of Chinese Medicine, is stable and meets the standard set by the Chinese government. The data resulted from validation experiments demonstrated that the established approach was satisfactory as far as sensitivity and reproducibility were concerned. The 10 batches of ROL were assessed with a combination of similarity and fingerprinting analysis. Nontargeted metabolomic analysis revealed extra 83 chemical constituents, in addition to the 5 characteristic compounds. The findings in this study indicate chromatographic fingerprinting coupled with nontargeted metabolomic analysis is applicable for the quality control of ROL.

Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1556/1326.2023.01180.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (82204710), Ministry of Science and Technology of China (G2022019018L), Anhui Natural Science Foundation (2208085QH271), Research Funds of Joint Research Center for Chinese Herbal Medicine of Anhui of IHM (yjzx2023004), Anhui University of Chinese Medicine (RH2300001171, 2023LCTH18, 2021LCTH04) and Fuyang Health Commission (FY2023-007).

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Supplementary Materials

  • 1.

    Li, X.; Xin, P.; Wang, C.; Wang, Z.; Wang, Q.; Kuang, H. Mechanisms of traditional Chinese medicine in the treatment of mammary gland hyperplasia. Am. J. Chin. Med. 2017, 45(3), 443458.

    • Search Google Scholar
    • Export Citation
  • 2.

    Li, H.-T.; Liu, H.-H.; Yang, Y.-X.; Wang, T.; Zhou, X.-L.; Yu, Y.; Li, S.-N.; Zheng, Y.; Zhang, P.; Wang, R.-L.; Li, J.-Y.; Zhang, S.-W.; Li, K.; Li, P.-Y.; Qian, L.-Q. Therapeutic effects of a traditional Chinese medicine formula plus Tamoxifen vs. Tamoxifen for the treatment of mammary gland hyperplasia: a meta-analysis of randomized trials. Front. Pharmacol. 2018, 9, 45.

    • Search Google Scholar
    • Export Citation
  • 3.

    Dong, F.; Irshad, H.; Oh, E.-Y.; Lerwill, M.-F.; Brachtel, E.-F.; Jones, N.-C.; Knoblauch, N.-W.; Montaser-Kouhsari, L.; Johnson, N.-B.; Rao, L.-K.; Faulkner-Jones, B.; Wilbur, D.-C.; Schnitt, S.-J.; Beck, A.-H. Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PLoS One 2014, 9(12), e114885.

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    Ping, Y.; Gao, Q.; Li, C.; Wang, Y.; Wang, Y.; Li, S.; Qiu, M.; Zhang, L.; Tu, A.; Tian, Y.; Zhao, H. Construction of microneedle of Atractylodes macrocephala Rhizoma aqueous extract and effect on mammary gland hyperplasia based on intestinal flora. Front. Endocrinol. 2023, 14, 1158318.

    • Search Google Scholar
    • Export Citation
  • 5.

    Oravecz, M.; Mészáros, J. Traditional Chinese medicine: theoretical background and its use in China. Orvosi Hetilap 2012, 153(19), 723731.

    • Search Google Scholar
    • Export Citation
  • 6.

    Han, Y.; Sun, H.; Zhang, A.; Yan, G.; Wang, X.-J. Chinmedomics, a new strategy for evaluating the therapeutic efficacy of herbal medicines. Pharmacol. & Ther. 2020, 216, 107680.

    • Search Google Scholar
    • Export Citation
  • 7.

    Liang, Y.-Z.; Wang, W.-P. Chromatographic fingerprinting coupled with chemometrics for quality control of traditional Chinese medicines. Chimia 2011, 65(12), 944951.

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    • Export Citation
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    Liang, Y.; Xie, P.; Chan, K. Perspective of chemical fingerprinting of Chinese herbs. Planta Med. 2010, 76(17), 19972003.

  • 9.

    Shi, Q.; Xia, F.; Wang, Q.; Liao, F.; Guo, Q.; Xu, C.; Wang, J. Discovery and repurposing of artemisinin. Front. Med. 2022, 16(1), 19.

    • Search Google Scholar
    • Export Citation
  • 10.

    Zhang, L.; Yan, J.; Liu, X.; Ye, Z.; Yang, X.; Meyboom, R.; Chan, K.; Shaw, D.; Duez, P. Pharmacovigilance practice and risk control of Traditional Chinese Medicine drugs in China: current status and future perspective. J. Ethnopharmacology 2012, 140(3), 519525.

