Metabolite identification strategy of non-targeted metabolomics and its application for the identification of components in Chinese multicomponent medicine Abelmoschus manihot L.

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Metabolite identification strategy of non-targeted metabolomics and its application for the identification of components in Chinese multicomponent medicine Abelmoschus Manihot L. Jian-ming Guo∗, Yu-wei Lu, Er-xin Shang, Ting Li, Yang Liu, Jin-ao Duan∗∗, Da-wei Qian, Yu-ping Tang

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Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, and National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China

a r t i c l e

i n f o

Article history: Received 29 September 2014 Revised 26 January 2015 Accepted 2 February 2015 Available online xxx Keywords: Abelmoschus Manihot L. Automated data mining Pattern recognition analysis

Identification of multicomponent in traditional Chinese medicine (TCM) is complex and time-consuming. The inspection of the full-scan mass chromatograms was usually performed manually, which is labor-intensive. It is difficult to distinguish low response signals from complex chemical background. Furthermore, this process is typically based on earlier knowledge of the chemical composition of TCM, and those molecules that have not been characterized earlier were thus ignored. In this paper, a strategy using UPLC–MS combined with pattern recognition analysis was developed to simplify and quicken the identification of multicomponent in Abelmoschus manihot (Linneus) Medik. First, complex signals obtained by UPLC–MS were processed using automated data mining algorithm and further processed with multivariate chemometric methods. Multicomponent in Abelmoschus Manihot L. can be clearly displayed in S- and VIP-plots. Using this method, 320 peaks which present in Abelmoschus Manihot L. were detected. In the next step, accurate mass spectra of the characteristic markers acquired by QTOF MS were used to estimate their elemental formulae and enable structure identification. By searching in METLIN database, 41 components were tentatively identified in Abelmoschus Manihot L. Our results showed that UPLC–MS based pattern recognition analysis approach can be used to quickly identify TCM multicomponent and for standardization of herbal preparations. © 2015 Published by Elsevier GmbH.

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Introduction

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Identification of components in traditional Chinese medicine (TCM) is complex and time-consuming due to the complicity of TCM and large number of detected ions. Several strategies have been developed for the analysis of multicomponent in TCM (Duan et al. 2014; Li et al. 2011; Shi et al. 2014). The targeted quantitative analysis is typically based on earlier knowledge of the chemical composition. The chemical molecules that have not been characterized in plant species, natural medicine or food product earlier might be ignored. In most targeted quantitative analysis, the inspection of the full-scan mass chromatograms was usually performed manually to identify the components of TCM in total ion chromatography (TIC) obtained from LC–MS. Manual inspection of the detected ions is labor-intensive, and is difficult to distinguish low response signals from the complex

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a b s t r a c t

∗

Corresponding author. Tel.: +86 25 85811917; fax: +86 25 85811917. Corresponding author. Tel.: +86 25 85811116; fax: +86 25 85811116. E-mail addresses: [email protected], [email protected] (J.-m. Guo), [email protected] (J.-a. Duan). ∗∗

chemical background in full-scan mass chromatograms (Gao et al. 2012; Yan et al. 2010). Non-targeted LC–MS profiling strategy for plant/herb aims to monitor global changes in plant/herb with a non-targeted manner (Ogura et al. 2013). Using non-targeted LC–MS profiling strategy such as non-targeted metabolomics, changes of both known and unknown or unexpected metabolites can be visualized. MarkerLynx, a spectral and chromatographic searching program for post-acquisition data processing, can be applied in non-targeted metabolomics study. MarkerLynx is capable of automatically processing LC/MS data sets to search expected (targeted) and unexpected (non-targeted) metabolites by comparing the chromatogram of the analyte with that of the control. MarkerLynx has been used for screening and identifying mycotoxins in herbal medicine, evaluating chemical consistency between traditional and dispensing granule decoctions and metabolite profiling of apple volatile content, etc. (Aprea et al. 2011; Fang et al. 2013; Mao et al. 2014; Naz et al. 2014; Shang et al. 2012). MarkerLynx and pattern recognition approach has also been applied for the identification of TCM metabolites in vivo (Guo et al. 2013; Tan et al. 2014). In the current study, non-targeted metabolomics strategy and MarkerLynx software were used for multicomponent identification in

