holistic fragment ions analysis coupled with chemometrics

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Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics Zhenzhen Xue a, Changjiangsheng Lai b, Liping Kang b, Akira Kotani c, Hideki Hakamata c, Zhixian Jing b, Hua Li a, Weihao Wang a, Bin Yang a,∗ a

Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, PR China National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, State Key Laboratory Breeding Base of Dao-di Herbs, Beijng 100700, PR China c School of Pharmacy, Tokyo University of Pharmacy and Life Sciences, Tokyo 192-0392, Japan b

a r t i c l e

i n f o

Article history: Received 1 June 2019 Revised 18 September 2019 Accepted 30 September 2019 Available online xxx Keywords: Phenylethanoid glycoside Isomer Diagnostic ion Neutral loss Untargeted fingerprint analysis Statistical analysis

a b s t r a c t Isomers derived from natural products are promising candidates for drug discovery. However, characterization of isomers by mass spectrometry, especially stereoisomers and positional isomers, remains a large challenge due to insufficient reference standards and isomers’ highly similar fragmentation pathways or nondistinctive ion abundances. Herein, this report presents the first proposal of a method, combining multiple diagnostic ion/neutral loss (DINL) postanalysis and especially untargeted fingerprint analysis of all fragment ions (FAAFI) by means of a home-made program and chemometrics, to profile chemical components and recognize their isomers derived from medicinal plants. As a proof-of-concept, the chemical profiling of phenylethanoid glycosides (PhGs) and their isomers, which showed remarkable neuroprotective, anti-inflammatory and immunomodulatory effects, was performed. Using DINL to extract PhGs and FAAFI to distinguish their stereoisomers and positional isomers, as many as 87 PhGs, including 14 isomers, were tentatively identified from Magnolia officinalis; in addition, 17 PhGs were unambiguously identified by comparing the retention time and MS/MS data with those of reference compounds. Under the theory of Big Data analysis, the untargeted fingerprint analysis concerning unbiased ions was sufficient to contribute to discrimination of isomers even without evident distinction occurred for main ions. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Natural products serve as the indispensable material base for medicinal plants to exert their extraordinary performance in clinic. Sustainable efforts have been devoted to characterize natural products utilizing the powerful tools of combinations of different types of mass spectrometry (MS) with chromatography [1–3]. However, it remains a large challenge to differentiate and characterize isomers by MS, both of stereoisomers and positional isomers, due to their extremely similar fragmentation pathways or nondistinctive ion abundances. Current methods to discriminate isomers are mainly based on direct analysis of such parameters as several characteristic ions [4,5], ion-mobility derived collision cross–section determination [6], chemical derivatization [7], retention time prediction by quantitative structure-retention relationships [8,9], ion abundance ∗

Corresponding author. E-mail address: [email protected] (B. Yang).

[10,11], and peak area-collision energy trajectories [12]. Most of the methods usually suffered from the following defects: (1) investigators needed to expertly master the fragmentation behaviors when analyzing diagnostic ions or the multiple monitoring reaction ion transition; (2) limited fragment ions of interest might lead to biased analysis; (3) to produce unbiased mass spectrometric responses, extensive work needed to be devoted to obtaining the optimal condition for each analyte in numerous runs. As is wellknown, one compound can not only produce main ions with high intensity but other minor ions with low intensity. Is it a preferable means for isomers characterization to implement analysis focused on all fragment ions which could reflect the holistic information of one compound rather than the characterized product ions? The stem barks of Magnolia officinalis Rehd. et Wils. and M. officinalis Rehd. et Wils. var. biloba Rehd. et Wils., known as Houpo in Chinese, are common traditional Chinese medicines (TCMs) for regulating Qi, and approximately 40 0 0 prescriptions containing Houpo have been recorded in the Prescription Dictionary of TCM [13]. Modern pharmacological studies have indicated that Houpo

https://doi.org/10.1016/j.chroma.2019.460583 0021-9673/© 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

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has anti-spasmodic [14], anti-cancer [15] and antidiabetic activities [16]. Phytochemical evaluations have shown that phenylethanoid glycosides (PhGs) with structural diversity were found in M. officinalis var. biloba [17], M. officinalis [18], and M. obovate (another origin of MOC in Japan) [19], along with lignans, phenolic glycosides, alkaloids and essential oils. PhGs are a class of water-soluble compounds widely distributed in more than 100 species of medicinal plants [20]. To date, approximately 500 PhGs have been isolated and uncovered diversities of biological properties [20–22]. PhGs are usually characterized by a phenethyl alcohol (C6 –C2 ) moiety attached to a β -glucopyranose/β -allopyranose (core sugar) via a glycosidic bond, and the core sugars are often abundantly bound to substituents, such as aromatic acids and various saccharides, through ester or glycosidic linkages, respectively. The stereoisomerism of core sugars and the positional isomerism of different types of substituents gave rise to multitudinous isomers of PhGs, providing us a favorable specimen to study isomers’ characterization. Herein, in this work, PhGs and their isomers from M. officinalis work as a case study. Ultra-performance liquid chromatography coupled with quadrupole time-of flight MS (UPLC-QTOF/MS) was applied to obtain holistic profiles of metabolites from M. officinalis, and a new method combining multiple diagnostic ion/neutral loss (DINL) postanalysis and isomer recognition using untargeted fingerprint analysis of all fragment ions (FAAFI) by means of a homemade program and chemometrics is proposed to characterize PhGs and their isomers. 2. Experimental 2.1. Chemicals and reagents Authentic standard compounds echinacoside, poliumoside and salidroside were purchased from Shanghai tongtian biotechnology Co., Ltd (Shanghai, China); plantamajoside, forsythoside B, forsythoside A, acteoside and isoacteoside were purchased from Chengdu pusi biotechnology Co., Ltd (Shanghai, China); their purities were above 98%. Reference compounds magnoloside N, magnoloside B, magnoloside H, magnoloside G, magnoloside F, magnoloside A, magnoloside L, magnoloside I, magnoloside K, magnoloside J, magnoloside O, magnoloside P, magnoloside M, magnoloside E, magnoloside D, 2-(3,4-dihydroxyphenyl) ethanol 1-O-[4-O-caffeoyl-2-O-α -L-rhamnopyranosyl-3-O-α -Lrhamnopyranosyl-6-O-β -D -glucopyranosyl]-β -D -glucopyranoside were isolated from the stem barks of M. officinalis by ourselves, their purity of each reference compound was determined to be more than 90% by normalization of the peak areas detected by LC-UV [18]. All the standards’ structures are given in Fig. 1. Acetonitrile and methanol of LC-MS grade were obtained from Merck (Darmstadt, Germany). LC-MS grade formic acid was obtained from Fisher Scientific (Belgium). Ultra-pure water (18.2 M cm−1 ) was prepared by a Thermo Scientific Barnstead Gen Pure UV/UF water purifier system (Germany). 2.2. Plant materials and sample preparation The raw samples of stem bark, flower and leaf of M. officinalis were collected from Enshi. All of the voucher samples were authenticated by the authors and deposited in the Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences (Beijing, China). The samples were powdered to a homogeneous size and sieved through a No. 50 mesh. The accurately weighed dried powders (0.1 g) were dissolved in 1.5 mL of 70% methanol and ultrasonically extracted for 30 min at 35 °C and 100 KHz to prepare the sample

