Next Article in Journal
Macroporous Poly(hydromethylsiloxane) Networks as Precursors to Hybrid Ceramics (Ceramers) for Deposition of Palladium Catalysts
Next Article in Special Issue
Virtual Instruments for Peak-Overlapping Studies to Determine Low- and High-Concentration Components with Ion Chromatography: Potassium and Sodium
Previous Article in Journal
The Role of –OEt Substituents in Molybdenum-Assisted Pentathiepine Formation—Access to Diversely Functionalized Azines
Previous Article in Special Issue
Approximated Uncertainty Propagation of Correlated Independent Variables Using the Ordinary Least Squares Estimator
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Molecules
Volume 29
Issue 16
10.3390/molecules29163807
molecules-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics

by
Xianrui Wang
1,2,
Jiating Zhang
1,2,†,
Fangliang He
1,3,†,
Wenguang Jing
1,2,
Minghua Li
1,2,
Xiaohan Guo
1,2,
Xianlong Cheng
1,2,* and
Feng Wei
1,2,*
1
Institute for Control of Traditional Chinese Medicine and Ethnic Medicine, National Institutes for Food and Drug Control, Beijing 102629, China
2
State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control, Beijing 102629, China
3
Institute for College of Traditional Chinese Medicine, China Pharmaceutical University, Nanjing 211198, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Molecules 2024, 29(16), 3807; https://doi.org/10.3390/molecules29163807
Submission received: 7 July 2024 / Revised: 7 August 2024 / Accepted: 9 August 2024 / Published: 11 August 2024
(This article belongs to the Special Issue Chemometrics Tools in Analytical Chemistry 2.0)
Browse Figures
Versions Notes

Abstract

:
Background: Periplocae Cortex (PC), Acanthopanacis Cortex (AC), and Lycii Cortex (LC), as traditional Chinese medicines, are all dried root bark, presented in a roll, light and brittle, easy to break, have a fragrant scent, etc. Due to their similar appearances, it is tough to distinguish them, and they are often confused and adulterated in markets and clinical applications. To realize the identification and quality control of three herbs, in this paper, Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry Expression (UHPLC-QTOF-MSE) combined with chemometric analysis was used to explore the different chemical compositions. Methods: LC, AC, and PC were analyzed by UHPLC-QTOF-MSE, and the quantized MS data combined with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to explore the different chemical compositions with Variable Importance Projection (VIP) > 1.0. Further, the different chemical compositions were identified according to the chemical standard substances, related literature, and databases. Results: AC, PC, and LC can be obviously distinguished in PCA and PLS-DA analysis with the VIP of 2661 ions > 1.0. We preliminarily identified 17 differential chemical constituents in AC, PC, and LC with significant differences (p < 0.01) and VIP > 1.0; for example, Lycium B and Periploside H2 are LC and PC’s proprietary ingredients, respectively, and 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside C, and 3,5-Di-O-caffeoylquinic acid are the shared components of the three herbs. Conclusions: UHPLC-QTOF-MSE combined with chemometric analysis is conducive to exploring the differential chemical compositions of three herbs. Moreover, the proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can be set to enhance the quality control of LC, PC, and AC.

