An Integrated Strategy for Global Qualitative and Quantitative Profiling of Traditional Chinese Medicine Formulas: Baoyuan Decoction as a Case

Clarification of the chemical composition of traditional Chinese medicine formulas (TCMFs) is a challenge due to the variety of structures and the complexity of plant matrices. Herein, an integrated strategy was developed by hyphenating ultra-performance liquid chromatography (UPLC), quadrupole time-of-flight (Q-TOF), hybrid triple quadrupole-linear ion trap mass spectrometry (Qtrap-MS), and the novel post-acquisition data processing software UNIFI to achieve automatic, rapid, accurate, and comprehensive qualitative and quantitative analysis of the chemical components in TCMFs. As a proof-of-concept, the chemical profiling of Baoyuan decoction (BYD), which is an ancient TCMF that is clinically used for the treatment of coronary heart disease that consists of Ginseng Radix et Rhizoma, Astragali Radix, Glycyrrhizae Radix et Rhizoma Praeparata Cum Melle, and Cinnamomi Cortex, was performed. As many as 236 compounds were plausibly or unambiguously identified, and 175 compounds were quantified or relatively quantified by the scheduled multiple reaction monitoring (sMRM) method. The findings demonstrate that the strategy integrating the rapidity of UNIFI software, the efficiency of UPLC, the accuracy of Q-TOF-MS, and the sensitivity and quantitation ability of Qtrap-MS provides a method for the efficient and comprehensive chemome characterization and quality control of complex TCMFs.

The clinical application and research of traditional Chinese medicine formulas (TCMFs) have drawn increasing attention in recent years because of their promising efficacies and minimal side effects, in particularly for multifactorial disorders 1 . Although well-accepted and widely used in China, TCMFs are considered as complementary and alternative medicines in many Western countries, mainly due to their complex chemical compositions, unclear effective material basis and action mechanisms, and unstable quality. Hence, more effort should be devoted to in-depth characterization of the chemome of TCMFs to interpret their clinical effects and to establish a comprehensive quality control method to ensure their stable clinical efficacy.
Ultra-performance liquid chromatography (UPLC) coupled with tandem mass spectrometry (MS/MS), e.g., quadrupole-time of flight MS (Q-TOF-MS) or hybrid triple quadrupole-linear ion trap MS (Qtrap-MS), has been a work horse for the measurement of complex TCMFs because of its superiority in terms of separation efficiency, detection sensitivity, and structural characterization potency 2,3 . Q-TOF-MS has been shown to be intrinsically capable of comprehensively acquiring accurate mass spectral data based on MS 1 full scan and MS E -based (also known as MS All ) data-independent acquisition (DIA), indicating promising potential for the global chemical profiling of complex matrices [4][5][6] . However, once obtained, it is a complicated task to completely assign the huge dataset yielded from Q-TOF-MS, and it is even more challenging to exactly assign the fragment ion species

Results
Fragmentation rules and DFIs of saponins and flavonoids. Saponins and flavonoids have been identified as the dominant chemical homologues in Ginseng Radix et Rhizoma, Astragali Radix, and Glycyrrhizae Radix et Rhizoma, and thereby serve as the primary chemical classes in BYD. Because attention has been given to the mass fragmentation pathways of ginsenosides, astragalosides, licorice saponins, and flavonoids [20][21][22][23][24][25] , the applicability of those cracking rules archived in the literature were verified in this study by employing several representatives, including nine ginsenosides, four astragalosides, ten licorice saponins, and five flavonoids. Moreover, due to the great convenience provided by DFI filtering 5 for compound searching and chemical identification, these authentic compounds were also employed to summarize the DFIs for the compounds with the above four chemical categories.
A total of 389 compounds, mainly saponins and flavonoids, have been reported from the four single herbs of BYD, and all of them were included to construct an in-house library. DFIs were proposed for all chemical subtypes based on the fragmentation described above (see Supplementary Fig. S5 Supplementary Fig. S5), and the neutral losses of 120 u and 90 u served as the diagnostic cleavages for flavonoid C-glycosides.