solutions. After being centrifuged at 20,630 g for 10 min, the supernatants of the sample solutions were subsequently filtered through a 0.2 μm GHP filter (PALL, USA) and stored at 4 °C for further qualitative study. 2.3. Metabolites profiling and MS/MS data collection by UPLC/QTOF-MS A UPLC reversed C18 analytical column (2.1 mm × 100 mm, 1.8 μm, ACQUITY UPLC®T3, Waters, USA) coupled with a C18 precolumn (2.1 mm × 10 mm, 1.8 μm, VanGuardTM T3, Waters, USA) was adopted and a temperature of 40 °C was applied. The mobile phases were 0.1% (v/v) formic acid aqueous solution (A) and 0.1% (v/v) formic acid acetonitrile (B) with the following gradient: 5– 20% B (0–12 min), 20–40% B (12–13 min), 40–60% B (13–15 min), 60–70% B (15–18 min), 70–90% B (18–23 min). The equilibration time and the flow rate were set at 4 min and 0.5 mL min−1 , respectively. The injection volume of the sample was 1 μL. The temperature of the autosampler chamber was set at 4 °C. Metabolites profiling was performed on a Waters Xevo G2-S QTOF/MS (Waters Micromass, Manchester, UK) operated in negative electrospray ionization mode. The QTOF/MS parameters were set as follows: mass range, 10 0–120 0 Da; scan time, 0.2 s; capillary voltage, 2.0 kV; cone voltage, 40 V; source temperature, 100 °C; desolvation gas temperature, 450 °C; cone gas flow rate, 50 L h−1 ; and desolvation flow rate, 900 L h−1 . Collision energies were set at a low energy of 6 eV for the precursor ions and an optimal high energy ramp of 35 to 60 eV for fragmentation information. The molecular masses of the precursor ion and product ions were accurately determined with leucine-enkephalin (m/z 554.2615 in negative electrospray ionization mode) at a concentration of 200 pg L−1 and an infusion flow rate of 20 μL min−1 . Data acquisition was controlled by MassLynx V4.1 software (Waters Corporation, Milford, USA). For metabolites characterization by UNIFITM , the MSE data were acquired in Continnum format. MS/MS data were also obtained on the above system using the same chromatographic conditions and only the quasi-molecular ion of every reference compound was chosen to produce pure product ion. The MS/MS chromatograms of the 15 PhGs with allose as the core sugar (1–15A) were obtained at 5–6 min for 1A, 6–7 min for 2–3A, 7–8 min for 4–7A, 7.5–9 min for 8–10A, 9–10 min for 11–12A, and 10–12 min for 13–15A. For the 9 PhGs with glucose as the core sugar (1–9G), chromatograms were obtained at 2.5–4 min for 1G, 7–9 min for 2–3G, 9–10.2 min for 4–5G, 9.8–11.3 min for 6–8G, and 11–14 min for 9G. 2.4. Data mining The whole process consisted of two parts as shown in Fig. 2. The first part was focused on targeted analysis, that is to extract the specific compounds of PhGs from a full scan profile using the diagnostic ions and neutral loss; and the second part was regarding the untargeted analysis, that is to analyze all fragment ions using a home-made program and statistical analysis to construct isomer recognition models, which were applied to distinguish the stereoisomerism of the core sugar and the positional isomerism of the substituents. In the first part, data were processed through extract ion chromatograms (EICs) manually and by UNIFI 1.8 (Waters, Milford, USA) after the fragmentation rules and DINLs of PhGs were concluded: (1) DINLs of the five subtypes of PhGs were picked from the MS/MS data of 24 reference compounds; (2) These ions were extracted from a full scan mass spectrum of the sample manually to efficiently identify the substructures for each cycle. To identify the coeluting peaks and reduce the false positive results, two filters of both “common fragment” and “common neutral loss” were simultaneously used to extract PhGs by UNIFI 1.8

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

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Fig. 1. Structures of reference compounds. Magnoloside N (1A), magnoloside B (2A), magnoloside H (3A), magnoloside G (4A), magnoloside F (5A), magnoloside A (6A), magnoloside L (7A), magnoloside I (8A), magnoloside K (9A), magnoloside J (10A), magnoloside O (11A), magnoloside P (12A), magnoloside M (13A), magnoloside E (14A), magnoloside D (15A), salidroside (1G), 2-(3,4-dihydroxyphenyl) ethanol 1-O-[4-O-caffeoyl-2-O-α -L-rhamnopyranosyl-3-O-α -L-rhamnopyranosyl-6-O-β -D-glucopyranosyl]-β D-glucopyranoside (2G), echinacoside (3G), plantamajoside (4G), forsythoside B (5G), forsythoside A (6G), acteoside (7G), poliumoside (8G), isoacteoside (9G).

(Waters, Milford, USA); (3) Corresponding profiles for each peak to the 5 EICs and the preliminary output characterization results of UNIFI were recorded; (4) Substructures were recognized by comparing the characteristic fragment ions with those of the reference compounds; and (5) Structures were confirmed by high-resolution MSE analysis. In the second part, data were analyzed by statistical means after the data set was obtained using a home-made program: (1) Product ions’ relative intensities of MS/MS and secondorder MSE spectrum were exported for the reference compounds and PhGs discovered from the samples, respectively; (2) The values of m/z of characteristic ions for each compound were adjusted according to that of magnoloside B (the compound with the substituted moieties of two sugars and one aromatic acid) and then all the product ions were produced to a union dataset using a homemade program performed by Python 3.7; (3) The recognition models for stereoisomerism of the core sugar and the positional isomerism of the substituents were established, respectively, by PLSDA using reference standards data, and the most discriminant ions were picked according to their VIP values, where usually the first five or ten ions were regarded as the most discriminant variables. The model quality was evaluated by R2 X, R2 Y, which respectively represented the fraction of the variance of the X matrix and the Y

matrix, and Q2 defined as the proportion of variance in the data predicted by the model; and (4) PCA was used to recognize the stereoisomer and positional isomers of compounds from samples. Isomers whose centroid format data were exported by UNIFI were characterized. All of the variables were Log-transformed prior to PLS-DA and PCA. PLS-DA and PCA were performed using SIMCA-P software (v11.5, Umetrics AB, Umeå, Sweden). 3. Results and discussion 3.1. Optimization of UPLC-QTOF/MS conditions The main compounds, including PhGs and phenolic glycosides, lignans, and alkaloids, were found in M. officinalis. While, more attention was paid to PhGs in the present study. Although the stem bark of M. officinalis exhibited only several major peaks of PhGs in UPLC-QTOF/MS analysis, a number of minor compounds could be observed in the enlarged chromatogram (Fig. 3B). Chromatograms of PhGs from the flowers and leaves of M. officinalis are also shown in Figs. S1, S2. The 70% methanol extract was analyzed by UPLC-QTOF/MS in an untargeted manner. The compounds were detected in the (−)ESI mode, where predominant [M − H]− or [M+HCOO]− ions could

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

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Fig. 2. Summary diagram for identification of PhGs and their isomers by UPLC-QTOF/MS. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

be obtained with high sensitivity. Meanwhile, a high energy ramp of 35 to 60 eV was optimized to obtain abundant fragment ions. To date, few studies have tried to comprehensively analyze PhGs in M. officinalis using LC-MS methods. Thus, to obtain satisfying chromatographic separation and high quality MSE data for PhGs, optimized gradient elution was adopted. For the sake of satisfying favorable baseline separation of polar compounds, the ACQUITY UPLC®T3 column was adopted and a temperature of 40 °C was applied to overcome the high pressures during the initial gradient of 95% water. It needs to mention that both of negative and positive modes were tested firstly, while the response to PhGs in positive mode was inferior duo to the interference of alkaloids distributed in M. officinalis. As shown in Fig. S3, the main peaks 1–4 and the most surrounding minor peaks in positive mode were all alkaloids, which made it difficult to extract MS data of PhGs for structural analysis. 3.2. Fragmentation rules and DINLs of PhG compounds PhGs have been most widely studied in families of Scrophulariaceae (e.g. Rehmannia glutinosa Libosch.), Plantaginaceae (e.g. Plantago asiatica L.), Orobanchaceae (e.g. Cistanche deserticola Y.C. Ma, C. tubulosa) and so forth [21], and recently found as the primary chemical classes in M. officinalis. Backed by the great convenience provided by DINLs scanning for compound retrieval and chemical

identification, reference compounds complemented by the cracking rules archived in the literature were simultaneously employed to summarize the DINLs for the PhG compounds. The MS/MS spectra of 24 reference compounds were analyzed using UPLC-QTOF/MS. According to the types of aromatic acids substituted on the core sugar, the 24 reference compounds could be classified into five types: caffeoyl (Caff), feruloyl (Feru), coumaroyl (Coum), vanilloyl (Vani) and syringoyl (Syr)-substituted, which could yield diagnostic fragment ions of m/z 161.0239, 175.0395, 145.0290, 465.1397 and 495.1503, respectively, meanwhile the first three subtypes could also generate characterized neutral loss of 162.0317 Da, 176.0473 Da and 146.0368 Da. The details for the category are shown in Fig. S4. As for saccharide, neutral losses of 162.0528 Da, 146.0579 Da, and 132.0423 Da were regarded as the indices of hexose group, e.g., glucose or allose, rhamnose group, and pentose group, e.g., apiose [23,24], respectively. In addition, pairs of ions of m/z 315.1080 and 135.0446 yielded from 3-hydroxy-salidroside and hydroxytyrosol, m/z 299.1131 and 137.0603 yielded from salidroside and tyrosol, and m/z 283.1182 and 121.0653 yielded from dehydroxy salidroside and dehydroxy tyrosol, indicated the existence of hydroxytyrosol, tyrosol, and dehydroxy tyrosol, respectively, as aglycone. The above results as well as the applicability of some fragmentation rules recorded in literature were corroborated each other. Representative structures and their MS spectra as well as fragmentation pathways are shown in Fig. 4, meanwhile, Fig. 5 lists the main aglycone, aromatic acid and saccharide types of PhGs in M. officinalis.