1. Introduction

The dried root bark of Acanthopanax gracilistylus W. W. Smith, a plant in the Araliaceae family, is known as Acanthopanacis Cortex (AC, Wu Jia Pi) [1,2]. It is believed to dispel wind and dampness, nourish the liver and kidneys, and strengthen tendons and bones [2]. Clinical practice treats rheumatic diseases, weak tendons and bones, physical weakness and fatigue, edema, and beriberi [3]. Recent research showed that as a potential new photosensitizer of head and neck squamous cell carcinoma, AC can upregulate the expression of Bax protein and Poly ADP-Ribose Polymerase-1-protein (PARP-1), further enhance the photodynamic therapy (PDT) effect, induce reactive oxygen species (ROS) production, and trigger the apoptosis pathway [4]. Periplocae Cortex (PC, Xiang Jia Pi), referring to the dried root bark of Periploca sepium Bge., has the effects of diuresis, reducing swelling, dispelling wind and dampness, and strengthening tendons and bones [2,5]. It is used clinically for lower limb edema, palpitations, shortness of breath, wind-cold-dampness bi syndrome, lumbosacral soreness, and weakness [5]. In addition, it has been proven that the chemical constituents contained in PC can effectively alleviate colonic inflammation, improve the intestinal epithelial barrier function, and prevent the occurrence of colitis and colitis-related tumors [6]. The Lycii Cortex (LC, Di Gu Pi) is the dried root bark of Lycium Chinese Mill of Solanaceae or Lycium barbarum L of Ningxia [7]. Its efficacy is mainly reflected in cooling the blood, removing steaming, clearing the lungs, and reducing internal heat. Therefore, it is commonly used in treating yin deficiency with tidal heat, bone steaming and night sweats, lung heat, cough, hemoptysis, nosebleeds, and internal heat and thirst [8,9]. LC, PC, and AC are all included in the 2020 edition of Chinese Pharmacopoeia. Their medicinal parts are all dried root bark, presented in a roll, single or double rolled, light and brittle, easy to break, and have a fragrant scent, and their cork layer has multiple rows of cells and contains starch granules. The three herbs have similar traits, making them readily confusing in markets and clinical applications. For example, many people mistake PC and LC for AC, and PC is utilized as AC or blended into AC for treatment. However, their clinical pharmacological actions differ from each other, and PC has toxicity. So, if the clinical application is inappropriate, it may directly threaten the patient’s life, resulting in irreparable damage. Therefore, to protect people’s lives, strengthen the market supervision and quality control of AC, PC, and LC, and avoid confusion about the use of the three herbs, it is essential to make use of modern scientific and technological methods to carry out the analysis of AC, PC, and LC.
To this end, numerous research studies have been conducted by various researchers from different perspectives. For example, using plant metabolomics and network pharmacology techniques, Li ZT et al. investigated and identified nine potential quality marker components of PC. One of these components is 4-methoxy benzaldehyde-2-O-β-d-xylopyranosyl-(1→6)-β-d-glucopyranoside, which can be used to differentiate PC harvested during the spring–autumn or summer seasons [10]. Zhang JX et al. established a rapid HPLC-ESI-MS method for the analysis of 24 components in LC, including 11 phenolic compounds, nine phenolic amides, and four cyclic peptides, of which cyclic peptides and phenolic amides were not only the abundant constituents but also the characteristic components for LC to be distinguished from the adulterants, and cyclic peptide was considered a chemical marker to distinguish LC from ones from different geographical regions [11]. Sun L et al. used an electronic nose (E-nose) to explore the difference in scent information between PC and AC and distinguish them quickly and reliably. Meanwhile, the volatile components of these two herbs were detected by gas chromatography-mass spectrometry (GC-MS), and the results showed that 24 volatile components, such as 2,4-divert-butyl phenol, dodecane, and so on, can be used as chemical marker components to distinguish PC and AC [12]. The above research has contributed to identifying and analyzing the PC, AC, and LC to a certain extent, providing scientific evidence to avoid confusion and misuse. However, it is undeniable that there are still some limitations: (1) The above analysis often focused on one or two of the LC, PC, and AC and failed to place the three traditional Chinese medicines into a unified analysis system at the same time, which means the analysis results were relatively one-sided, and (2) due to the poor stability of volatile components, the volatile components in LC, PC, and AC would be lost in large quantities after being stored for some time. Therefore, the research of taking volatile components as the breakthrough point only applied to the analysis of fresh herbal medicines rather than the long-stored Chinese herbal medicines, making it difficult to be representative.
Given the current analyses’ shortcomings, the non-volatile components were taken as the breakthrough point in this paper. LC, PC, and AC, stored for different periods, were selected as research objects to avoid the influence of unstable, volatile components and improve the representativeness of research and analysis. Furthermore, considering that UHPLC-QTOF-MSE has the advantages of high separation efficiency, high resolution, and high sensitivity, it is an excellent method for component analysis, exploration of action mechanisms, and non-targeted identification of complex traditional Chinese medicines [13,14,15,16,17,18,19]. So, in this paper, the PC, LC, and AC samples were all analyzed by UHPLC-QTOF-MSE and brought into a unified analysis system. The overall route of this research is shown in Figure 1. Firstly, the UHPLC-QTOF-MSE technology was utilized to analyze PC, LC, and AC under the unified analysis conditions. Secondly, the Progenesis QI software (2.3 version) was used to perform peak position calibration and digitize the mass chromatography [20]. Then, the differential chemical components were explored combined with chemometric analysis in the SIMCA 14.1 software. Finally, according to the chemical standard substances, related literature, and databases, the differential chemical components of PC, LC, and AC were identified.

2. Results

2.1. The Results of UHPLC-QTOF-MSE Analysis

In UHPLC-QTOF-MSE analysis, we finally obtained the mass spectrometry information for chemical standard substances, which can be obtained from Figure S1, and the base-peak chromatogram of AC, PC, and LC is shown in Figure 2. The blank methanol had no obvious interference on the detection of the three herbs. AC, PC, and LC present different chromatograms, which suggests that the three herbs do have different chemical compositions. It laid a foundation for us to explore the differential chemical constituents of the three herbs. On the other hand, if our research were only based on the UHPLC-QTOF-MSE analysis, it would be difficult to establish the characteristic relationship between chemical constituents and the three Chinese medicines, not to mention to explore different chemical compositions. Therefore, it was a good choice to combine the UHPLC-QTOF-MSE data with chemometric analysis.