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Please cite this article as: J.-m. Guo et al., Metabolite identification strategy of non-targeted metabolomics and its application for the identification of components in Chinese multicomponent medicine Abelmoschus Manihot L., Phytomedicine (2015), http://dx.doi.org/10.1016/j.phymed.2015.02.002

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TCM. UPLC–TOF-MS combined with pattern recognition analysis were developed to simplify and quicken the identification of the metabolites of TCM. Advanced chemometric/statistical techniques (PCA and PLS-DA) were used to explore data and extract useful information. Here, we demonstrate the application and proved the repeatability of this strategy using a Chinese medicine Abelmoschus Manihot L. as a model sample. Abelmoschus Manihot (Linneus) Medik. (checked with www.theplantlist.org http://www.theplantlist.org/tpl1.1/record/ kew-2609589) has been reported for its pharmacological activities including protecting liver injury (Ai et al. 2013), improving kidney function (Tu et al. 2013), treating diabetes (Zhou et al. 2012). Abelmoschus Manihot L. is now served as the ingredient of ’HuangKui Capsule’ (commercial name), which is one of the most commonly used patent Chinese medicines in treating chronic glomerulonephritis and diabetic nephropathy. Recently, a randomized controlled clinical trial was organized to assess the efficacy and safety of Abelmoschus Manihot L. in patients with primary glomerular disease, new data from this trial suggest that this herb is more effective than the angiotensin-receptor blocker losartan in reducing proteinuria in patients with primary glomerular disease (Zhang et al. 2014). The results showed that Abelmoschus Manihot L. is a promising TCM for patients with chronic kidney disease (Carney 2014; Zhang et al. 2014). Several literatures describe the growing interest on studying the total flavone of Abelmoschus Manihot L. and its monomer (Lai et al. 2006). The identification of metabolites in Abelmoschus Manihot L. such as flavonoids has been performed previously (Guo et al. 2010, 2011a; Xue et al. 2011a, 2011b). More than 30 components have been isolated and characterized by high performance chromatography– mass spectrometry (HPLC–MS) method (Lai et al. 2009). The inspection of the mass data was performed manually to identify the components of Abelmoschus Manihot L. in TIC, which is labor-intensive, and the relatively low responding ions from the complex chemical background in full-scan mass chromatograms is difficult to be detected and identified by HPLC–MS. In this paper, a strategy using UPLC–MS combined with pattern recognition analysis approach was developed to simplify and quicken the identification of multicomponent in Abelmoschus Manihot L. This strategy will contribute to the standardization of herbal preparations.

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Experimental

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Chemicals and reagents

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HPLC grade acetonitrile was purchased from TEDIA Company Inc. (Fairfield, USA). Ultra-pure water was purified by an EPED super purification system (Nanjing, China). Formic acid was obtained from Merck KGaA (Darmstadt, Germany). Other reagents and chemicals were of analytical grade.

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Sample preparation Abelmoschus Manihot L. flowers were collected from Jiangyan County of Jiangsu Province, China. According to the processing procedure of HuangKui Capsule, a total of 50 g of mixed powders of Abelmoschus Manihot L. flowers was immersed in 800 mL 75% ethanol for 1 h. The mixture was refluxed for 1 h at 90 °C and filtered using analytical filter paper. The extracts were evaporated by rotary evaporation under vacuum at 60 °C. In order to obtain 480 g of dry Abelmoschus manihot ethanol extract, 2000 g dry Abelmoschus manihot flowers are required. Therefore, the “drug-extract” ratio (DER) of Abelmoschus manihot ethanol extract is 4.0–4.5. The main active components of Abelmoschus Manihot L. flowers were hyperoside, isoquercitrin, hibifolin, quercetin-3 -O-glycoside,