Integrated strategy for the comprehensive chemical characterization of BYD. The ingredients in a given matrix can be broadly sub-divided into primary and minor components 2 . The primary components usually afford significant LC-MS response, whereas the minor components suffer from extensive co-elution with the primary components and insufficient sensitivity of the adopted method 33 . Currently, in-depth profiling the primary constituents is a laborious and time-consuming task, let alone the minor components. Therefore, a systematic strategy was proposed to rapidly and comprehensively screen the chemical constituents in BYD. Firstly, an in-house library that covers most primary components in BYD was constructed, and UNIFI software and Q-TOF-MS were combined to perform automated data mining and structural assignment of the primary constituents. Secondly, several sensitive IDA-mediated methods were applied to the Qtrap-MS domain to extract information belonging to the minor constituents when they were co-eluted with the primary ones, and the mass fragmentation pattern-assisted structural identification was performed by integrating the low-resolution and high-resolution mass spectral information obtained from Qtrap-MS and Q-TOF-MS, respectively.

Automated identification of major components.
A versatile data process platform, UNIFI software, was used for the automated processing of the dataset acquired by MS E mode of UPLC/Q-TOF-MS with the assistance of an in-house compound library. Because parameter setting plays a pivotal role in processing outcomes 12 , the parameters were carefully validated in terms of the accuracy and comprehensiveness of the detection results. The intensity threshold was set at 100cps as a compromise to improve the detection sensitivity while avoiding false positive detection. Regarding peak assignment, the candidate mass-to-charge ratios were automatically matched with the information recorded in the library via three important parameters, mass tolerance, adducts/ pseudo-molecular ions, and fragment ions, and a candidate compound list was directly outputted. The wide mass tolerance could lead to a higher identification rate, resulting in a higher false detection rate. Thus, 5 ppm was set as a compromise based on the generally acceptable mass error range for accurate analysis. The deprotonated molecular ions ([M− H] − ) together with the adduct ions ([M + HCOO] − and [M + Cl] − ) were automatically considered to enhance the selectivity (see Supplementary Fig. S1 and Fig. S3). Attention was also paid to the fragment ion mapping to estimate the rationality of the candidate compounds, which could allow the fragment ions to be automatically recognized and marked with blue tags in the MS/MS spectra (see Supplementary Fig. S1 and Fig. S3). The reliability and accuracy of UNIFI for the automated detection and identification of primary compounds in BYD were demonstrated by analyzing 49 authentic compounds, including 32 saponins, 14 flavonoids, and 3 diterpenes, that were isolated from BYD or its constituent herbs. All 49 reference compounds were rapidly and accurately captured by UNIFI. Following the UNIFI-mediated data processing, 113 compounds (see Fig. 2 and Table 1) were rapidly detected and identified, including 37 ginsenosides, 10 astragalosides, 24 licorice saponins, 28 flavonoids, 6 procyanidins, 4 lignans, and 4 diterpenes.

Minor components characterization. The detection of minor components was performed by combining
Qtrap-MS and Q-TOF-MS. Because comparable sensitivity has been demonstrated between these two analytical platforms 34,35 , most of the components found by various modes of Qtrap-MS were included in the dataset from Q-TOF-MS. Therefore, a workflow was designed to detect and identify the minor components, mainly saponins and flavonoids, in BYD using these two techniques. Firstly, the mass spectral information of the paired precursor-to-product ions afforded by Qtrap-MS guided the extraction of the corresponding ion information by Q-TOF-MS. Then, the proposed DFIs and fragmentation rules, in particular neutral loss (NL) and RDA reactions, were introduced for structural identification.
Saponin-and flavonoid-focused compound screening by Qtrap-MS. As a complementary tool for Q-TOF-MS, Qtrap-MS is advantageous for highlighting the distribution of certain chemical homologues in complex matrices using some targeted screening methods. Based on the aforementioned mass fragmentation patterns of saponins and flavonoids, several survey experiments, such as Prec, pMRM, and MIM 36,37 , were applied to search for saponins and flavonoids in BYD, and an EPI scan was triggered to generate the MS/MS spectra.
The pMRM and MIM modes were combined to screen the saponins in BYD. The pMRM was carried out following a previously published procedure 38 Table 1 and Table S1. All the saponins found with UNIFI were also detected using this method.