2.2. The Results of Chemometric Analysis

The quantized mass spectrometry data were normalized by unit variance scaling (UV); then, PCA analysis was carried out. The score plot and loading scatter plot of PCA analysis are shown in Figure 3 and Figure 4. LC, PC, and AC can be distinguished from each other effectively and clearly. At the same time, the cumulative interpretation rate (R2X [6]) of six principal components (PCs) is 0.964, and the cumulative prediction rate (Q2) is 0.937 [21,22]. On the other hand, the load and score plots are complementary illustrations of each other. The position of a sample in a given direction on the score plot is affected by a variable in the same direction on the load plot. In Figure 4, each point represents a variable whose position shows the importance of that variable in the principal component construction. Variables closer to the origin of the axes contribute less to the principal component, while variables further from the origin are more important [22]. For example, as shown in Figure 4, the red dots of 6.73 min_475.1785 m/z, 14.71 min_399.1827 m/z, 16.05 min_737.3172 m/z, 19.54 min_727.4019 m/z, etc., contribute less to PC1 and PC2, which is not conducive to the distinction between LC, PC, and AC. At the same time, the blue dots of 4.96 min_593.1910 m/z, 5.57 min_585.2092 m/z, 6.61 min_323.1269 m/z, 6.87_874.3739 m/z, 8.04 min_897.3897 m/z, 10.14 min_255.0869 m/z, etc., make great contributions to PC1 and PC2 and are negatively correlated with PC1 but positively correlated with PC2, which helps to distinguish AC, PC, and LC. Therefore, in differential chemical components analysis of PC, LC, and AC, we should pay more attention to the data points that are far from the origin of the coordinates.
Further, to explore the differential chemical components responsible for distinguishing AC, PC, and LC, the quantized mass spectrometry data were normalized by Pareto scaling (Par) to execute PLS-DA analysis. The score plot and loading scatter plot of PLS-DA analysis are shown in Figure 5 and Figure 6. LC, PC, and AC can also be clearly distinguished in supervised PLS-DA analysis, with the cumulative prediction rate (Q2) being 0.998. On the other hand, due to the PLS-DA model having the risk of overfitting, we further used 200 permutation tests and cross-validation analysis (CV-ANOVA) to assess whether the PLS-DA model was overfitting. The results of model validation show that the PLS-DA model is not overfitting with p = 3.85 × 10−37 < 0.01 [23,24,25,26].
Moreover, VIP was used to find component ions contributing to distinguishing LC, PC, and AC in the PLS-DA model. VIP > 1.0 is often considered a commonly used criterion for screening differential components [27,28].
There were 2661 ions whose VIP values are more significant than 1.0. Based on the chemical standard substances, Waters UNIFI database, HMDB database, relevant literature reports, and our self-built database, we preliminarily identified the differential chemical constituents [29,30,31]. For example, the adduct ion of [M+H]+ of compound A was m/z = 897.3897, which showed that the molecular composition of compound A was C44H52N10O11. Moreover, five main fragments at m/z 879.3695 (C44H51N10O10), m/z 689.3059 (C34H41N8O8), m/z 503.2247 (C23H31N6O7), m/z 395.1719 (C21H23N4O4), and m/z 159.0917 (C7H15N2O2) were presented in the secondary mass spectrometry (MS2) to prove the identification analysis. After comparing with “Lyciumin B” of the chemical standard substances, Waters UNIFI database, the HMDB databases, and our self-built database, compound A was confirmed to be 11-(hydroxymethyl)-2-[[3-(1H-indol-3-yl)-2-[[1-(5-oxopyrrolidine-2-carbonyl)pyrrolidine-2-carbonyl]amino]propanoyl]amino]-3,6,9,12-tetraoxo-5-propan-2-yl-1,4,7,10,13-pentazatricyclo [14.6.1.017,22]tricosa-16(23),17,19,21-tetraene-14-carboxylic-acid (Lyciumin B) that was also included in the HMDB database, with the characteristic ion of m/z 879.3695 and data deviation being within 3.0 ppm [12,30]. As a cyclic peptide, Lyciumin B contains many easily broken peptide bonds (-CO-NH-), and the part ionic fragments after cleavage of Lyciumin B are shown in Figure 7.
The compound B appeared to be an adduct ion [M+H]+ at m/z = 921.5216, combined with its six secondary fragment ions m/z = 587.3598 (C34H51O8), m/z = 417.2136 (C20H33O9), m/z = 305.1600 (C14H25O7), m/z = 747.4313 (C41H63O12), m/z = 457.2959 (C28H41O5), and m/z = 161.0806 (C7H13O4). Through “Periplocoside C (m/z = 921.5276)” of the chemical standard substances and relevant reference literature comparison, we finally identified the compound C as Periplocoside C, and its accurate molecular formula is C49H76O16 with data deviation being within 2.0 ppm [32,33]. Since ether bonds are easily broken under ESI ionization, Periplocoside C containing multiple ether bonds (-C-O-C-) is usually cleaved into multiple secondary fragment ions. In addition, ether bonds are highly susceptible to free radical reactions. Based on the above analysis, the secondary cleavage ion fragments that assisted in the identification of Periplocoside C are shown in Figure 8.
The adduct ion of [M+H]+ of compound C was m/z = 531.3184, which showed that the molecular composition of compound C was C28H42N4O6. Meanwhile, multiple fragment ions were presented in the MS2, such as m/z = 367.2711 (C19H35N4O3), m/z = 167.0721 (C9H11O3), m/z = 222.1112 (C12H16NO3), and m/z = 123.0441 (C7H7O2). After comparison with “Kukoamine A (m/z = 531.3195)” of the chemical reference substances and database retrieval combined with the attribution of ionic fragments, we identified compound C as Kukoamine A with data deviation within 3.0 ppm [30,34]. The detailed information of part fragment ions is shown in Figure 9.
The adduct ion of [M+Na]+ of compound D was m/z = 499.1244, which showed that the molecular composition of compound D was C23H24O11 with data deviation within 5.6 ppm. At the same time, three fragment ions m/z = 315.0693 (C17H15O6), m/z = 171.1120 (C8H11O4), m/z = 163.0667 (C6H11O5), and m/z = 145.0286 (C6H9O4) were obviously present in the high energy channel to assist in the verification of compound D [35]. Based on the fragment cleavage pattern and UNIFI database comparison, we identified compound D as likely to be 5-Hydroxy-6,7-dimethoxyflavone-4′-O-beta-D-glucopyranoside.
The compound E appeared to be an adduct ion [M+H]+ at m/z = 153.0555 with data deviation within 2.0 ppm, and there were three cleavage ion fragments m/z = 135.0420 (C8H7O2), m/z = 125.0600 (C7H9O2), and 121.0285 (C7H5O2) in the high-energy channel. Based on the comparison with “2-Hydroxy-4-methoxybenzaldehyde (m/z = 153.0563)” of the chemical reference substances and HMDB database, we finally identified compound E as 2-Hydroxy-4-methoxybenzaldehyde [30,32,34].
As shown in Table 1, we preliminary identified 17 differential chemical constituents whose VIP > 1.0 in AC, PC, and LC. More information is detailed in Table S2.
Furthermore, we used a nonparametric rank sum test (data non-normal distribution) to verify whether there is a significant difference for the 17 differential chemical components in LC, AC, and PC. As shown in Table 2, the probability values of the 17 compounds were all < 0.01, which showed that the identified 17 chemical constituents had substantial differences between PC, AC, and LC. For example, Kukoamine A (C28H42N4O6, m/z = 531.3184) and Lyciumin A (C42H51N9O12, m/z = 874.3735), as well as Lyciumin B (C42H51N9O12, m/z = 897.3895), in LC had a higher ionic strength compared to PC and AC (basically at the baseline level), while the Periploside C (C44H52N10O11, m/z = 921.5216) and Periplocoside (C36H56O13, m/z = 719.3613) in PC had a higher ionic strength. In conclusion, we identified 17 differential compounds that were expected to be potential chemical markers for differentiating the PC, AC, and LC.