quercetin, myricetin and rutin. HPLC fingerprint of Abelmoschus Manihot L. flower extract can be referred to our previously published paper (Guo et al. 2011a). In our previous report, we have analyzed the content of the main marker compounds such as rutin, hyperoside, isoquercitrin, hibifolin, myricetin, quercetin-3 -glucose and quercetin in 13 batches of Abelmoschus manihot L. flowers (Lu et al. 2013). The result showed that the contents of the above main marker range from 0.273 to 0.89 mg/g (rutin), 3.34 to 10.3 mg/g (hyperoside), 1.59 to 4.76 mg/g (isoquercitrin), 10.1 to 25.3 mg/g (hibifolin), 0.403 to 2.32 mg/g (myricetin), 1.26 to 4.33 mg/g (quercetin-3 -glucose), and 0.189 to 0.686 mg/g (quercetin), respectively. This herbal preparation is in accordance to EMA guidelines (http://www.ema.europa.eu/docs/en_GB/document_library/Scientific _guideline/2009/09/WC500003272.pdf).

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Ultra performance liquid chromatography–mass spectrometer (UPLC–MS)

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Chromatography was performed on an ACQUITY UPLC system (Waters Corp., Milford, MA/USA). The separation was carried out on an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm i.d., 1.8 μm; Waters Corp., Milford, MA, USA). The analysis was achieved with gradient elution using (A) acetonitrile and (B) water (containing 0.05% formic acid) as the mobile phase. 5 μL Abelmoschus Manihot L. methanol solution or methanol (as solvent group) was injected to UPLC for five times. The other experimental details can be referred to our previously published paper (Guo et al. 2013). An MSE (massE , E represents collision energy), experiment was carried out as previously reported. MSE is a technique which enables almost simultaneous acquisition of both LC/MS and fragmentation data from a single experiment.

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Data processing and pattern recognition analysis (PCA)

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The mass chromatographic data were deconvoluted using MarkerLynx software (Waters), and a data matrix was generated for PCA. The ion intensities of each detected peak were normalized against the sum of the peak intensities within the spectra of that sample using MarkerLynx. Ions from different samples were considered to be the same ion if they had been demonstrated to have the same tR (tolerance of 0.01 min) and m/s value (tolerance of 0.01 Da). If a peak was not detected in a sample, the ion intensity would be documented as zero in the table. The resulting three-dimensional data comprising of a peak number (tR–m/s pair), sample name and ion intensity were going to be analyzed by supervised orthogonal partial least squared discriminant analysis (OPLS-DA) using the MarkerLynx software. In order to quickly separate and identify Abelmoschus Manihot L. multicomponent from the solvent, the integrated ions in solvent group and Abelmoschus Manihot L. group were analyzed using an orthogonal projection to latent structures (OPLS) model. OPLS analysis was conducted to represent the major latent variables in the data matrix and was described in a scores scatter plot after data were Pareto scaled. The structure of differentiate components was searched online in METLIN database. The simplified workflow illustrating of UPLC– ESI-Q-TOFMS combined with pattern recognition approach strategy for multicomponent identification in TCM was shown in Fig. 1.

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MarkerLynx XS processing settings

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Data files were processed with the MarkerLynx XS software package used as a platform to search for expected and unexpected metabolites (compounds) with accurate mass and fragment ions information. The data files from Abelmoschus Manihot L. and the solvent were labeled as ‘analyte’ and ‘control’, respectively, to extract the ions from

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Traditional Chinese Medicine (Abelmoschus Manihot)

UPLC-Q-TOFMS Alignment of retention time and mass data Markerlynx PLS-DA analysis Markers in S- and VIP-plot Components of Abelmoschus Manihot METLIN Metabolomics Database

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Characterization of TCM components Fig. 1. A simplified workflow illustrating of UPLC–ESI-Q-TOFMS combined with pattern recognition approach strategy for multicomponent identification in TCM.