The flavonoids in BYD can be divided into aglycones, O-glycosides, and C-glycosides, and different scan modes were integrated to comprehensively detect the flavonoids. Firstly, a stepped MIM scan was used to record the potential flavonoid aglycones. The minimum molecular weight of the flavonoid skeleton is 222 u, and the molecular weight of a natural flavonoid should be at least 238 u due to the substitution of at least one hydroxy group 39 . Therefore, the mass range was set to m/z 237-401, corresponding to the substitution of at least one hydroxy group and at most six methoxy groups 39 . Consequently, signals at m/z 253, 255, 267, 269, 271, 283, 285, 289, 299, and 301 were revealed for the aglycones. Then, the aglycone ions were utilized to screen for flavonoid O-glycosides using Prec scan mode, and MIM mode from m/z 401-727 was used for flavonoid C-glycoside screening because C-glycosides contain at most two sugar substituents (2 × 162 u) 40 . In total, 12 flavonoid aglycones, 41 flavonoid O-glycosides, and 3 flavonoid C-glycosides were detected, and the fragment information obtained from EPI was carefully assigned to the corresponding precursor ions. Similar to the saponins, the flavonoids identified by UNIFI were also detected by Qtrap-MS.
Structural identification of minor saponins. Following the introduction of the mass spectral information obtained from Qtrap-MS to the Q-TOF-MS dataset, accurate MS and MS/MS data were assigned to their corresponding compounds, and structural identification was performed. Among the detected saponins, 35 minor saponins were readily assigned as ginsenosides based on their observed aglycone ions, following the manual identification workflow (Table 1). Moreover, the successive neutral losses assisted in characterizing the glycan chain, such as cleavages of 162 u, 146 u, and 132 u corresponding to glucosyl, rhamnosyl, and arabinosyl or xylosyl residues, respectively. For example, S PG -17 was easily elucidated as a PPT-type ginsenoside from the observed [A− H] − ion at m/z 475.379, and the fragment ions at m/z 799.485 and 637.432 corresponding to the neutral dissociations of one and two glucosyl residues; hence, it was identified as an isomer of Rg1. A total of 32 minor OTSs were rapidly classified and identified according to the summarized DFIs (Table 1). For instance, the [M− H] − ion of S GU -13 was observed at m/z 895.396 corresponding to a molecular formula of C 44 H 64 O 19 . The dominant fragment ion at m/z 351.057 suggested that two glucuronosyl moieties exist in the structure of S GU -13. By matching with the in-house library, S GU -13 was tentatively deduced as an isomer of uralsaponin F. The characteristic ion at m/z 497.115 in the MS/MS spectra of S GU -16, S GU -20, and S GU -29 indicates the possible presence of a GlcA-GlcA-Rha chain in these OTSs 41 . Moreover, seven OTSs that contain single glucuronosyl moiety were also detected and putatively identified by the NL of 176.033 u.
Structural identification of minor flavonoids. Based on our preliminary studies, the flavonoids in BYD can be structurally divided into seven sub-types (see Supplementary Fig. S5), including flavones, flavanones, isoflavones, chalcones, flavans, isoflavans, and pterocarpans. It was difficult to distinguish the aglycone skeleton types merely by the accurate mass data due to the wide occurrence of isomers. Thus, seven types of flavonoids were further sorted into four groups according to their specific fragmentation rules. Chalcones can be easily transformed to flavanones when encountering a high CE 39    Herein, the FA-1-related flavonoids were adopted to illustrate the structural characterization process. A total of 30 compounds were detected as liquiritigenin or isoliquiritigenin derivatives according to the prominent aglycone ion at m/z 255.066 and the 1,3 A − and 1,3 B − ions at m/z 135.016 and 119.058, respectively. Among them, six compounds were unambiguously verified by comparing with reference standards (F GU -30, 31, 59, 65, 66, and 83), whereas the identities of the other compounds were tentatively assigned by comparing with the data in the literature.
The sources of the components detected in BYD were proved by parallel measurement of the single herbal medicines (see Table 1 and Supplementary Fig. S6).