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    • Export Citation
  • 11.

    Wang, Y.; Cui, W.; Pang, G.; Xiong, L.; Liu, Q.; Xu, L.; Li, H.; Lin, Y. Analyses of physical and chemical compositions of different medicinal specifications of CRPV by use of multiple instrumental techniques combined with multivariate statistical analysis. Molecules (Basel, Switzerland) 2022, 27(10), 3285.

    • Search Google Scholar
    • Export Citation
  • 12.

    Pei, L.-K.; Sun, S.-Q.; Guo, B.-L.; Huang, W.-H.; Xiao, P.-G. Fast quality control of Herba Epimedii by using Fourier transform infrared spectroscopy. Spectrochimica Acta Part A, Mol. Biomol. Spectrosc. 2008, 70(2), 258264.

    • Search Google Scholar
    • Export Citation
  • 13.

    Liu, A.; Wang, J.; Guo, Y.; Xiao, Y.; Wang, Y.; Sun, S.; Chen, J. Evaluation on the concentration change of paeoniflorin and glycyrrhizic acid in different formulations of Shaoyao-Gancao-Tang by the tri-level infrared macro-fingerprint spectroscopy and the whole analysis method. Spectrochimica Acta Part A, Mol. Biomol. Spectrosc. 2018, 192, 93100.

    • Search Google Scholar
    • Export Citation
  • 14.

    Wang, M.; Yao, C.; Li, J.; Wei, X.; Xu, M.; Huang, Y.; Mei, Q.; Guo, D.-A. Software assisted multi-tiered mass spectrometry identification of compounds in traditional Chinese medicine: Dalbergia odorifera as an Example. Molecules (Basel, Switzerland) 2022, 27(7), 2333.

    • Search Google Scholar
    • Export Citation
  • 15.

    Lei, H.; Zhang, Y.; Zu, X.; Ye, J.; Liang, Y.; Cheng, T.; Zhang, W. Comprehensive profiling of the chemical components and potential markers in raw and processed Cistanche tubulosa by combining ultra-high-performance liquid chromatography coupled with tandem mass spectrometry and MS/MS-based molecular networking. Anal. Bioanal. Chem. 2021, 413(1), 129139.

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    • Export Citation
  • 16.

    Zhang, W.; Saif, M.-W.; Dutschman, G.-E.; Li, X.; Lam, W.; Bussom, S.; Jiang, Z.; Ye, M.; Chu, E.; Cheng, Y.-C. Identification of chemicals and their metabolites from PHY906, a Chinese medicine formulation, in the plasma of a patient treated with irinotecan and PHY906 using liquid chromatography/tandem mass spectrometry (LC/MS/MS). J. Chromatogr. A 2010, 1217(37), 57855793.

    • Search Google Scholar
    • Export Citation
  • 17.

    Yu, S.; Li, J.; Guo, L.; Di, C.; Qin, X.; Li, Z. Integrated liquid chromatography-mass spectrometry and nuclear magnetic resonance spectra for the comprehensive characterization of various components in the Shuxuening injection. J. Chromatogr. A 2019, 1599, 125135.

    • Search Google Scholar
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    Ma, Q.; Ma, C.; Wu, F.; Xiong, Y.-K.; Feng, Y.; Liang, S. Preparation and structural determination of four metabolites of senkyunolide I in rats using ultra performance liquid chromatography/quadrupole-time-of-flight tandem mass and nuclear magnetic resonance spectra. BMC Complement. Altern. Med. 2016, 16(1), 504.

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    Wang, Z.; Wang, Z.; Jiang, M.; Yang, J.; Meng, Q.; Guan, J.; Xu, M.; Chai, X. Qualitative and quantitative evaluation of chemical constituents from Shuanghuanglian Injection using nuclear magnetic resonance spectroscopy. J. Anal. Methods Chem. 2022, 2022, 7763207.

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    Ming, L.; Huang, H.; Jiang, Y.; Cheng, G.; Zhang, D.; Li, Z. Quickly identifying high-risk variables of ultrasonic extraction oil from multi-dimensional risk variable patterns and a comparative evaluation of different extraction methods on the quality of Forsythia suspensa seed oil. Molecules (Basel, Switzerland) 2019, 24(19), 3445.