3. Discussion

3.1. Optimization of Analysis Conditions

Before the formal experiment, we optimized the analysis conditions through the pre-trial. As far as sample pretreatment is concerned, we found that the extraction effect of methanol was better than that of 50% methanol-water and ethyl acetate. As for mass spectrometry, the mass spectrometry information in the positive ion mode was more abundant than in the negative ion mode. The MSE mode was used to collect the mass spectrometry data, in which collision energy will change from low to high energy circularly, thereby ensuring the simultaneous collection of precursor ions and fragment ions and facilitating the subsequent database retrieval and literature comparison. In the investigation of collision energy, it was distinct that the mass spectrometry had the most abundant data information with the collision energy being 10–40 V, rather than 10–60 V or 10–80 V.

3.2. Discussion on Chemometric Analysis

Generally speaking, PCA and PLS-DA are the most commonly used analytical methods to explore the differential chemical components in chemometric analysis [22,37]. Drawing on their successful application in Chinese medicine, we also used PCA and PLS-DA to explore the different chemical compositions of LC, AC, and PC. Moreover, before PCA and PLS-DA analysis, the data need to be normalized; unit variance scaling (UV) and Pareto scaling (Par) are the common normalization methods for PCA and PLS-DA, respectively [12,22,37,38]. In addition, if the PLS-DA model is overfitting, the analysis result will be unreliable. Therefore, permission tests and cross-validation were used to verify the reliability of the PLS-DA model in this paper. In addition, the VIP in the PLS-DA model can help us to quickly lock the most important ions for distinguishing the three herbs from a large amount of complex ion information.

3.3. Discussion on Sample Representativeness

The LC, PC, and AC samples, known as reference herbs, come from the National Institutes for Food and Drug Control. The herbs are high-quality traditional Chinese medicines of different origins, such as Sichuan, Shanxi, and Zhejiang, and they were used as control reference in the process of traditional Chinese medicine testing. Its collection must be strictly standardized for origin and harvesting, and the species and origin are entirely accurate and verified by experts, aligning with Chinese Pharmacopoeia’s quality requirements. Therefore, the samples of different origins and different collection years are representative. Moreover, the analysis found that the mass spectrometry information of the same herbs of different origins and storage time varied, especially those preserved for a longer time with the least mass spectrometry information due to the loss of volatile components [12]. Therefore, based on the representative samples from various sources and with different storage times, we could control the chemical composition differences to the greatest extent and realize accurate chemometric analysis. In addition, all samples were pulverized in the year of collection and were kept in a standard library of control herbs before formal analysis. In summary, the samples are representative to some degree. However, it is undeniable that the number of samples used in this study is indeed small. Still, the samples involve different years and origins, and it takes time to collect them, so it will be necessary to further increase the sample size for analysis and validation in subsequent studies.

3.4. Discussion on Analysis Results

Traditional Chinese medicine is a multi-component system containing thousands of chemical components, with a large amount of chemical information that can reflect the species characteristics of the sample. After performing a chemometric analysis on AC, PC, and LC, we could characterize the chemical composition and species relationship. In this paper, we have identified 17 potential differential chemical components, including unique chemical components and shared chemical components whose content has significant differences. For example, Lyciumin B is the unique component of LC, and Periploside H2 is the unique component of PC. The content of these chemical components will vary with individual differences, but it remains the key chemical marker for identifying and distinguishing the three Chinese medicines. The shared chemical components were present in AC, LC, and PC, but the compositional content, such as N-Feruloyltyramine, varied greatly. Further, considering the representativeness of samples from the National Institutes for Food and Drug Control collected from different producing areas, the 17 potential chemical markers currently identified are representative enough to realize the identification and analysis of AC, LC, and PC. In summary, we can use the 17 potential chemical markers to realize the market supervision and quality control of AC, LC, and PC, and perhaps we can take the following measures: (1) For proprietary chemical components, Lyciumin B and Periploside H2 are the proprietary chemical components of LC and PC, respectively. So we can stipulate that Lyciumin B should not be detected in PC and AC and Periploside H2 should not be detected in LC and AC, etc. (2) For shared chemical components, such as 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside K, Periplocoside C, 3,5-Di-O-caffeoylquinic acid, etc., we can enhance the quality control of LC, PC, and AC by setting content limits to these chemical components. (3) Perhaps multiple ingredients including proprietary and shared chemical components can be used as an “ingredients combination”, and when all of them can be detected, it is deemed that the herb has been detected.