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the data sets of Abelmoschus Manihot L. which were absent from that of the solvent.

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MS data imputed into MarkerLynx and analyzed using an orthogonal projection to latent structures (OPLS) model. Data sheets (xls.) were exported from MarkerLynx, the ions with the same retention time were combined and the m/z was designed to the larger m/z. Accurate mass spectra acquired by QTOF MS of the most characteristic markers were used to estimate their elemental formulae. By searching in METLIN database, the structure of most characteristic markers can be tentatively identified.

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Results and discussion

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Multivariate statistical analysis for Abelmoschus Manihot L. components identification

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Abelmoschus Manihot samples and control samples were analyzed by UPLC-Q-TOF-MS. Each sample was analyzed for five times. The constituents in Abelmoschus Manihot L. were detected using the UPLC– ESI-Q-TOF-MS system (Fig. 2); 12 peaks that have been identified in previous reports through manual inspection (Guo et al. 2011b) can be detected. However, some ions with lower abundance were embedded in the background ions, resulting to the compounds being missed during manual inspection. Moreover, the interference of other ions caused difficulties in locating the ions from the respective mass spectrum and lead to their omission. Therefore, without using data-mining and multivariate statistical analysis strategy, it would be difficult to identify low-level Abelmoschus Manihot L. components through visual-manual examination of Abelmoschus Manihot L. samples because of endogenous interference. MarkerLynx XS is a post-acquisition data processing software that can be applied to the full-scan data files individually or in batch format. By comparing the data files of the analyte (those sample containing metabolites, here the Abelmoschus Manihot L. sample) against the control (usually a blank sample, here the solvent sample), this

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software automates the detection, identification, and reporting of expected and unexpected ions peaks in a faster and more complete manner than manual inspection according to the processing set. As shown in Fig. 3, MarkerLynx can differentiate the components in analyte and control group, thereafter, the differentiated components can be identified as the metabolites in Abelmoschus Manihot L. Data processing was conducted using MarkerLynx software. Thousands of ions were generated and related results are shown (Fig. 3). Multiple pattern recognition methods were employed to display the differences between Abelmoschus Manihot L. group and solvent group. In the PCA scores, Abelmoschus Manihot L. group and control group cluster into two discrete groups, indicating that the constituents in Abelmoschus Manihot L. are different from solvent group. Supervised PLS-DA is a better approach to discriminate the constituents among different groups. As shown in Fig. 3, the S- and VIP-plots from the supervised PLS-DA can help discriminate the constituents in Abelmoschus Manihot L. In order to gain the details of different constituents, the UPLC–ESI-Q-TOFMS datasets from the control and Abelmoschus Manihot L. group were subjected to the PLS-DA. Markers that contribute to the group separation can be clearly displayed in the S- and VIP-plots. In the OPLS-DA scores plot (Fig. 3), Abelmoschus Manihot L. group and control group generated two clusters and were clearly separated in the component. OPLS loading S-plot (Fig. 3A) and VIP-plot (Fig. 3B) were then investigated. Multicomponent in Abelmoschus Manihot L. can be clearly displayed as the dots in the S- and VIP-plots. Ions of relatively high confidence variable with large changes between two groups were picked up for further structure elucidation. As seen from Fig. 3, most of the ions were clustered around the origin point. A few of ions scattered in the margin region of S-plot, these ions were those components originated from Abelmoschus Manihot L. As shown in Fig. 4, the trend plots of tR–m/z: 4.29–479.0810 in dataset clearly demonstrated those ions that only existed in Abelmoschus Manihot L. Thus, the ions of interest which were present only in Abelmoschus Manihot L. and absent in the solvent group were extracted easily at the top of the S- and VIP-plots. Based on the S- and VIP-plots from PLS-DA of the datasets, 320 interest ions from dataset were extracted as Abelmoschus Manihot L. constituents.