Quantitative and semi-quantitative analysis of the detected components. sMRM mode is superior to the common MRM mode when a large number of ion transitions are involved in the quantitative analysis; thus, it was introduced in the present study to simultaneously monitor 175 compounds detected under the quantitation condition. The detailed parameters, including ion transitions, corresponding t R , and optimal DPs and CEs for the 175 targeted analytes are listed in Table S2. Based on the comprehensive semi-quantitative analysis of BYD by sMRM mode, 36 representative primary components of them, including 11 flavonoids and 25 saponins, were selected for simultaneous, absolute quantitative analysis. The representative chromatograms of BYD and the extracted ion chromatogram (EIC) of the mixed standards are shown in Fig. 3. All 36 analytes showed good linear regression (r 2 > 0.999) within the test ranges. The LODs of the compounds were 0.04-23.21 ng/mL, and the LOQs were 0.17-55.25 ng/mL. These data are summarized in Table S3, indicating that sMRM is sensitive enough to quantitatively determine the large-scale analytes. The relative standard deviation (RSD) values of the intra-and inter-day precision studies were less than 5.23% (Table S4), indicating that the developed method exhibits satisfactory precision. The recoveries were between 90.68% and 108.92%, with RSDs less than 10%, which meets the quantitative criteria for multi-analytes in complex matrices. Satisfactory repeatability was demonstrated by RSDs of less than 5.01% for all the analytes, and the results of the stability assay suggested that the samples remained stable during measurement. The developed sMRM method was then applied to the simultaneous quantification of 36 analytes in six repeated batches of BYD extracts, and the data are shown in Table 2.

Discussion
The efficacy and safety of TCMFs have been demonstrated by the long application history in China and other East Asian countries, such as Korea and Japan. It remains a great challenge to comprehensively understand the chemical composition of TCMFs, although great efforts have been devoted and some state-of-the-art analytical platforms, such as UPLC-Q-TOF-MS and UPLC-Qtrap-MS have been introduced. Attention has been given to the chemical fingerprinting of Ginseng Radix, Astragali Radix, Glycyrrhizae Radix, and Cinnamomi Cortex; however, the chemical composition of BYD has not been thoroughly studied because the decoction process might generate new chemical components through complex chemical reactions 42 . In addition, the contents of certain compounds cannot be calculated based on using the mixture ratio of single herbs in a TCMF because drug-drug interactions could occur during the decoction process 43,44 . For instance, the content of isoflavonoids and astragalosides in BYD was significantly greater than those in the single herbs based on direct comparison of the peak areas; some new compounds, such as ginsenosides S PG -1 and S PG -24, along with licorice saponins S GU -2, S GU -25, and S GU -39, were revealed in BYD, which are found in trace amounts in Ginseng Radix and Glycyrrhizae Radix but in higher amounts in BYD. Therefore, it is critical and necessary to characterize the chemical profile of TCMFs, even if the constituent herbs have been well defined because the chemical profile of a TCMF cannot be determined by simply pooling all the components from the single herbs. Because it is a labor-intensive and time-consuming task to assess the quality of TCMFs by comprehensive characterization of their chemical profiles, it is usually feasible to conduct quality control of TCMFs by monitoring tens of components 45,46 . Although all components captured by UNIFI could be mined by manual data processing, the software has two attractive advantages compared with conventional chemical profiling workflows. First and foremost, the software is sufficiently versatile so that fully automated data processing can be achieved, and all candidate compounds are directly listed following the construction of an in-house library and the setting of the optimum parameters. Only 10 min is required for UNIFI to complete the analysis. Secondly, fragment ion matching can be adopted to improve the accuracy of UNIFI based on the predictive fragmentation pathways. For example, ginsenoside Re and liquiritin (see Supplementary Figs S1 and S3) were identified by matching not only their molecular ions but also the MS/MS fragment species with the information summarized in the library. Therefore, UNIFI provided a simple, efficient, and accurate method for primary component detection and identification of BYD, indicating a promising option for the chemical analysis and quality control of TCMFs, which are critical to ensure the efficacy and safety of TCMFs.