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

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Fig. 3. UPLC-QTOF/MS chromatograms of the stem bark of M. officinalis. (A) Base peak ion chromatogram (BPI); (B) Enlarged BPI. Compounds confirmed by reference standards are marked in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

3.3. Metabolites profiling of PhGs in M. officinalis

β -D-glucopyranosyl]-β -D-glucopyranoside and magnolosides P, O, M, E, D by comparison with reference standards, respectively.

3.3.1. Extraction and characterization of PhGs manually PhG compounds were firstly extracted from the second-order MSE spectral data obtained using M. officinalis samples after the high-resolution diagnostic fragment ions were set down. Their EICs are shown in Fig. S5, then the conservative substructures could be rapidly and efficiently recognized. High-resolution was critical for extraction of diagnostic ions to reduce false-positive signals. For the reference compounds, their MSE mass errors were less than 5 ppm or 5 mDa. Therefore, the extraction width for diagnostic ions was extended to ±10 ppm or ±10 mDa [25]. Structures of the recognized compounds were further identified by analyzing their high-resolution MSE spectra. After the moiety of aromatic acid was distinguished, characterization of sugar moieties and aglycones was performed based on their neutral losses and corresponding pairs of ions concluded in section of 3.2, respectively. As a result, through manual extraction of the diagnostic ions, the structures of a part of PhGs were preliminarily characterized, 73 PhGs in total including 55 PhGs of Caff-type, 3 PhGs of Feru-type, 8 PhGs of Coum-type, 1 PhGs of Vani-type, 1 PhGs of Syr-type and 5 PhGs of other types without aromatic acid moieties were characterized. Most PhGs were compounds of Caff-type and had an aglycone of hydroxytyrosol. In addition, compounds 5, 17, 23, 27, 31–34, 36, 39, 43, 46, 51, 65, 68, 76 and 82 were unambiguously identified as salidroside, magnolosides N, B, H, G, F, A, L, I, K, J, 2-(3,4-dihydroxyphenyl) ethanol 1-O-[4-Ocaffeoyl-2-O-α -L-rhamnopyranosyl-3-O-α -L-rhamnopyranosyl-6-O-

3.3.2. Extraction and characterization of PhGs by UNIFITM EIC performed manually was sometimes flawed in characterization of coeluting peaks. Resorting to the core-idea of database retrieval, the automated UNIFI software was able to dramatically accomplish chromatographic peak detection, molecular formula prediction, MS/MS fragment matching, preliminary chemical characterization, and especially could characterize the coeluting ingredients almost independent of human assistance [26]. Then UNIFI was used for automated extracting PhG compounds from the dataset acquired by the MSE scan mode with the assistance of an in-house TCM library. The retention time of 24 reference compounds was added to the TCM library to achieve their unambiguous characterization in different samples, and the English name and molecular formula of 69 PhGs and phenolic glycosides that were ever isolated or characterized from M. officinalis (up to 2018) were involved. All the structure files were prepared using ChemBioDraw 14.0 and then saved as a .mol file individually. Considering the relatively low accurate rate of UNIFI by only one filtering, to reduce the false positive results, two filtering of “common fragment” and “common neutral loss” were simultaneously utilized to efficiently characterize other potentials. Key parameters within UNIFI were set as follows: peak detection time, 0–15 min; intensity threshold, 200 and 25 counts for low energy and high energy acquisitions, respectively; mass accuracy, ±10 ppm; and retention time window, ± 0.2 min. Based on the illustration in Fig. 5B, groups

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

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Fig. 4. Representative MS spectra of PhGs of Caff/Feru/Coum-type and Vani/Syr-type and their proposed fragmentation pathways. (A) Caff-type (magnoloside H); (B) Ferutype (magnoloside K); (C) Coum-type (magnoloside I); (D) Vani-type (magnoloside P); (E) Syr-type (magnoloside O). (Caff: caffeoyl, Feru: feruloyl, Coum: coumaroyl, Vani: vanilloyl, Syr: syringoyl.).

of caffeoyl, feruloyl and coumaroyl were able to produce characteristically diagnostic ions of m/z 161.0239, 175.0395, 145.0290 and neutral loss of 162.0317 Da, 176.0473 Da and 146.0368 Da. Simultaneously using the two filters of “common fragment” and “common neutral loss”, a total of 53 compounds were extracted from the profile and 50 compounds were further characterized as PhGs according to their detailed MSE data. The accuracy rate of 94.34% was much higher than that of using only “common fragment” or “common neutral loss”, in which the accuracy rates were 24.54% and 68.75%, respectively. Combined with the two filters of “common fragment” and “common neutral loss”, in addition to the repeated compounds characterized from EICs, 28 other unknown compounds 24, 26, 37, 40, 41, 47–50, 52, 53, 56, 60, 62, 67, 69, 70, 74, 77, 78, 81, 86, 87, 95, 96, and 98–100 were putatively characterized as PhGs using the UNIFI method. Most compounds such as compounds 24, 53, 74 and 77 were the coeluting peaks to compounds 25, 54, 73 and 78. The above results showed the UNIFI method could complement the method of manually extracting characteristic ions. Ultimately, 101 PhGs were identified or putatively characterized from M. officinalis (Table 1). Compounds 1, 23, 27, 32–34, 54, 57, 61, 68, 76, 79, 82–83 and 90 were the shared compounds among stem bark, leaf and flower, and compounds 58, 78, 87, 89 and 91, the aglycone of which was tyrosol or 3,4-dehydroxyphenethyl alcohol, were especially prevalent in flowers.

3.4. Establishment of models for discrimination isomers of PhGs The MS spectra showed in Fig. S6 indicated that it is hard to discriminate the isomers of PhGs directly only based on the main fragment ions, so untargeted FAAFI by means of a home-made program and chemometrics were employed to establish models for discrimination stereoisomers and positional isomers of PhGs. 3.4.1. Generation of data set for the subsequent isomer recognition models A home-made program was used to preprocess the raw data of all fragment ions and then generate a data set. At first, the MS/MS data of reference compounds were used to establish the two classification models. To obtain comprehensive information about the profile of PhGs, the MS/MS data of reference compounds and the metabolites profiling of M. officinalis samples were acquired in Continnum format. To reduce the analysis load and make the alignment convenient, the MS/MS data were transferred into Centroid format using UNIFI. Meanwhile, a mass range of 100 to quasi-molecular ions was applied (the quasi-molecular ions were excluded). Among all of the compounds, there were three main types of fragment ions, the characteristic ions with higher relative intensity and the specific ions attributed to the specific structure of the compound and the background ions. The latter two types usually had low relative intensity. Notably, the characteristic ions

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

JID: CHROMA

 (mDa)

Component name

2.7

-H

623.2183, 477.1627, 315.1046, 161.0215

785.2504

1.3

-H

623.2175, 461.1651, 315.1081, 161.0218

803.2606

803.2610

−0.4

-H+H2 O

623.2183, 477.1627, 461.1628, 315.1044, 161.0215

C35 H47 O21

803.2609

803.2610

−0.1

-H+H2 O

623.2175, 477.1576, 461.1651, 315.1081, 161.0218

4.14

C41 H55 O25

947.3051

947.3032

1.9

-H

785.2531, 623.2183, 477.1582, 461.1651, 161.0215

11 12

4.53 4.68

C47 H65 O30 C29 H35 O15

1109.3584 623.1970

1109.3561 623.1976

2.3 −0.6

-H -H

947.3327, 785.2474, 639.2051, 477.1576, 161.0218 461.1651, 315.1044, 161.0218

13 14

4.85 4.99

C29 H37 O16 C35 H45 O20

641.2055 785.2517

641.2082 785.2504

−2.7 1.3

-H+H2 O -H

461.1651, 315.1044, 161.0218 623.2175, 477.1576, 461.1651, 315.1008, 161.0218

15

5.31

C41 H55 O25

947.3051

947.3032

1.9

-H

16

5.49

C41 H55 O25

947.3075

947.3032

4.3

-H

785.2531, 639.2103, 623.2225, 461.1651, 315.1044, 161.0218 785.2474, 623.2234, 477.1582, 161.0189