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    Ding, W.; Yang, T.; Liu, F.; Tian, S. Effect of different growth stages of Ziziphora clinopodioides Lam. on its chemical composition. Pharmacognosy Mag. 2014, 10(Suppl 1), S1-5.

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    Dai, W.; Hu, L.; Ji, L.; Li, J.; Bi, K.; Li, Q. A comprehensive method for quality evaluation of Houttuyniae Herba by a single standard to determine multi-components, fingerprint and HPTLC method. Anal. Sci. Int. J. Jpn. Soc. Anal. Chem. 2015, 31(6), 535541.

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    Ji, D.; Li, Q.; Yang, H.; Fan, Y.; Wang, T.; Chen, Y. Determination of five coumarins in Angelicae Pubescentis Radix from different origins by HPTLC-scanning. J. Anal. Methods Chem. 2022, 2022, 3415938.

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    Ye, Z.; Dai, J.-R.; Zhang, C.-G.; Lu, Y.; Wu, L.-L.; Gong, A.G.W.; Xu, H.; Tsim, K.W.K.; Wang, Z.-T. Chemical differentiation of Dendrobium officinale and Dendrobium devonianum by using HPLC fingerprints, HPLC-ESI-MS, and HPTLC analyses. Evidence-Based Complement. Altern. Med. eCAM 2017, 2017, 8647212.

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    Honglian, Z.; Li, L.; Huiyu, W.; Jikai, S.; Hongling, L.; Wei, D.; Meijuan, Z. Rapid detection of the metformin illegally added of in TCM and health food by TLC-IR. Pakistan J. Pharm. Sci. 2020, 33(3), 11151119.

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    Lyu, M.; Liu, Y.; Qiu, Y.; Yang, S.; Yuan, H.; Wang, W. Differentiation between Chaenomelis Fructus and its common adulterant, Guangpi Mugua. J. AOAC Int. 2021, 104(6), 16521660.

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Senior editors

Editor(s)-in-Chief: Kowalska, Teresa (1946-2023)

Editor(s)-in-Chief: Sajewicz, Mieczyslaw

Editors(s)

  • Danica Agbaba (University of Belgrade, Belgrade, Serbia)
  • Łukasz Komsta (Medical University of Lublin, Lublin, Poland)
  • Ivana Stanimirova-Daszykowska (University of Silesia, Katowice, Poland)
  • Monika Waksmundzka-Hajnos (Medical University of Lublin, Lublin, Poland)

Editorial Board

  • R. Bhushan (The Indian Institute of Technology, Roorkee, India)
  • J. Bojarski (Jagiellonian University, Kraków, Poland)
  • B. Chankvetadze (State University of Tbilisi, Tbilisi, Georgia)
  • M. Daszykowski (University of Silesia, Katowice, Poland)
  • T.H. Dzido (Medical University of Lublin, Lublin, Poland)
  • A. Felinger (University of Pécs, Pécs, Hungary)
  • K. Glowniak (Medical University of Lublin, Lublin, Poland)
  • B. Glód (Siedlce University of Natural Sciences and Humanities, Siedlce, Poland)
  • A. Gumieniczek (Medical University of Lublin, Lublin, Poland)
  • U. Hubicka (Jagiellonian University, Kraków, Poland)
  • K. Kaczmarski (Rzeszow University of Technology, Rzeszów, Poland)
  • H. Kalász (Semmelweis University, Budapest, Hungary)
  • K. Karljiković Rajić (University of Belgrade, Belgrade, Serbia)
  • I. Klebovich (Semmelweis University, Budapest, Hungary)
  • A. Koch (Private Pharmacy, Hamburg, Germany)
  • P. Kus (Univerity of Silesia, Katowice, Poland)
  • D. Mangelings (Free University of Brussels, Brussels, Belgium)
  • E. Mincsovics (Corvinus University of Budapest, Budapest, Hungary)
  • Á. M. Móricz (Centre for Agricultural Research, Budapest, Hungary)
  • G. Morlock (Giessen University, Giessen, Germany)
  • A. Petruczynik (Medical University of Lublin, Lublin, Poland)
  • R. Skibiński (Medical University of Lublin, Lublin, Poland)
  • B. Spangenberg (Offenburg University of Applied Sciences, Germany)
  • T. Tuzimski (Medical University of Lublin, Lublin, Poland)
  • Y. Vander Heyden (Free University of Brussels, Brussels, Belgium)
  • A. Voelkel (Poznań University of Technology, Poznań, Poland)
  • B. Walczak (University of Silesia, Katowice, Poland)
  • W. Wasiak (Adam Mickiewicz University, Poznań, Poland)
  • I.G. Zenkevich (St. Petersburg State University, St. Petersburg, Russian Federation)