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Identification and structure illustration of the components of Abelmoschus Manihot L

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Differentiated ions from OPLS loading S-plot were further investigated. These ions were identified as the assumptive metabolites based on PCA trend plots and marked in the OPLS loadings S-plot. The filtering step was conducted using fold change (FC) analysis. The value of FC was calculated as the MF abundance ratios between each two group. The FC of 78 ions was above 10. The FC of 242 ions was between 1.0 and 10. Markers with FC value ࣙ1.0 and higher abundance compared with solvent were picked out only. Based on this principle, 320 makers were selected as the marker compound candidates. To avoid any false positive results, those makers were re-extracted from the raw data files of the samples (Supplemental Fig. 1). The structures of a total of 41 markers were searched through METLIN and tentatively proposed. According to the peak abundance variance in chromatogram (Fig. 4) and subsequent MS/MS spectra analysis in certain mass and retention time, we are able to tentatively identify their chemical structure. Utilizing the MSE technique, two distinct mass chromatograms were produced in the negative ion model from both the low and high CE data channels in a single LC injection. One at the low CE contained protonated molecular ion ([M − H]− ) information, which was used to analyze the profile of the constituents in Abelmoschus

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Fig. 3. The S- and VIP-plots from PLS-DA dataset of control group and Abelmoschus Manihot L. group. (A) S-plot and (B) VIP-value plot. Points in the upper right corner of (A) indicate increased ions responsible for variation among control and Abelmoschus Manihot L. group which are components derived from Abelmoschus Manihot L. (B) The point graph is the VIP-value plot, which represents the value of each ion. The farther away from the origin, the higher the VIP value of the ions was. These ions are only detected in Abelmoschus Manihot L. sample, as a result its high discriminating power. CoeffCS represents coefficient of regression.

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Fig. 4. The SIM chromatograms of myricetin-O-glucoside in control and Abelmoschus Manihot L. sample. Insets display the intensity of one of them in each sample and the possible structure searched through METLIN. X-axis represents different groups and samples, the data files from Abelmoschus Manihot L. and solvent were labeled as ‘analyte’ and ‘control’, (C-1–C-5 represent solvent control group 1–5 and HK-1–HK-5 represent Abelmoschus Manihot L. group.). Y-axis represents the variables of the selected ions.

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Variables colored by Sample Group (original)

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Manihot L. The other at high CE contained fragment ions information for structural characterization of the corresponding ion. Fig. 5A and B shows the MS spectrum of tR–m/z: 4.29–479.0810 at the low and high CE, respectively. Important ions were selected based on VIP (variable important in the projection) values. The VIP value indicates the contribution of each feature to the regression model. METLIN was chosen as the chemical database for compound identification because it contained more than 24,000 unique structures and 60,000 high resolution MS/MS spectra. The identification process of these metabolites is described by choosing m/z 521.0927 as an example.

Peak: m/z 521.0927, tR 4.81, 5.20 According to the trend plot of m/z 521.0927 generated by MarkerLynx (Fig. 6A), ion m/z 521.0927 presented in Abelmoschus Manihot L. group but was absent in the control group. By comparing the peak abundance distinction in the selected ion mode chromatograph (m/z 521.0927) of control group and Abelmoschus Manihot L. group, this peak was expected to be derived from Abelmoschus Manihot L. The ion with m/z 521.0946 was further searched in METLIN online database with 7 possible structures (Fig. 6B). Furthermore, as shown in MS/MS spectrum (Fig. 6C), daughter ion with m/z 317.028 was generated from [M − H]− ion m/z 521.0946 by losing one