17∗ 18 19 20

5.60 5.76 5.90 6.05

C41 H55 O25 C35 H45 O21 C40 H53 O25 C41 H55 O25

947.3075 801.2479 933.2894 947.3075

947.3032 801.2453 933.2876 947.3032

4.3 2.6 1.8 4.3

-H -H -H -H

785.2517, 639.2155, 771.2360, 785.2474,

623.2225, 477.1576, 609.2003, 623.2225,

21 22

6.17 6.24

C35 H45 O21 C41 H55 O25

801.2479 947.3075

801.2453 947.3032

2.6 4.3

-H -H

477.1576, 315.1044, 161.0218, 135.0407 623.2175, 477.1620, 315.1081,161.0218,

23∗ 25

6.36 6.53

C35 H45 O20 C35 H45 O20

785.2531 785.2517

785.2504 785.2504

2.7 1.3

-H -H

27∗ 28 29

6.67 6.90 7.15

C34 H43 O20 C29 H35 O16 C35 H45 O20

771.2360 639.1948 785.2517

771.2348 639.1925 785.2504

1.2 2.3 1.3

-H -H -H

30

7.21

C35 H45 O20

785.2531

785.2504

2.7

-H

639.2155, 785.2531, 135.0407 623.2183, 623.2225, 478.1538, 477.1605, 133.0286, 609.2053, 477.1576, 623.2175, 478.1538, 477.1605, 133.0286, 623.2183,

PG+Caff+Glc+Rha Isomer of magnoloside B or F PG+Caff+Glc+Rha Isomer of magnoloside B or F PG+Caff+Glc+Rha Isomer of magnoloside B or F PG+Caff+Glc+Rha Isomer of magnoloside B or F PG+Caff+2Glc+Rha Isomer of magnoloside N PG+Caff +3Glc+Rha PG+Caff+Rha Isomer of magnoloside A or M or D PG+Caff+Rha PG+Caff+Glc+Rha Isomer of magnoloside B or F PG+Caff+2Glc+Rha Isomer of magnoloside N PG+Caff+2Glc+Rha Isomer of magnoloside N Magnoloside N∗ PG+Caff+2Glc PG+Caff+2Glc+Api PG+Caff+2Glc+Rha Isomer of magnoloside N PG+Caff+2Glc PG+Caff+2Glc+Rha Isomer of magnoloside N Magnoloside B∗ PG+Caff+Glc+Rha (allose as core sugar, 6-mono-substitued)

31∗ 32∗

7.29 7.50

C34 H43 O20 C35 H45 O20

771.2360 785.2531

771.2348 785.2504

1.2 2.7

-H -H

33∗ 34∗ 35

7.62 7.75 7.91

C29 H35 O15 C28 H33 O15 C35 H45 O20

623.1978 609.1801 785.2517

623.1976 609.1819 785.2504

0.2 −1.8 1.3

-H -H -H

Formula

m/z (observed)

m/z (calc.)

Caff-type (55) 6 3.14

C35 H45 O20

785.2531

785.2504

7

3.22

C35 H45 O20

785.2517

8

3.88

C35 H47 O21

9

4.06

10

609.2053, 623.2183, 135.0407 461.1672, 447.1499, 623.2175, 279.0862, 161.0237, 162.0269,

477.1582, 461.1651, 279.0862, 161.0237, 162.0269, 477.1620, 161.0218 461.1651, 279.0862, 161.0237, 162.0269, 477.1627,

477.1620, 161.0218 161.0218 477.1620, 315.1008, 161.0218 477.1620, 161.0218, 135.0431

461.1628, 315.1081, 262.0795, 221.0660, 261.0763, 315.1081,

315.1046,161.0215 161.0218, 135.0407, 179.0555, 147.0409, 623.2187, 624.2217, 176.0437, 461.1551 161.0218, 135.0407

315.1044, 262.0795, 221.0660, 261.0763, 461.1672,

161.0218, 179.0555, 623.2187, 176.0437, 315.1046,

135.0407, 147.0409, 624.2217, 461.1551 161.0215

477.1576, 315.1081, 161.0218, 135.0407 477.1627, 461.1672, 315.1082, 161.0215, 315.1046, 315.1044, 477.1576, 262.0795, 221.0660, 261.0763,

161.0215, 161.0218, 161.0218, 179.0555, 623.2187, 176.0437,

135.0407 135.0407 135.0407, 478.1538, 147.0409, 477.1605, 624.2217, 133.0286, 461.1551

Magnoloside H∗ PG+Caff+Glc Magnoloside B isomer PG+Caff+Glc+Rha (allose as core sugar, 2,3,6-tri-substitued) PG+Caff+Glc+Rha Isomer of magnoloside B or F Magnoloside G∗ Magnoloside F∗ Magnoloside A∗ Magnoloside L∗ Magnoloside B isomer PG+Caff+Glc+Rha (allose as core sugar, 2,3,6-tri-substitued)

B

F

L

√ √ √ √ √ √ √

√

√ √ √ √ √ √ √ √ √ √ √ √

√ √ √

√ √ √

√

√

√ √ √

(continued on next page)

7

[m5G;October 16, 2019;0:0]

(−)-ESI-MS/MS (m/z)

tR (min)

ARTICLE IN PRESS

Adducts

No

Z. Xue, C. Lai and L. Kang et al. / Journal of Chromatography A xxx (xxxx) xxx

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

Table 1 Characterization of PhGs and their isomers in M. officinalis by UPLC−QTOF/MS.

No

tR (min)

Formula

m/z (observed)

m/z (calc.)

 (mDa)

Adducts

(−)-ESI-MS/MS (m/z)

Component name

785.2531

785.2504

2.7

-H

623.2234, 461.1584, 315.1046, 161.0189

42

8.44

C35 H45 O20

785.2517

785.2504

1.3

-H

623.2175, 477.1620, 161.0218, 135.0431

43∗ 44 46∗

8.46 8.54 8.63

C36 H47 O20 C29 H35 O16 C41 H55 O24

799.2663 639.1940 931.3132

799.2661 639.1925 931.3083

0.2 1.5 4.9

-H -H -H

637.2234, 491.1868, 329.1423, 161.0218 477.1537, 315.1082, 161.0215 769.2805, 623.2225, 477.1620, 161.0218

55 57

9.31 9.45

C23 H25 O11 C29 H35 O15

477.1397 623.1970

477.1397 623.1976

0.0 −0.6

-H -H

58 59 61

9.53 9.55 9.73

C41 H55 O23 C41 H55 O23 C28 H33 O15

915.3132 915.3125 609.1801

915.3134 915.3134 609.1819

−0.2 −0.9 −1.8

-H -H -H

315.1008, 461.1651, 119.0339, 115.0393, 145.0504, 753.2811, 753.2767, 447.1499, 119.0339, 115.0393, 145.0504,

64 66

9.81 9.88

C23 H25 O11 C35 H45 O20

477.1403 785.2517

477.1397 785.2504

0.6 1.3

-H -H

161.0189 623.2175, 477.1576, 315.1081, 161.0218

68∗ 71

10.33 10.69

C29 H35 O15 C29 H35 O15

623.1970 623.1978

623.1976 623.1976

−0.6 0.2

-H -H

461.1651, 315.1081, 161.0218 477.1940, 461.1672, 315.1046, 161.0189

75

10.91

C35 H45 O20

785.2517

785.2504

1.3

-H

76∗ 79 80 82∗ 85 89 91 92

11.11 11.32 11.47 11.67 12.17 12.96 13.40 13.86

C28 H33 O15 C35 H45 O19 C35 H45 O19 C29 H35 O15 C35 H45 O19 C29 H35 O13 C29 H35 O13 C35 H45 O20

609.1801 769.2551 769.2551 623.1978 769.2551 591.2058 591.2058 785.2517

609.1819 769.2555 769.2555 623.1976 769.2555 591.2078 591.2078 785.2504

−1.8 −0.4 −0.4 0.2 −0.4 −2.0 −2.0 1.3

-H -H -H -H -H -H -H -H

93

13.94

C35 H45 O20

785.2460

785.2504

−4.4

-H

623.2175, 478.1538, 477.1605, 133.0286, 447.1499, 607.2226, 607.2226, 461.1672, 607.2226, 161.0218 161.0189 623.2175, 478.1538, 477.1605, 133.0286, 623.2175,

161.0218 315.1081, 298.1002, 443.1541, 251.0457, 607.2226, 607.2354, 315.1081, 298.1002, 443.1541, 251.0457,

461.1651, 279.0862, 161.0237, 162.0269, 315.1081, 461.1628, 461.1672, 315.1046, 461.1672,

477.1576, 279.0862, 161.0237, 162.0269, 477.1576,

161.0218, 144.9814, 114.0270, 251.0573 445.1722, 461.1563, 161.0218, 144.9814, 114.0270, 251.0573

315.1081, 262.0795, 221.0660, 261.0763, 161.0218, 315.1046, 315.1081, 161.0215, 315.1046,

461.1651, 262.0795, 221.0660, 261.0763, 461.1695,

297.0974, 249.0761, 205.0711, 113.0236, 181.0529, 206.0785, 299.1135, 315.1081, 297.0974, 205.0711, 181.0529,