3.5. Research Advantage, Limitation, and Prospect

In this paper, UHPLC-QTOF-MSE combined with chemometric analysis was used to explore the different chemical compositions. Further, 17 differential chemical components were identified according to the chemical standard substances, related literature, and databases. These chemical components may be potential quality control markers to distinguish LC, PC, and AC. It is beneficial to strengthen the quality control and market supervision of LC, PC, and AC. Therefore, in terms of practicality, UHPLC-QTOF-MSE combined with chemometric analysis helps explore the differential chemical components of similar herbs and realize the identification of similar herbs based on differential chemical markers. However, this method has a high cost and low analysis speed. Samples need to be extracted and analyzed by personnel with the necessary professional knowledge. Moreover, as mentioned before, although the samples are representative to some extent, the number of samples is small; so, it is necessary to increase the sample size for analysis and verification in the future. In addition, from the point of view of compositional identification, we are still determining what most compounds are. It is well known that the composition of traditional Chinese medicine is very complex, containing thousands of compounds, and the identification of compounds is time-consuming, laborious, and may not be correct. Therefore, rational utilization of information on unknown components in traditional Chinese medicines to facilitate quality control of traditional Chinese medicines may be a direction for future research and development.

4. Materials and Methods

4.1. Experimental Materials

A total of 24 standard samples belonging to the three species, including AC (6 cortex samples), LC (9 cortex samples), and PC (9 cortex samples), were collected from the National Institutes for Food and Drug Control. All samples were identified by the laboratory and met the requirements of the 2020 edition of Chinese Pharmacopoeia; the detailed information, such as number, year, name, and so on, about standard samples is shown in Table S1. In addition, all the samples were pulverized in the year of collection and stored in a cool and dry place of a standard library of control herbs. The chemical reference standards of 3,5-Di-O-caffeoylquinic acid, Lyciumin A, Lyciumin B, Periplocoside, Periplocoside C, Periplocoside B, Periplocoside, Kukoamine A, 2-Hydroxy-4-methoxybenzaldehyde, 7-Methoxycoumain, N-caffeoyltyramine, etc. were purchased from Sunshine Trading Co., Ltd., Hong Kong, China.

4.2. Reagent Materials

Mass spectrometry-grade methanol (lot: ED341-CN) was purchased from Honeywell Trading Co., Ltd. of Shanghai, China. Mass spectrometry-grade acetonitrile (lot: 222372) was purchased from Thermo Fisher Scientific Technology Co., Ltd. of Shanghai, China. Mass spectrometry-grade formic acid (L1670) was purchased from Honeywell Trading Co., Ltd. of Shanghai, China. Ultrapure water (GB 19298) was purchased from Watsons Food and Beverage Co., Ltd. of Guangzhou, China.

4.3. Sample Pretreatment and UHPLC-QTOF-MSE Analysis

The 10.00 mg of each standard was weighed precisely, and the solution was diluted to 200 mL; then, 1.00 mL was pipetted and diluted to 200 mL again to make the standard solution with a concentration of 250 ng/mL for identification of chemical composition. The specific procedure for herbal samples pretreatment is as follows: firstly, accurately weigh 1.00 g of herbal material dried powder of AC, PC, and LC, and place in 50 mL tapered bottle with a plug respectively; then, accurately add 25.00 mL of mass-spectrum methanol with a pipette into tapered bottle to perform ultrasound for 30 min (power: 500 W, frequency: 40 kHz); finally, take out and cool to room temperature and filter with 0.22 μm organic filter membrane to obtain samples to be analyzed. The samples were stored at 4 °C in the refrigerator before UHPLC-QTOF-MSE analysis. In addition, the quality control sample was a mixed AC, PC, and LC solution.
The UHPLC-QTOF-MSE analysis was performed using liquid chromatography tandem time-of-flight mass spectrometry on Waters Xevo G2-XS QTof (Waters, United States of America, USA). Chromatographic separations were conducted on Waters Acquity UHPLC BEH-C18 (2.1 mm × 100 mm, 1.7 μm) chromatographic column (lot: 186002352 Waters, United States of America, USA). For the analysis of AC, PC, and LC samples, the column temperature was programmed at 35 ℃. The mobile phases were 0.1% formic acid in water (A phase)-acetonitrile (B phase), and the gradient elution conditions were as follows: 0–23 min, 5–95% B; 23–26 min, 95% B; 26–26.01 min, 95–5% B; and 26.01–30 min, 5% B. The injection volume was 2.0 μL. On the other hand, the ESI-positive ionization mode was used for detection and analysis in this study. The MSE data acquisition method was used in which the data acquisition rate was set to 0.2 s; The scanning range of m/z was 100–1500; the collision gas was high purity Argon, and the real-time mass axis calibration solution (lock mass) was Leucine Enkephalin (LE) whose concentration was 300 ng/mL. In addition, capillary: 3.0 kV; sampling cone: 40 V; source offset: 80 V; desolvation temperature: 450 °C; desolvation gas: 900 L/h, collision energy:10–40 V; and source temperature: 120 °C. The mass axis and lock mass were calibrated before sample analysis.