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acetylgalactoside (−214), which indicate m/z 521 is a acetylgalactoside conjugation (Fig. 6C). Based on all the information above, we could tentatively recognize this peak as myricetin acetylgalactoside. To further confirm the structure, standard substance is needed as a comparison. By this approach, we have identified a set of components present in Abelmoschus Manihot L. When the reference compound was not available, MassFragment software was used for the confirmation of the proposed structure. MS/MS fragmentation of the metabolites was compared with those of candidate molecules found in METLIN databases and verified with earlier literature on similar compounds. This increased our confidence in the MS fragment analysis of the proposed structure which facilitated the confirmation of a proposed structure, thereby making data processing significantly easier. Among the compounds detected, some were present at low concentration levels and have not been characterized previously in Abelmoschus Manihot L. Compared with previous studies, the strategy used in this report could facilitate to detect more components, and the analysis process was accelerated with automated data processing. In previous manual peak assignment study, researchers need to have some knowledge about the mass spectrum. Moreover, the assignment of a structure to a given spectrum is time consuming and the result may vary from researcher to researcher. Non-targeted metabolomics aims to monitor global system changes in a nontargeted manner. It is the non-targeted approach that allows the visualization of changes of both known and unknown or unexpected metabolites, allowing the researcher to focus or target their research to a specific metabolite or group of metabolites. The examples highlighted in this paper have shown that LC–MS based metabolomics could be an advanced tool to help finding more components in TCM with regards to its capacity of processing large datasets, differentiating and classifying of sample groups. The main task of this paper is to provide a validated method suitable for assessment of standardized preparations from Abelmoschus manihot L. flowers, which is important to ensure batch to batch reproducible metabolomic profile and pharmacological activity. Therefore, we analyzed 3 batches of Abelmoschus manihot L. (Figs. 7 and 8). As shown in Fig. 7, 3 batches of Abelmoschus manihot L. show similar HPLC chromatographic profile, demonstrating the consistently of different batches of Abelmoschus manihot L. As shown in Fig. 8, 3 batches of Abelmoschus manihot L. can clearly separate from solvent with PCA analysis. This result proved the reproducibility of our strategy for the analysis of Abelmoschus manihot L. After analysis of 3 batches of Abelmoschus manihot L. using our strategy, 41 markers can be used for the standardization of this preparation. In our previous report, we have analyzed the content of the main marker compounds such as rutin, hyperoside, isoquercitrin, hibifolin, myricetin, quercetin-3 -glucose and quercetin in 13 batches of Abelmoschus manihot L. ethanol extract (Lu et al. 2013). The report showed that the main marker compounds exist in all the 13 batches of extracts, although the contents of the main marker compounds in Abelmoschus manihot L. ethanol extract range from different batches. Therefore, in Fig 8 of our present manuscript, it is understandable that Batch 1 is far away from the other two batches. And as 3 batches of Abelmoschus Manihot L. clearly separate from solvent with PCA analysis, which showed that our strategy is suitable for the identification of multicomponent in Abelmoschus manihot L.

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In this work, UPLC–MS combined with metabolomics and pattern recognition analysis approach were used to identify the components of Abelmoschus Manihot L. Pattern recognition analysis method was used to differentiate components between Abelmoschus Manihot L. and solvent group. The structure of differentiate components was searched in METLIN online database. Our results showed that UPLC–

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MS based-metabolomics approach can be used to quickly identify Abelmoschus Manihot L. components. Furthermore, this work demonstrates the potential application of combining the UPLC–MS approach with the metabolomics approach in identifying the components of TCM.

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There is no conflict of interest of any author.

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This work was supported by Jiangsu Provincial TCM Administration Bureau Project (No. LZ13019), “Six Talent Peaks Program” of Jiangsu Province of China (2013 YY-009), National Natural Science Foundation of China (No. 81473408), Jiangsu 333 Project for the Cultivation of High-level Innovative Talents, Key Research Project in Basic Science of Jiangsu College and University (No. 13KJA360002), the Practical and Innovative Training Program of College Students in Jiangsu Province (201310315034Z), and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. We are also pleased to thank Waters China for technical support.

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Supplementary Materials

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Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.phymed.2015.02.002.

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References

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