161.0218, 179.0555, 623.2187, 176.0437, 35.0407 161.0215, 161.0215 135.0407 161.0215,

315.1008, 179.0555, 623.2187, 176.0437, 315.1081,

161.0189 161.0218 249.0761, 113.0236, 206.0785,

135.0407, 147.0409, 624.2217, 461.1551 135.0405

135.0428

161.0218, 147.0409, 624.2217, 461.1551 161.0218

PG+Caff+Glc+Rha Isomer of magnoloside B or F PG+Caff+Glc+Rha Isomer of magnoloside B or F Magnoloside J∗ PG+Caff+Glc 2-(3,4-dihydroxyphenyl) ethanol 1-O-[4-O-caffeoyl-2-O-α -L-rhamnopyranosyl-3-Oα -L-rhamnopyranosyl-6-O-β -D-glucopyranosyl]β -D-glucopyranoside∗ PG+Caff Isolugrandoside isomer15 PG+Caff+Rha (glucose as core sugar, 3,4-bis-substitued) LG+Caff+2Rha+Glc PG+Caff+3Rha β -(3,4-Dihydroxyphenyl) ethyl-3-O-E-caffeoyl-O[β -D -apiofuranosyl-(1→2)]-β -D -glucopyranoside isomer17 PG+Caff+Api (glucose as core sugar, 3,4-bis-substitued) PG+Caff PG+Caff+Glc+Rha Isomer of magnoloside B or F Magnoloside M∗ PG+Caff+Rha Isomer of magnoloside A or M or D PG+Caff+Glc+Rha (allose as core sugar, 2,3,6-tri-substitued)

Magnoloside E∗ PG+Caff+2Rha PG+Caff+2Rha Magnoloside D∗ PG+Caff+2Rha Tyrosol+Caff+2Rha Tyrosol+Caff+2Rha PG+Caff+Glc+Rha (allose as core sugar, 2,3,6-tri-substitued)

PG+Caff+Glc+Rha Isomer of magnoloside B or F

√

√

√

√ √ √

√ √

√

√ √

√ √ √

√ √ √ √ √

√ √ √ √ √ √

√

√

√ √

√

(continued on next page)

[m5G;October 16, 2019;0:0]

C35 H45 O20

L

ARTICLE IN PRESS

8.20

F

√

Z. Xue, C. Lai and L. Kang et al. / Journal of Chromatography A xxx (xxxx) xxx

38

B

JID: CHROMA

8

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

Table 1 (continued)

JID: CHROMA

Formula

m/z (observed)

m/z (calc.)

 (mDa)

Adducts

(−)-ESI-MS/MS (m/z)

94

14.00

C35 H45 O20

785.2460

785.2504

−4.4

-H

97

14.39

C29 H35 O15

623.1970

623.1976

−0.6

-H

101 14.60 Feru-type (3) 8.23 39∗ 63 9.79 84 11.88

C29 H35 O15

623.1978

623.1976

0.2

-H

623.2123, 135.0407, 147.0409, 624.2217, 461.1551 461.1651, 119.0339, 115.0393, 145.0504, 477.1627,

C36 H47 O20 C30 H37 O15 C52 H49 O18

799.2663 637.2131 961.2968

799.2661 637.2132 961.2919

0.2 −0.1 4.9

-H -H -H

623.2175, 477.1576, 461.1651, 315.1044, 175.0370 461.1651, 315.1081, 175.0370 785.2517, 623.2175, 477.1576, 461.1651, 315.1081, 175.0370

Magnoloside K∗ PG+Feru+Rha PG+Feru+2Glc+Rha

755.2398 769.2578 607.2051 607.2024 899.3153 607.2051 753.2598 607.2051

755.2399 769.2555 607.2027 607.2027 899.3185 607.2027 753.2606 607.2027

−0.1 2.3 2.4 −0.3 −3.2 2.4 −0.8 2.4

-H -H -H -H -H -H -H -H

609.2053, 623.2175, 461.1651, 461.1672, 753.2823, 461.1695, 607.2253, 461.1651,

Magnoloside I∗ PG+Coum+Rha+Glc PG+Coum+Rha PG+Coum+Rha PG+Coum+3Rha PG+Coum+Rha PG+Coum+2Rha PG+Coum+Rha

773.2509

773.2504

0.5

-H

465.1365, 315.1008

magnoloside P∗

803.2609 acid (5) 315.1082 623.2175 447.1499 461.1651 299.1118

803.2610

−0.1

-H

495.1485, 315.1044, 135.0407

magnoloside O∗

315.1080 623.2187 447.1503 461.1659 299.1131

0.2 −1.2 −0.4 −0.8 −1.3

-H -H -H -H -H

135.0428 477.1620, 315.1044, 315.1044, 137.0618,

315.1081, 135.0431 135.0407 135.0407 119.0457

PG PG+Rha+Glc PG+Api PG+Rha salidroside∗

933.2894 639.1948 1035.3234 769.2578 755.2398

933.2876 639.1925 1035.3193 769.2555 755.2399

1.8 2.3 4.1 2.3 −0.1

-H -H -H -H -H

771.2303, 477.1576, 873.3036, 607.2253, 609.2053, 262.0795, 221.0660, 261.0763,

609.2053, 477.1620, 161.0218,135.0407 477.1576, 315.1008, 461.1651, 161.0218 477.1576, 161.0218, 179.0555, 147.0409, 623.2187, 624.2217, 176.0437, 461.1551

Coum-type (8) 8.00 C34 H43 O19 36∗ 45 8.57 C35 H45 O19 54 9.20 C29 H35 O14 72 10.70 C29 H35 O14 73 10.71 C41 H55 O22 83 11.85 C29 H35 O14 88 12.81 C35 H45 O18 90 13.04 C29 H35 O14 Vani-type (1) 9.13 C34 H45 O20 51∗ Syr-type (1) 9.84 C35 H47 O21 65∗ Others-no substituted aromatic 1 1.95 C14 H19 O8 2 2.33 C26 H39 O17 3 2.55 C19 H27 O12 4 2.73 C20 H29 O12 2.96 C14 H19 O7 5∗ UNIFI (28) 24 6.51 C40 H53 O25 26 6.66 C29 H35 O16 37 8.12 C44 H59 O28 40 8.38 C35 H45 O19 41 8.39 C34 H43 O19

Component name

B

461.1695, 279.0862, 161.0237, 162.0269,

315.1044, 262.0795, 221.0660, 261.0763,

161.0218, 179.0555, 623.2187, 176.0437,

PG+Caff+Glc+Rha (allose as core sugar, 2,3,6-tri-substitued)

315.1081, 298.1002, 443.1541, 251.0457, 461.1628,

161.0218, 144.9814, 114.0270, 251.0573 161.0215,

297.0974, 249.0761, 205.0711, 113.0236, 181.0529, 206.0785,

Plantalloside isomer15 PG+Caff+Rha (glucose as core sugar, 3,4-bis-substitued)

135.0428

PG+Caff+Rha Isomer of magnoloside A or M or D

315.1081, 315.1044, 145.0248 145.0260 461.1695, 145.0248 315.1008, 145.0248

145.0273 145.0273

315.1081, 145.0248 145.0248

315.1044, 161.0218 161.0218 478.1538, 279.0862, 477.1605, 161.0237, 133.0286, 162.0269,

PG+Caff+2Glc+Api PG+Caff+Glc PG+Caff+Glc+3Api PG+Caff+2Rha PG+Coum+Glc+Api (allose as core sugar, 6-mono-substitued)

L √

477.1576, 478.1538, 477.1605, 133.0286,

477.1620, 477.1620, 315.1044, 315.1082, 607.2202, 315.1081, 461.1651, 315.1081,

F

√

√ √ √ √

√

√ √

√

√

√

√ √

√ √

√ √ √ √ √ √ √ √ √ √ √ √ √ √ √

√ √

√ √

√ √ √

√

(continued on next page)

9

[m5G;October 16, 2019;0:0]

tR (min)

ARTICLE IN PRESS

No

Z. Xue, C. Lai and L. Kang et al. / Journal of Chromatography A xxx (xxxx) xxx

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

Table 1 (continued)

JID: CHROMA

10

No

tR (min)

Formula

m/z (observed)

m/z (calc.)