 

KOWALSKA, TERESA (1946-2023)
E-mail: kowalska@us.edu.pl

SAJEWICZ, MIECZYSLAW
E-mail:msajewic@us.edu.pl

Indexing and Abstracting Services:

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2022  
Web of Science  
Total Cites
WoS
647
Journal Impact Factor 1.9
Rank by Impact Factor

Chemistry, Analytical (Q3)

Impact Factor
without
Journal Self Cites
1.9
5 Year
Impact Factor
1.4
Journal Citation Indicator 0.41
Rank by Journal Citation Indicator

Chemistry, Analytical (Q3)

Scimago  
Scimago
H-index
29
Scimago
Journal Rank
0.28
Scimago Quartile Score

Chemistry (miscellaneous) (Q3)

Scopus  
Scopus
Cite Score
3.1
Scopus
CIte Score Rank
General Chemistry 211/407 (48th PCTL)
Scopus
SNIP
0.549

2021  
Web of Science  
Total Cites
WoS
652
Journal Impact Factor 2,011
Rank by Impact Factor Chemistry, Analytical 66/87
Impact Factor
without
Journal Self Cites
1,789
5 Year
Impact Factor
1,350
Journal Citation Indicator 0,40
Rank by Journal Citation Indicator Chemistry, Analytical 72/99
Scimago  
Scimago
H-index
29
Scimago
Journal Rank
0,27
Scimago Quartile Score Chemistry (miscellaneous) (Q3)
Scopus  
Scopus
Cite Score
2,8
Scopus
CIte Score Rank
General Chemistry 210/409 (Q3)
Scopus
SNIP
0,586

2020
 
Total Cites
650
WoS
Journal
Impact Factor
1,639
Rank by
Chemistry, Analytical 71/83 (Q4)
Impact Factor
 
Impact Factor
1,412
without
Journal Self Cites
5 Year
1,301
Impact Factor
Journal
0,34
Citation Indicator
 
Rank by Journal
Chemistry, Analytical 75/93 (Q4)
Citation Indicator
 
Citable
45
Items
Total
43
Articles
Total
2
Reviews
Scimago
28
H-index
Scimago
0,316
Journal Rank
Scimago
Chemistry (miscellaneous) Q3
Quartile Score
 
Scopus
393/181=2,2
Scite Score
 
Scopus
General Chemistry 215/398 (Q3)
Scite Score Rank
 
Scopus
0,560
SNIP
 
Days from
58
submission
 
to acceptance
 
Days from
68
acceptance
 
to publication
 
Acceptance
51%
Rate

2019  
Total Cites
WoS
495
Impact Factor 1,418
Impact Factor
without
Journal Self Cites
1,374
5 Year
Impact Factor
0,936
Immediacy
Index
0,460
Citable
Items
50
Total
Articles
50
Total
Reviews
0
Cited
Half-Life
6,2
Citing
Half-Life
8,3
Eigenfactor
Score
0,00048
Article Influence
Score
0,164
% Articles
in
Citable Items
100,00
Normalized
Eigenfactor
0,05895
Average
IF
Percentile
20,349
Scimago
H-index
26
Scimago
Journal Rank
0,255
Scopus
Scite Score
226/167=1,4
Scopus
Scite Score Rank
Chemistry (miscellaneous) 240/398 (Q3)
Scopus
SNIP
0,494
Acceptance
Rate
41%

 

Acta Chromatographica
Publication Model Online only
Gold Open Access
Submission Fee none
Article Processing Charge 400 EUR/article
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access
Purchase per Title  

Acta Chromatographica
Language English
Size A4
Year of
Foundation
1988
Volumes
per Year
1
Issues
per Year
4
Founder Institute of Chemistry, University of Silesia
Founder's
Address
PL-40-007 Katowice, Poland, Bankowa 12
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2083-5736 (Online)

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