It may be as important to detect and identify the minor components as it is to detect and identify the primary components when attempting to comprehensively understand the chemical composition of TCMFs. In most cases, the characterization of the minor constituents suffers from their co-elution with the major components and from the insufficient sensitivity of the established method. Despite being useful for the detection and identification of primary compounds, UNIFI extensively neglects minor compounds. The MS and MS/MS signal intensities of the compounds in BYD span three orders of magnitude, resulting in the signals belonging to the minor components being easily submerged by their co-eluting primary components. Therefore, IDA using Qtrap-MS was introduced as a complementary method for MS E by Q-TOF-MS to provide a deeper data mining of the MS E dataset. The Q3 cell of the Qtrap-MS enables rapid switching between conventional radiofrequency/ arc (RF/DC) resolving quadrupole mass filter to perform sensitive MRM or NL scanning and linear ion trap (LIT) apparatus to perform EPI scanning 47 . In particular, the scan rate of the LIT of 20000 Da/s could fulfill the demands of acquisition of high-quality MS/MS spectral data for all precursor ions that pass the IDA threshold.  Table 2.
Scientific RepoRts | 6:38379 | DOI: 10.1038/srep38379 Although great sensitivity can be obtained by the MS E mode in Q-TOF-MS, all co-eluted precursor ions simultaneously rush into the collision cell to generate fragment ion species which are further transmitted to the TOF chamber of the Q-TOF-MS at the same time due to the wide pass Q1 mode 48 . Therefore, the fragment species of all co-eluted compounds share a single MS/MS spectrum and the exact pairing of precursor ions with the corresponding product ion cannot be achieved. Alternatively, the narrow-pass Q1 mode is normally employed for IDA, which affords MS/MS spectra with minimal interference because only precursor ions meeting the preset criteria within the selected narrow m/z window (0.6-0.8 Da wide for unit resolution) are transferred to the collision cell to generate product ions. In other words, separate acquisition of the MS/MS spectra is theoretically guaranteed for all Q1 signals detected by pMRM/MIM/Prec mode. Therefore, IDA mode provides useful guidance for the assignment of accurate mass spectral data from sophisticated Q-TOF-MS chromatograms under MS E mode. In the present study, the MIM and pMRM scanning methods were developed to screen potential saponins based on the mass spectrometric behavior obtained with the assistance of several authentic compounds, and the detection of 67 minor saponins, including 35    their respective precursor ions in the high-resolution MS spectrum and analyzing all ion species using the proposed fragmentation patterns, the six co-eluted compounds were identified as yunganoside I2 or licorice saponin B2, uralsaponin P, uralsaponin U or uralsaponin N, ginsenoside Ro, ginsenoside Rc, and ginsenoside Ra1. By combining the rapid and automated identification by UNIFI, the sensitive targeted detection by Qtrap-MS, and the accurate mass measurements by Q-TOF-MS, an LC-MS-based qualitative analysis strategy consisting of two progressive steps was proposed for the rapid, accurate, and global chemical profiling of BYD. The cracking rules and DFIs of the primary chemical homologues in BYD were proposed by employing several representatives, and the mass spectral patterns assisted the structural identification of flavonoids and saponins. For the first step, 113 major components were rapidly identified by automated data-processing with UNIFI software in approximately ten minutes. In particular, the identities of 49 components were confirmed by comparison with the reference standards. In the second step, as many as 123 minor compounds (mainly saponins and flavonoids) were systematically detected from BYD by multiple screening methods based on UPLC/Qtrap-MS and were putatively identified by cross-talking between Qtrap-MS and Q-TOF-MS. Twenty of the compounds were identified as possibly new compounds, including seven licorice saponins (S GU -2, S GU -5, S GU -14, S GU -25, S GU -27, S GU -39, S GU -52), seven ginsenosides (S PG -1, S PG -21, S PG -23, S PG -24, S PG -27, S PG -36, S PG -58), and six flavonoids (F AM -51, F AM -55, F GU -14, F GU -15, F GU -32, F GU -74). Altogether, 236 compounds were identified from BYD, including 139 saponins, 83 flavonoids, 6 procyanidins, 4 lignans, and 4 diterpenes. Furthermore, the quantitation of 36 primary compounds and the relative quantification of 139 compounds were performed by sMRM using Qtrap-MS for the quality control of BYD.