 (mDa)

Adducts

(−)-ESI-MS/MS (m/z)

Component name

8.63

C41 H55 O25

947.3075

947.3032

4.3

-H

48

8.76

C35 H45 O20

785.2517

785.2504

1.3

-H

49 50 52 53 56 60 62

8.90 8.95 9.15 9.15 9.35 9.69 9.76

C35 H45 O19 C36 H47 O20 C42 H57 O27 C29 H35 O14 C28 H33 O14 C42 H57 O25 C41 H55 O23

769.2534 799.2663 993.3040 607.2051 593.1856 961.3158 915.3125

769.2555 799.2661 993.3087 607.2027 593.1870 961.3189 915.3134

−2.1 0.2 −4.7 2.4 −1.4 −3.1 −0.9

67

10.13

C29 H35 O15

623.1978

623.1976

0.2

-H

69

10.54

C29 H35 O15

623.1965

623.1976

−1.1

-H

785.2517, 135.0407 623.2175, 279.0862, 161.0237, 162.0269, 607.2225, 623.2175, 785.2460, 461.1651, 447.1456, 785.2517, 769.2805, 161.0218, 461.1628, 119.0339, 115.0393, 145.0504, 461.1651,

70

10.57

C41 H55 O25

947.3012

947.3032

−2.0

-H

785.2460, 623.2175, 477.1620, 161.0218

74 77

10.76 11.16

C29 H35 O14 C29 H35 O15

607.2023 623.1970

607.2027 623.1976

−0.4 −0.6

-H -H

461.1641, 315.1119, 145.0265 461.1651, 315.1081, 161.0218

78 81 86 87 95 96

11.27 11.57 12.51 12.66 14.00 14.29

C35 H45 O18 C29 H35 O14 C30 H37 O15 C35 H45 O18 C45 H61 O28 C29 H35 O15

753.2598 607.2023 637.2153 753.2638 1049.3353 623.1970

753.2606 607.2027 637.2132 753.2606 1049.3349 623.1976

−0.8 0.4 2.1 3.2 0.4 −0.6

-H -H -H -H -H -H

98 99

14.43 14.43

C39 H51 O23 C29 H35 O15

887.2864 623.1978

887.2821 623.1976

4.3 0.2

-H -H

100

14.53

C39 H51 O23

887.2803

887.2821

−1.8

-H

591.2267, 461.1641, 461.1651, 591.2318, 887.3353, 461.1651, 119.0339, 115.0393, 145.0504, 725.2885, 461.1695, 119.0339, 115.0393, 145.0504, 725.2775, 135.0407

-H -H +HCOO -H -H -H -H

623.2175, 477.1576, 315.1044, 162.0218, 477.1620, 262.0795, 221.0660, 261.0763, 461.1641, 477.1620, 623.2225, 315.1044, 315.1081, 623.2175, 623.2175, 135.0407 315.1046, 298.1002, 443.1541, 251.0457, 315.1044,

445.1790, 315.1119, 315.1081, 445.1703, 315.1117, 315.1081, 298.1002, 443.1541, 251.0457, 461.1695, 315.1044, 298.1002, 443.1541, 251.0457, 579.2214,

161.0218, 179.0555, 623.2187, 176.0437, 161.0208 315.1044, 461.1695, 145.0248 145.0248 477.1397, 477.1486,

135.0407, 478.1538, 147.0409, 477.1605, 624.2217, 133.0286, 461.1551 175.0370, 135.0407 315.1044, 161.0218

315.1081, 175.0342 461.1695, 315.1081,

161.0215, 297.0974, 249.0761, 144.9814, 205.0711, 113.0236, 114.0270, 181.0529, 206.0785, 251.0573 161.0208

283.1172, 145.0265 175.0361 299.1183, 161.0218, 161.0218, 144.9814, 114.0270, 251.0573 315.1044, 161.0215, 144.9814, 114.0270, 251.0573 447.1499,

PG+Caff+2Glc+Rha Isomer of magnoloside N PG+Caff+Glc+Rha (allose as core sugar, 6-mono-substitued)

PG+Caff+2Rha PG+Feru+Rha+Glc PG+Caff+2Glc+Rha PG+Coum+Rha PG+Coum+Api PG+Feru+Rha+2Glc PG+Coum+2Rha+Glc Plantalloside isomer15 PG+Caff+Rha (glucose as core sugar, 4,6-bis-substitued)

161.0208, 135.0407 297.0974, 205.0711, 181.0529,

249.0761, 113.0236, 206.0785,

PG+Caff+Rha Isomer of magnoloside A or M or D PG+Caff+2Glc+Rha Isomer of magnoloside N PG+Coum+Rha PG+Caff+Rha Isomer of magnoloside A or M or D BG+Caff+Rha+Glc PG+Coum+Rha PG+Feru+Rha LG+Caff+2Rha PG+Caff+2Api+Rha+Glc Plantalloside isomer15 PG+Caff+Rha (glucose as core sugar, 3,4-bis-substitued)

161.0218, 297.0974, 205.0711, 181.0529,

135.0407 249.0761, 113.0236, 206.0785,

PG+Caff+2Api+Rha Plantalloside isomer15 PG+Caff+Rha (glucose as core sugar, 3,4-bis-substitued)

161.0208, 135.0417

135.0417

315.1044, 161.0218,

PG+Caff+2Api+Rha

F

L

√ √

√ √ √ √ √

√

√ √

√ √

√

√

√

√

√ √ √ √ √ √ √ √ √ √

√

√

√

√ √

√

√

√

[m5G;October 16, 2019;0:0]

Serial number of compounds was labeled according to the corresponding retention time. Caff: caffeoyl; Coum: coumaroyl; Feru: feruloyl; Glc: glucopyranosyl; All: allopyranosyl; Rha: rhamnopyranosyl; Api: apiofuranosyl; PG: hydroxytyrosol plus Glc/All; LG: p-tyrosol plus Glc/All; BG: dehydroxytyrosol plus Glc/All. √ : The components identified in a part of M. officinalis. B: bark; F: flower; L: leaf. ∗ : The components confirmed by comparison with the reference standards.

ARTICLE IN PRESS

47

B

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Fig. 5. Main aglycone, aromatic acid and saccharide groups of PhGs in M. officinalis.

in the current study are referring to the ions with higher relative intensity and produced from loss of characteristic moieties of aromatic acid and sugars, e.g., the fragment ions of m/z 623.2187, 161.0239, 461.1659, 477.1608, 315.1080 and 135.0446 of magnoloside B, which were induced from loss of caffeoyl, glucose, rhamnose and allose, respectively; and the specific ions were the ions usually produced from loss of H2 O or other small moieties attributed to the specific structure of the compound. Almost each PhG could produce characteristic ions, rather than specific ions. Thus, the following data adjustment focused on characteristic ions. To avoid differences among the characteristic ions ascribed to loss of different aromatic acid and sugar moieties, characteristic ions of other compounds were adjusted by adding or subtracting, referring to the ions of magnoloside B. Fig. 6 illustrates the detailed adjustment information for characteristic ions of magnolosides I, L and forsythoside B. Taking magnoloside I, which consists of moieties of coumaroyl, glucose and apiose, as an example, the difference values between coumaroyl and caffeoyl, glucose and glucose, apiose and rhamnose were 15.9949 Da, 0 Da and 14.0157 Da, respectively, and then the quasimolecular ion should be added by 30.0106 Da, the quasi-molecular ion minus coumaroyl moiety and quasi-molecular ion minus coumaroyl and glucose moieties should be added by 14.0157 Da, respectively, and the fragment ion produced from coumaroyl should be added by 14.0157 Da, and finally, the values of m/z of the fragment ions of magnoloside I were adjusted and merged with those of magnoloside B. Detailed data about adjustment of fragment ions of other compounds are provided in Supplementary Table S1. After the adjustment of the values of m/z of characteristic ions and no modification to background ions and specific ions, all of the data were ready to be merged into a union set using Python 3.7. In the process of generating the union set, the fragment ions of magnoloside B still worked as references, and fragment ions of other compounds with differentials less than 0.01 were regarded as the same ones and

unified their names according to these of magnoloside B. Other fragment ions which were not merged were all retained and average values were used to fill the vacancies of some compounds. The main purpose of the application of average values was to weaken interference of some interfering ions. Detailed information of the programming code is shown in Fig. S7. Thus, a data matrix mixed with 2556 variables and 20 observations (reference compounds 2– 9A, 11–15A, and 3–9G) was obtained to export into SIMCA-P for multivariable analysis (Data matrix was offered in Supplementary Excel 1). Both PLS-DA and PCA were used to establish the isomer recognition model to discriminate the isomers of PhGs. PLS-DA can maximize the difference among the groups and the VIP-plot generated from the PLS-DA model can aid in screening variables with the largest contribution to the separation among the groups. Model quality was evaluated by R2 X, R2 Y, which respectively represented the fraction of the variance of the X matrix and the Y matrix, and Q2 was defined as the proportion of variance in the data predicted by the model. After the discriminant variables were generated, PCA, an unsupervised classification technique, was used to explore the overall sample distribution and could confirm the reasonability of the discriminant ions. 3.4.2. Establishment of models for discrimination stereoisomers of PhGs Discrimination of isomers contained two aspects: characterization of stereoisomerism of the core sugar and positional isomerism of the substituents on the core sugar. At first, models for the distinction of the stereoisomerism of core sugars were established. PhGs were classified into two groups according to the total amount of sugars in the structure, i.e., disaccharide glycoside and trisaccharide glycoside. Then, a PLS-DA analysis was performed to distinguish the stereoisomerism of the core sugars in the above two groups; the score plots of PLS-DA are given in Fig. S8A-B. To reduce the workload and more efficiently distinguish unknown com-