4.4. Data Processing and Analysis

The mass spectrometry information of AC, PC, and LC was processed by Progenesis QI software (version 2.3) with the parameters as follows: type of machine: high-resolution mass spectrometer; ionization polarity: positive; retention time: 1.00~26.00 min; peak picking limits: automatic; and Rt window: 0.1 min. We obtained the quantized data, including retention time (Rt), mass-to-charge ratio (m/z), and ionic strength (I). SIMCA P 14.1 (Umetrics, Umeå, Sweden) was used for data analysis, in which PCA and PLS-DA were adopted to explore the differential chemical composition ions based on VIP [22,37,38,39,40]. Further, the differential chemical components of LC, AC, and PC were identified according to the chemical standard substances, related literature, and databases and verified based on mathematical statistical analysis.

5. Conclusions

In this paper, UHPLC-QTOF-MSE and chemometric analysis were successfully applied to explore the differential chemical components of PC, AC, and LC. Moreover, 17 differential chemical constituents were identified according to the chemical standard substances, related literature, and databases and verified based on non-parametric test. The proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can also be set to enhance the quality control of LC, PC, and AC. This study is beneficial for strengthening the quality control of these three Chinese medicines.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules29163807/s1, Table S1: the detailed information of Acanthopanacis Cortex, Lycii Cortex, and Periplocae Cortex samples; Table S2: specific information of compounds and fragment ions; Figure S1: the mass spectrogram of chemical reference standards used for identifying compounds.

Author Contributions

Data curation, X.W. and J.Z.; formal analysis, W.J., M.L. and X.G.; funding acquisition, X.C. and F.W.; investigation, F.H., W.J., M.L. and X.G.; project administration, X.C. and F.W.; writing—original draft, X.W.; writing—review and editing, J.Z. and X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key R&D Program of China (2023YFC3504105) and the Training Fund for academic leaders of NIFDC (2023X10).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data information can be obtained from Supplementary Materials.

Acknowledgments

Firstly, we are grateful to the editor and reviewers for their comments and suggestions. Then, we thank the National Key R&D Program of China (2023YFC3504105) and the Training Fund for academic leaders of NIFDC (2023X10). Finally, we thank the Institute for Control of Traditional Chinese Medicine and Ethnic Medicine and the National Institutes for Food and Drug Control for support.

Conflicts of Interest

The authors declare that they have no conflicts of interests.