These findings systematically illustrated the comprehensive chemical composition of BYD and provided the valuable evidences for clarification of the therapeutic material basis and action mechanism of this formula. Saponins and flavonoids were disclosed to be the main components in BYD, and many of them have been reported to have a verity of pharmacological activities on cardiovascular system, which is the main clinical application of BYD. For example, ginsenosides, including Re, Rb1, and Rg1, have the ability to protect the myocardia against injury produced by ischemia and reperfusion 49 ; astragaloside IV, one of the main active ingredients in Astragali Radix, has the functions of vasodilating effect and protecting the vascular endothelial cells 50 ; calycosin has the protective action against cardiac injury 51 ; glycyrrhizin was identified as a thrombin inhibitor in vitro and in vivo 52 ; isoliquiritigenin and isoliquiritin, two main active flavonoids of licorice, were reported to have a vasorelaxant effect and be able to decrease the tube formation in vascular endothelial cells 53,54 .
In conclusion, the integrated LC-MS-based strategy provided a meaningful and practical workflow for the rapid, accurate, and comprehensive identification and quantitation of the complicated TCMFs, which will supply valuable references for the further interpretation of their clinical effects, action mechanism, and quality control.

Methods
Materials and reagents. All crude materials were collected from a TCM market (Anguo, Hebei, China).
Acetonitrile (ACN) and MeOH of LC-MS grade were obtained from Merck (Darmstadt, Germany). LC-MS grade formic acid was obtained from Sigma-Aldrich (Steinheim, Germany). Deionized water was prepared on a Millipore Milli-Q water purification system (Billerica, MA, USA).

Sample preparation.
Pulverized crude materials consisting of Ginseng Radix et Rhizoma (10 g), Astragali Radix (30 g), Glycyrrhizae Radix et Rhizoma Praeparata Cum Melle (10 g), and Cinnamomi Cortex (5 g) were immersed in 550 mL of deionized water for 1 h and were then heated under reflux for 1.5 h two times. Both extractants were combined, filtered, and freeze-dried into powder. Accurately weighed lyophilized powder (0.2 g) was thoroughly suspended in ten volumes of deionized water. After centrifugation at 9,600 rpm for 10 min, a 500 μ L aliquot of the supernatant was loaded onto a preconditioned Phenomenex Strata-X SPE column (500 mg/5 mL, Torrance, CA, USA) and successively eluted with 6 mL of water and 6 mL of MeOH. The MeOH effluent was concentrated to dryness under reduced pressure, reconstituted in 1 mL of MeOH, and filtered through a 0.22-μ m membrane prior to LC-MS analysis. The injection volume was 0.6 μ L. Additionally, extract samples of the four constituent herbs of BYD were prepared in parallel.
A stock solution (1 mg/mL) of each reference sample was obtained by dissolving accurately weighed compound in MeOH. All solutions were maintained at − 20 °C prior to use. Leucine-enkephalin was used as the lock mass compound for accurate mass calibration. All MS E data were acquired with negative polarity. The ion-source parameters were set as follows: source temperature, 110 °C; desolvation temperature, 450 °C; desolvation gas (N 2 ), 650 L/h, and nebulizer gas, 20 L/h. Two separate runs were conducted using the optimal compound-dependent parameters for flavonoids and saponins. For the former, parameters were set as follows: the capillary voltage, − 2.03 kV; cone voltage, − 10 V; collision energy (CE), − 6 eV for MS and − 15 to −55 eV for MS/MS, respectively; and MS 1 scan range, m/z 237-731. Regarding the latter, parameters were applied as follows: capillary voltage, − 2.3 kV; cone voltage, − 30 V; CE, − 6 eV for MS and − 30 to − 70 eV for MS/MS, respectively; and MS 1 scan range, m/z 441-1251.
Post-acquisition data processing was automatically performed by UNIFI software (v.1.6.0, Waters), and MassLynx 4.1 software (Waters) was utilized for minor compound identification. An in-house library that contained the molecular formulae, molecular weights, and chemical structures of 389 compounds that were previously isolated from Ginseng Radix et Rhizoma (75 compounds), Astragali Radix (109 compounds), Glycyrrhiza Radix et Rhizoma (162 compounds), and Cinnamomi Cortex (43 compounds) was constructed to assist the chemical identification. Moreover, the retention times and mass spectral information, particular the DFIs of the authentic compounds, were also included in the in-house library.