Please cite this article as: Z. Xue, C. Lai and L. Kang et al., Profiling and isomer recognition of phenylethanoid glycosides from Magnolia officinalis based on diagnostic/holistic fragment ions analysis coupled with chemometrics, Journal of Chromatography A, https://doi.org/ 10.1016/j.chroma.2019.460583

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Fig. 6. Adjustment of characteristic fragment ions of magnoloside I (8A), L (7A) and forsythoside B (5G) referring to the data of magnoloside B.

pounds, the first five discriminant variables contributing largely to the classification were screened by VIP value. More clear separations were observed on the new PLS-DA score plot in Fig. S9A-B, which shows that the above two groups of PhGs were separately distinguished according to the stereoisomerism of the core sugars. The values of Q2 were 0.915 and 0.995, respectively, which indicated the models were capable of predicting the classification. Through tracing analysis of the five most discriminant ions, the ions of m/z 297.0974, 249.0761, 119.0339, 298.1002 and 144.9814, distinguishing the core sugar in disaccharide glycosides and m/z 262.0795, 279.0862, 147.0409, 478.1538 and 179.0555, distinguishing the core sugar in trisaccharide glycosides, were established. Most of the ions were produced from aromatic acid or the core sugar plus aglycone, as shown in Fig. S10. Thus, whether the compounds had two or three saccharides in structure, the PhGs with allose as the core sugar could be clearly separated from PhGs with glucose as the core sugar. 3.4.3. Establishment of models for discrimination positional isomers of PhGs After stereoisomerism of the core sugars was characterized, the substituted positions of the different moieties to the core sugar were further identified. According to the reference compounds of PhGs isolated from M. officinalis [18], most PhGs were disaccharide glycosides or trisaccharide glycosides. As for the disaccharide glycosides, the other sugar was usually linked to the C-2 of core sugar directly, and for the trisaccharide glycosides, the other two sugars were usually linked to the C-2, 6 of core sugar except the trisaccharide glycoside of 6-mono-substitued in which the other two sugars were linked to each other and then linked to the aromatic acyl. So the positional isomerism of allopyranoside was mainly caused by different linked positions of aromatic acids. The preliminary classifications are shown in Fig. S8C-D. To obtain the obvious classification quickly, the first ten discriminant variables were screened and a new PLS-DA score plot is shown in Fig. S9C-D. The result revealed that PhGs that included two sugars with glucose as the core sugar of 3,4-bis-substitued, 3,6-bis-substitued and 4,6-bissubstitued could be clearly separated into three groups; the same

situation also applied for PhGs containing two sugars with allose as the core sugar of 2,3-bis-substitued, 2,4-bis-substitued and 2,6bis-substitued (Fig. S11A), meanwhile, PhGs with three sugars with allose as the core sugar of 2,3,6-tri-substituted, 2,4,6-tri-substitued and 6-mono-substitued could also be separated into three groups. The recognition model for PhGs containing three sugars with glucose as the core sugar was not available due to lacking reference standards. Q2 values of 0.972, 0.977 and 0.84 also showed good predicative abilities in the above three models. The first ten ions of m/z 205.0711, 113.0236, 115.0393, 443.1541, 114.0270, 181.0529, 206.0785, 145.0504, 251.0457 and 251.0573 discriminated the substituted positions in disaccharide glycosides with glucose as the core sugar, m/z 161.1402, 161.1016, 160.8407, 144.1222, 158.9964, 160.0556, 161.5200, 174.0233, 161.1564 and 221.0379 discriminated the substituted positions in disaccharide glycosides with allose as the core sugar and m/z 477.1605, 161.0237, 221.0660, 623.2187, 624.2217, 133.0286, 162.0269, 261.0763, 176.0437 and 461.1551 discriminated the substituted positions in trisaccharide glycosides with allose as the core sugar, most of them also come from aromatic acids or the core sugar plus aglycone (Fig. S12). Next, PCA analysis based on the picked discriminant variables was used to explore the reference compounds’ distribution trends as well as confirm the reasonability of the models. The PCA score plots of the reference PhGs revealed unambiguous classification among each of the groups as shown in Fig. S13A-D and Fig. S11B. This result indicated that by using multivariable analysis, the stereoisomerism of the core sugar and the positional isomerism of substituents to the core sugar could be characterized. To be honest, that in some cases, only several samples were included in some isomer group, e.g. the models in Fig. S9C. An extremely tough reality is that the amount of reference compounds is limited, although we have tried our best to isolate or collect. The goodness of fit of PLS-DA models were validated by the chance permutations test (n = 200) according to the reported literatures [27,28]. The corresponding parameters were [R2 (0.0, 0.294), Q2 (0.0, −0.37)], [R2 (0.0, 0.119), Q2 (0.0, −0.396)], [R2 (0.0, 0.162), Q2 (0.0, −0.238)], [R2 (0.0, 0.508), Q2 (0.0, 0.0285)], [R2 (0.0, 0.164), Q2 (0.0, −0.348)], respectively, for discrimination of PhGs owning two

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Fig. 7. Multivariable analyses for the classification of isomers in samples based on discriminant variables. (A) PCA score plot of PhGs with two sugars with allose and glucose as core sugars, respectively (t[x] refers to the score of the xth principal component; PC1: 84.95%, PC2: 8.88%); (B) PCA plot of PhGs with three sugars with allose and glucose as core sugars, respectively (PC1: 61.80%, PC2: 22.44%); (C) PCA plot of PhGs with two sugars with glucose as the core sugar (PC1: 47.30%, PC2: 31.72%); (D) PCA plot of PhGs with three sugars with allose as the core sugar (PC1: 41.13%, PC2: 33.77%). (Observations were marked according to the abbreviated name of compounds, 2A: magnoloside B, 3A: magnoloside H, 4A: magnoloside G, 5A: magnoloside F, 6A: magnoloside A, 7A: magnoloside L, 11A: magnoloside O, 12A: magnoloside P, 13A: magnoloside M, 14A: magnoloside E, 15A: magnoloside D, 3G: echinacoside, 4G: plantamajoside, 5G: forsythoside B, 6G: forsythoside A, 7G: acteoside, 8G: poliumoside, 9G: isoacteoside, H25, 29, 35, 41, 48, 75, 92, 94, 57, 61, 67, 96, 97 and 99 referred to the compounds characterized from the samples.).

sugars with allose and glucose as core sugars (Fig. S9A), PhGs owning three sugars with allose and glucose as core sugars (Fig. S9B), PhGs owning two sugars with allose as the core sugar (Fig. S11A), PhGs owning two sugars with glucose as the core sugar (Fig. S9C) and PhGs owning three sugars with allose as the core sugar (Fig. S9D). Except the models in S9C, the intercept values of R2 and Q2 from other models were all < 0.3 or < 0.05, respectively, which indicated a statistical significance and no over-fitting with high predictive value of the model. 3.5. Characterization of isomers of PhGs in samples After the recognition model was established, the isomers of PhGs in the samples could be recognized. The centroid format data of these isomers were obtained using UNIFI and then aligned using Python 3.7 to be statistically analyzed (Data matrix was offered in Supplementary Excel 2). The above compounds were first separated into two groups according to the total numbers of sugars. Then, the discriminant variables were picked from the data set and input into the recognition models to first characterize the stereoisomerism of the core sugar. It should be emphasized that the output models were PCA score plots that could explore the overall observations of distribution trends under unsupervised conditions. Fig. 7A shows the compounds 57, 61, 67, 96, 97 and 99 with two sugars were classified into the group with glucose as the core

sugar and Fig. 7B shows the compounds 25, 29, 35, 41, 48, 75, 92 and 94 with three sugars were classified to the group with allose as the core sugar. Next, the substituted position was also characterized. Compounds 57, 61, 96, 97 and 99 with two sugars were classified to the group of 3,4-bis-substitued, compound 67 was classified to the group of 4,6-bis-substitued in Fig. 7C, compounds 29, 35, 75, 92 and 94 with three sugars were classified to the group of 2,3,6-trisubstituted and compounds 25, 41 and 48 were classified to the group of 6-mono-substituted in Fig. 7D. The results revealed the above compounds had the largest possibility to belong to the group of 3,4-bis-substituted, 4,6-bis-substituted 2,3,6-tri-substituted and 6-mono-substituted, respectively. With this step, stereoisomerism of the core sugar and the positional isomerism of substituents of the above compounds were preliminarily identified. In addition to the above primary characterization, there was still an interesting discovery about the relationship between the substituted position of the aromatic acid and its corresponding retention time. In terms of PhGs with two sugars with allose as core sugar, retention time was prolonged progressively according to the substituted position of the caffeoyl group from C-3 (magnolosides A and L), C-4 (magnolosides M) to C-6 (magnolosides E and D) of the core sugar. The same situation applied for PhGs with three sugars with allose as the core sugar and PhGs with two sugars with glucose as the core sugar. Based on the find-