References

  1. Yang, H.D.; Tang, Z.S.; Xue, T.T.; Zhu, Y.Y.; Su, Z.H.; Xu, H.B. Acyl-quinic acids from the root bark of Acanthopanax gracilistylus and their inhibitory effects on neutrophil elastase and cyclooxygenase-2 in vitro. Bioorg. Chem. 2023, 140, 106798. [Google Scholar] [CrossRef]
  2. National Pharmacopoeia Commission. Pharmacopoeia of the People’s Republic of China, 11th ed.; China medical science press: Beijing, China, 2020; pp. 67–68+269. [Google Scholar]
  3. Liu, C.; Yan, L.; Qian, Y.; Song, P.; Wang, T.; Wei, M. The Extract of Acanthopanacis Cortex Relieves the Depression-Like Behavior and Modulates IL-17 Signaling in Chronic Mild Stress-Induced Depressive Mice. Dose Response 2023, 21, 15593258221148817. [Google Scholar] [CrossRef]
  4. Shi, S.; Cho, H.; Sun, Q.; He, Y.; Ma, G.; Kim, Y.; Kim, B.; Kim, O. Acanthopanacis Cortex extract: A novel photosensitizer for head and neck squamous cell carcinoma therapy. Photodiagnosis Photodyn. Ther. 2019, 26, 142–149. [Google Scholar] [CrossRef]
  5. Li, Y.; Li, J.; Zhou, K.; He, J.; Cao, J.; An, M.; Chang, Y.X. A Review on Phytochemistry and Pharmacology of Cortex Periplocae. Molecules 2016, 21, 1702. [Google Scholar] [CrossRef]
  6. Lin, Z.; Chen, L.; Cheng, M.; Zhu, F.; Yang, X.; Zhao, W.; Zuo, J.; He, S. Cortex periplocae modulates the gut microbiota to restrict colitis and colitis-associated colorectal cancer via suppression of pathogenic Th17 cells. Biomed. Pharmacother. 2022, 153, 113399. [Google Scholar] [CrossRef]
  7. Wei, J.; Chahel, A.A.; Ni, Y.; Wei, X.; Zhao, Y.; Wang, Y.; Zeng, S. Lycium RIN negatively modulate the biosynthesis of kukoamine A in hairy roots through decreasing thermospermine synthase expression. Int. J. Biol. Macromol. 2023, 252, 126246. [Google Scholar] [CrossRef]
  8. Zhang, J.; Guan, S.; Sun, J.; Liu, T.; Chen, P.; Feng, R.; Chen, X.; Wu, W.; Yang, M.; Guo, D.A. Characterization and profiling of phenolic amides from Cortex Lycii by ultra-high performance liquid chromatography coupled with LTQ-Orbitrap mass spectrometry. Anal. Bioanal. Chem. 2015, 407, 581–595. [Google Scholar] [CrossRef]
  9. Li, X.; Jiang, X.W.; Chu, H.X.; Zhao, Q.C.; Ding, H.W.; Cai, C.H. Neuroprotective effects of kukoamine A on 6-OHDA-induced Parkinson’s model through apoptosis and iron accumulation inhibition. Chin. Herb. Med. 2020, 13, 105–115. [Google Scholar] [CrossRef]
  10. Li, Z.T.; Zhang, F.X.; Fan, C.L.; Ye, M.N.; Chen, W.W.; Yao, Z.H.; Yao, X.S.; Dai, Y. Discovery of potential Q-marker of traditional Chinese medicine based on plant metabolomics and network pharmacology: Periplocae Cortex as an example. Phytomedicine 2021, 85, 153535. [Google Scholar] [CrossRef]
  11. Zhang, J.X.; Guan, S.H.; Yang, M.; Feng, R.H.; Wang, Y.; Zhang, Y.B.; Chen, X.; Chen, X.H.; Bi, K.S.; Guo, D.A. Simultaneous determination of 24 constituents in Cortex Lycii using high-performance liquid chromatography-triple quadrupole mass spectrometry. J. Pharm. Biomed. Anal. 2013, 77, 63–70. [Google Scholar] [CrossRef]
  12. Sun, L.; Wu, J.; Wang, K.; Liang, T.; Liu, Q.; Yan, J.; Yang, Y.; Qiao, K.; Ma, S.; Wang, D. Comparative Analysis of Acanthopanacis Cortex and Periplocae Cortex Using an Electronic Nose and Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis. Molecules 2022, 27, 8964. [Google Scholar] [CrossRef] [PubMed]
  13. Li, N.; Xie, L.; Yang, N.; Sun, G.; Liu, H.; Bi, C.; Duan, J.; Yuan, Y.; Yu, H.; Xu, Y.; et al. Rapid classification and identification of chemical constituents in Epimedium koreanum Nakai by UHPLC-Q-TOF-MS combined with data post-processing techniques. Phytochem. Anal. 2021, 32, 575–591. [Google Scholar] [CrossRef] [PubMed]
  14. Zhou, M.; Huo, J.; Wang, C.; Wang, W. UHPLC/Q-TOF MS Screening and Identification of Antibacterial Compounds in Forsythia suspensa (Thunb.) Vahl Leaves. Front. Pharmacol. 2022, 12, 704260. [Google Scholar] [CrossRef]
  15. Liu, H.; Yang, L.; Wan, C.; Li, Z.; Yan, G.; Han, Y.; Sun, H.; Wang, X. Exploring potential mechanism of ciwujia tablets for insomnia by UHPLC-Q-TOF-MS/MS, network pharmacology, and experimental validation. Front. Pharmacol. 2022, 13, 990996. [Google Scholar]
  16. Wang, Y.; He, S.; Cheng, X.; Lu, Y.; Zou, Y.; Zhang, Q. UHPLC-Q-TOF-MS/MS fingerprinting of Traditional Chinese Formula SiJunZiTang. J. Pharm. Biomed. Anal. 2013, 80, 24–33. [Google Scholar] [CrossRef] [PubMed]
  17. Sang, Q.; Jia, Q.; Zhang, H.; Lin, C.; Zhao, X.; Zhang, M.; Wang, Y.; Hu, P. Chemical profiling and quality evaluation of Zhishi-Xiebai-Guizhi Decoction by UHPLC-Q-TOF-MS and UHPLC fingerprint. J. Pharm. Biomed. Anal. 2021, 194, 113771. [Google Scholar] [CrossRef] [PubMed]
  18. Yang, B.; Xuan, S.; Ruan, Q.; Jiang, S.; Cui, H.; Zhu, L.; Luo, X.; Jin, J.; Zhao, Z. UHPLC/Q-TOF-MS/MS-based metabolomics revealed the lipid-lowering effect of Ilicis Rotundae Cortex on high-fat diet induced hyperlipidemia rats. J. Ethnopharmacol. 2020, 256, 112784. [Google Scholar] [CrossRef]
  19. Chang, K.; Gao, P.; Lu, Y.Y.; Tu, P.F.; Jiang, Y.; Guo, X.Y. Identification and characterization of quinoline alkaloids from the root bark of Dictamnus dasycarpus and their metabolites in rat plasma, urine and feces by UHPLC/Qtrap-MS and UHPLC/Q-TOF-MS. J. Pharm. Biomed. Anal. 2021, 204, 114229. [Google Scholar] [CrossRef] [PubMed]