UPLC/Qtrap-MS conditions for qualitative analysis. A Waters ACQUITY H-Class UPLC system
(Milford, MA, USA) was connected online with an ABSciex 4500 Qtrap mass spectrometer (Foster City, CA, USA) via an ESI interface. The chromatographic separation program was identical to the method described above. An auto-sampler was responsible for triggering the mass spectrometer via a pulse signal.
In the mass domain, the ion source parameters were maintained as follows: polarity, negative; ion spray voltage, − 4500 V; source temperature, 550 °C; curtain gas (CUR), 35 psi; ion source gas 1 (GS1), 55 psi; ion source gas 2 (GS2), 55 psi. The data acquisition and processing were performed by ABSciex Analyst 1.6.2 software. Some survey experiments, including pMRM mode, MIM mode, and Prec scan, were adopted to trigger EPI scans in the linear ion trap cell (scan rate, 20000 Da/s) through an IDA procedure to search for saponins and flavonoids.
The scanning programs for saponins were as follows: pMRM-EPI. pMRM-EPI procedures in the literature were used with minor modifications 57 . Two separate runs were performed with the mass ranges of m/z 441.5-843.5 and m/z 843.5-1255.5 for Q1. The dwell time was set to 8 ms for each ion transition. The IDA threshold and the CE of EPI were set to 500 cps and − 65 ± 20 eV, respectively.

MIM-EPI.
Stepped MIM-EPI protocols developed in our previous study were implemented with minor modifications 58  Six batches of BYD lyophilized powders were prepared using the procedure described above. The accurately weighed lyophilized powders of each batch (0.2 g) were thoroughly suspended in ten volumes of 5% aqueous ACN. After centrifugation at 9,600 rpm for 10 min, the supernatant was filtered through a 0.22 μ m membrane prior to LC-MS analysis. The injection volume and other LC conditions were set as previously described.
Each of the 36 analytes was prepared at a concentration of approximately 100 ng/mL with 50% aqueous ACN. They were directly infused into the ESI interface to investigate their optimal mass spectrometric parameters, including DPs and CEs. The ion transitions, corresponding t R , and optimal DPs and CEs of the sMRM scan mode are shown in Table S3. The MRM detection window for each ion pair was set to 1.0 min, and the target scan time was set to 1.0s.

Preparation of stock and working solutions.
To improve the quantitation precision and repeatability, baicalin was chosen as the internal standard (IS) for the determination of 11 flavonoids, and tenuifolin was used as the IS for 25 saponins (Table S2).

Calibration curves.
A mixed solution containing all 36 references was diluted to the appropriate concentrations using 50% aqueous ACN to construct calibration curves. At least six concentrations of the solution were Scientific RepoRts | 6:38379 | DOI: 10.1038/srep38379 analyzed in duplicate, and then calibration curves were generated to confirm the linearity between the ratio of the peak areas (analyte/IS) and the concentrations of the 36 analytes.
Assay validation of the scheduled MRM. The stock solutions were diluted to a series of appropriate concentrations with 50% aqueous methanol and were then injected into the LC-MS for analysis. The LODs and LOQs were determined as signal-to-noise ratios (S/N) of approximately 3 and 10, respectively. The repeatability of the method was determined by analyzing six replicates of a BYD sample and is represented as the relative standard deviation (RSD) of the content of each analyte. The intra-and inter-day variations were used to analyze the precision of the established method. For the intra-day variability test, six replicates of the same solution were analyzed on a single day, while for the inter-day variability test, the same solution was examined in triplicate on three consecutive days. The variations are expressed as the RSDs of the data. Stability tests were performed by analyzing the BYD sample solution over a period of 0 h, 2 h, 4 h, 8 h, 10 h, 12 h, and the RSD was used to evaluate the stability. Recovery tests were conducted on samples spiked with approximately 100% of known amounts of the analytes, with six replicates for each sample.