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ings, the caffeoyl moieties of compounds 29, 35, 57, 61, and 67, 96, 97, 99, and 75, 92, 94 were substituted to a great extent at C-3, C-4 and C-6 on the core sugar, respectively. Under the rule that the compounds with aromatic acid substituted at the same position were defined as the isomers, compounds 29 and 35 were characterized as isomers of magnoloside B [18], compound 57 and 61 were characterized as isomers of isolugrandoside [20] and β -(3,4-dihydroxyphenyl) ethyl-3-O-E-caffeoyl-O[β -D -apiofuranosyl-(1→2)]-β -D -glucopyranoside [22], respectively, compounds 67, 96, 97 and 99 were characterized as isomers of plantalloside [20], and besides, compounds 25, 41, 48, 75, 92 and 94 characterized as having three sugars with allose as the core sugar were found to have the aromatic acid substituted at C-6 of the core sugar. Taking PhGs as a case, untargeted FAAFI combined with statistical analysis was proposed for the first time and used to discriminate the stereoisomers and positional isomers of PhGs. Compared to the available approaches, such as the peak area-collision energy trajectories, retention time prediction by quantitative structureretention relationships and ion-mobility derived collision cross– section determination [6,8,9,12], in which the ion transition or certain characterized ions of interest were studied, unbiased analysis concerning holistic fragment ions might be an effective complement for adding the creditability of results. Meanwhile, the current strategy added the characterization for stereoisomer of PhGs compared to that by retention time prediction by quantitative structure-retention relationships and could discriminate the positional isomers based on the classification results when there were much more isomers with similar ClogP values. Besides, untargeted FAAFI might be an alternative method when none significant variation was observed for optimum collision energy among isomers whose minor differences only occurred at the substitutes. Although the core analysis technology in essence was similar in current study and Qiao’s study [1], the source of data (untargeted analysis) and the detailed data processing were totally different. Concerning the untargeted analysis in current study, the MS2 spectra data of compounds rather than the chromatograms data were firstly introduced to be generated a dataset and analyzed to explore the discriminant fragment ions contributing to isomer characterization. Meanwhile, a home-made program was used to preprocess the raw data of all fragment ions for the first time (as we all know, the current commercial software was mainly used to preprocess the chromatograms data rather than the MS spectra which were more complicated). In the targeted analysis, the extract ion chromatogram by manual and UNIFITM software filtering were simultaneously utilized to process MS data in current strategy, furthermore, dual filtering of “common fragment” and “common neutral loss” by UNIFITM not only supplemented characterization for coeluting ingredients but also facilitated accurate characterization of PhGs, whereas automatic characterization by UNIFITM was not employed in Qiao’s study. Resorting to the theory of Big Data analysis, a primary discrimination model was tentatively established, but much more data were needed to enlarge the model and to obtain a result closer to the true value. In addition, considering the lithiated or sodiated ions would give fragments from the mono- and/or oligosaccharide moieties in positive ionization [7,29], which would provide detailed information for determination monosaccharides and their sequence, or glycosidic linkage position, more attention would be paid to studying the combination of negative and positive ionization modes to enhance the results and make the data more robust. Although subsequent verification should be performed, this method sheds light on the characterization of isomers of all kinds of natural products from TCMs or other medicinal plants and introduces the concept and methods of Big Data to the untargeted fingerprint analysis of fragment ions.

4. Conclusions A targeted scanning of DINL and untargeted FAAFI coupled with a home-made program and chemometrics analysis were established to tentatively characterize PhGs from M. officinalis and to discriminate isomers with stereoisomerism and positional isomerism. Eight DINL scans based on characteristic ions were used to extract the PhGs compounds and 2556 ions based on all fragment ions were used to establish the isomer recognition models, and finally, 101 PhGs were identified or putatively characterized from M. officinalis, of those, 17 PhGs were unambiguously identified by comparing the retention time and MS/MS data with those of reference compounds. Moreover, 14 isomers were putatively recognized by isomer recognition models. This approach could be applied to other medicinal plants extracts for rapid characterization of metabolites and efficient recognition isomers. Declaration of Competing Interest The authors declare no competing interests. Acknowledgments This work was supported by the National Natural Science Foundation of China (nos. 81773895) and the Basic Research Program of the Ministry of Science and Technology of the People’s Republic of China (nos. 2015FY111500). Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.chroma.2019.460583. References [1] X. Qiao, X.H. Lin, S. Ji, Z.X. Zhang, T. Bo, D.A. Guo, M. Ye, Global profiling and novel structure discovery using multiple neutral loss/precursor ion scanning combined with substructure recognition and statistical analysis (MNPSS): characterization of terpene-conjugated curcuminoids in Curcuma longa as a case study, Anal. Chem. 88 (2016) 703–710. [2] X.J. Shi, W.Z. Yang, S. Qiu, C.L. Yao, Y. Shen, H.Q. Pan, Q.R. Bi, M. Yang, W.Y. Wu, D.A. Guo, An in-source multiple collision-neutral loss filtering based nontargeted metabolomics approach for the comprehensive analysis of malonyl-ginsenosides from Panax ginseng, P. quinquefolius, and P. notoginseng, Anal. Chim. Acta 952 (2017) 59–70. [3] Y.L. Song, Q.Q. Song, J. Li, N. Zhang, Y.F. Zhao, X. Liu, Y. Jiang, P.F. Tu, An integrated strategy to quantitatively differentiate chemome between Cistanche deserticola and C. tubulosa using high performance liquid chromatography-hybrid triple quadrupole-linear ion trap mass spectrometry, J. Chromatogr. A 1429 (2016) 238–247. [4] F. Destaillats, C. Cruz-Hernandez, K. Nagy, F. Dionisi, Identification of monoacylglycerol region-isomers by gas chromatography-mass spectrometry, J. Chromatogr. A 1217 (2010) 1543–1548. [5] H. Ouyang, J.M. Li, B. Wu, X.Y. Zhang, Y. Li, S.L. Yang, M.Z. He, Y.L. Feng, A robust platform based on ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry with a two-step data mining strategy in the investigation, classification, and identification of chlorogenic acids in Ainsliaea fragrans Champ, J. Chromatogr. A 1502 (2017) 38–50. [6] G.F. Feng, Y. Zheng, Y.F. Sun, S. Liu, Z.F. Pi, F.R. Song, Z.Q. Liu, A targeted strategy for analyzing untargeted mass spectral data to identify lanostane-type triterpene acids in Poria cocos by integrating a scientific information system and liquid chromatography-tandem mass spectrometry combined with ion mobility spectrometry, Anal. Chim. Acta 1033 (2018) 87–99. [7] L.M. de Souza, N. Dartora, C.T. Scoparo, P.A. Gorin, M. Iacomini, G.L. Sassaki, Differentiation of flavonol glucoside and galactoside isomers combining chemical isopropylidenation with liquid chromatography-mass spectrometry analysis, J. Chromatogr. A 1447 (2016) 64–71. [8] C.J.S. Lai, L.P. Zha, D.H. Liu, L.P. Kang, X.J. Ma, Z.L. Zhan, T.G. Nan, J. Yang, F.J. Li, Y. Yuan, L.Q. Huang, Global profiling and rapid matching of natural products using diagnostic product ion network and in silico analogue database: Gastrodia elata as a case study, J. Chromatogr. A 1456 (2016) 187–195. [9] T.A. Garran, R.F. Ji, J.L. Chen, D.M. Xie, L.P. Guo, L.Q. Huang, C.J.S. Lai, Elucidation of metabolite isomers of Leonurus japonicus and Leonurus cardiaca using discriminating metabolite isomerism strategy based on ultra-high performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry, J. Chromatogr. A 1598 (2019) 141–153.

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