  20. Zhang, J.; Yang, W.; Li, S.; Yao, S.; Qi, P.; Yang, Z.; Feng, Z.; Hou, J.; Cai, L.; Yang, M.; et al. An intelligentized strategy for endogenous small molecules characterization and quality evaluation of earthworm from two geographic origins by ultra-high performance HILIC/QTOF MS(E) and Progenesis QI. Anal. Bioanal. Chem. 2016, 408, 3881–3890. [Google Scholar] [CrossRef]
  21. Ben Salem, K.; Ben Abdelaziz, A. Principal Component Analysis (PCA). Tunis. Med. 2021, 99, 383–389. [Google Scholar]
  22. Casal, C.A.; Losada, J.L.; Barreira, D.; Maneiro, R. Multivariate Exploratory Comparative Analysis of LaLiga Teams: Principal Component Analysis. Int. J. Environ. Res. Public. Health 2021, 18, 3176. [Google Scholar] [CrossRef] [PubMed]
  23. Xu, G.; Shu, Y.; Xu, Y. Metabolomics analyses of traditional Chinese medicine formula Shuang Huang Lian by UHPLC-QTOF-MS/MS. Chin. Med. 2022, 17, 62. [Google Scholar] [CrossRef] [PubMed]
  24. Zhang, W.; Cao, J.; Li, Z.; Li, Q.; Lai, X.; Sun, L.; Chen, R.; Wen, S.; Sun, S.; Lai, Z. HS-SPME and GC/MS volatile component analysis of Yinghong No. 9 dark tea during the pile fermentation process. Food Chem. 2021, 357, 129654. [Google Scholar] [CrossRef]
  25. Gavaghan, C.L.; Wilson, I.D.; Nicholson, J.K. Physiological variation in metabolic phenotyping and functional genomic studies: Use of orthogonal signal correction and PLS-DA. FEBS Lett. 2002, 530, 191–196. [Google Scholar] [CrossRef] [PubMed]
  26. Du, Y.; Fan, P.; Zou, L.; Jiang, Y.; Gu, X.; Yu, J.; Zhang, C. Serum Metabolomics Study of Papillary Thyroid Carcinoma Based on HPLC-Q-TOF-MS/MS. Front. Cell Dev. Biol. 2021, 9, 593510. [Google Scholar] [CrossRef] [PubMed]
  27. Olsson, M.; Hellman, U.; Wixner, J.; Anan, I. Metabolomics analysis for diagnosis and biomarker discovery of transthyretin amyloidosis. Amyloid 2021, 28, 234–242. [Google Scholar] [CrossRef] [PubMed]
  28. Qiu, X.; Wang, H.; Lan, Y.; Miao, J.; Pan, C.; Sun, W.; Li, G.; Wang, Y.; Zhao, X.; Zhu, Z.; et al. Blood biomarkers of post-stroke depression after minor stroke at three months in males and females. BMC Psychiatry 2022, 22, 162. [Google Scholar] [CrossRef]
  29. Horai, H.; Arita, M.; Kanaya, S.; Nihei, Y.; Ikeda, T.; Suwa, K.; Ojima, Y.; Tanaka, K.; Tanaka, S.; Aoshima, K.; et al. MassBank: A public repository for sharing mass spectral data for life sciences. J. Mass. Spectrom. 2010, 45, 703–714. [Google Scholar] [CrossRef]
  30. Wishart, D.S.; Guo, A.; Oler, E.; Wang, F.; Anjum, A.; Peters, H.; Dizon, R.; Sayeeda, Z.; Tian, S.; Lee, B.L.; et al. HMDB 5.0: The Human Metabolome Database for 2022. Nucleic Acids Res. 2022, 50, D622–D631. [Google Scholar] [CrossRef]
  31. Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J. Cheminformatics 2014, 6, 13. [Google Scholar] [CrossRef]
  32. Li, Z.T.; Zhang, F.X.; Chen, W.W.; Chen, M.H.; Tang, X.Y.; Ye, M.N.; Yao, Z.H.; Yao, X.S.; Dai, Y. Characterization of chemical components of Periplocae Cortex and their metabolites in rats using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Biomed. Chromatogr. 2020, 34, e4807. [Google Scholar] [CrossRef]
  33. Itokawa, H.; Xu, J.; Takeya, K.; Watanabe, K.; Shoji, J. Studies on chemical constituents of antitumor fraction from Periploca sepium. II. Structures of new pregnane glycosides, periplocosides A, B and C. Chem. Pharm. Bull. 1988, 36, 982–987. [Google Scholar] [CrossRef]
  34. Chen, X.H.; Su, L.; Jiang, L.J.; Zhang, W.; Jiang, Y.Y.; Liu, B. Identification of compounds in Lycii cortex by UPLC-LTQ-orbitrap-MS. Zhongguo Zhong Yao Za Zhi 2019, 44, 4486–4494. [Google Scholar]
  35. Beszterda, M.; Frański, R. Comment on “Phenolic profiling and evaluation of in vitro antioxidant, α-glucosidase and α-amylase inhibitory activities of Lepisanthes fruticosa (Roxb) Leenh fruit extracts”. Food Chem. 2021, 361, 130107. [Google Scholar] [CrossRef]
  36. Concannon, S.; Ramachandran, V.N.; Smyth, W.F. A study of the electrospray ionisation of selected coumarin derivatives and their subsequent fragmentation using an ion trap mass spectrometer. Rapid Commun. Mass. Spectrom. 2000, 14, 1157–1166. [Google Scholar] [CrossRef]
  37. Ruiz-Perez, D.; Guan, H.; Madhivanan, P.; Mathee, K.; Narasimhan, G. So you think you can PLS-DA? BMC Bioinform. 2020, 21 (Suppl. 1), 2. [Google Scholar] [CrossRef]
  38. Lee, L.C.; Liong, C.Y.; Jemain, A.A. Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: A review of contemporary practice strategies and knowledge gaps. Analyst 2018, 143, 3526–3539. [Google Scholar] [CrossRef] [PubMed]
  39. Zheng, R.; Chen, Z.; Guan, Z.; Zhao, C.; Cui, H.; Shang, H. Variable importance for projection (VIP) scores for analyzing the contribution of risk factors in severe adverse events to Xiyanping injection. Chin. Med. 2023, 18, 15. [Google Scholar] [CrossRef] [PubMed]
  40. Xu, S.; Bai, C.; Chen, Y.; Yu, L.; Wu, W.; Hu, K. Comparing univariate filtration preceding and succeeding PLS-DA analysis on the differential variables/metabolites identified from untargeted LC-MS metabolomics data. Anal. Chim. Acta 2024, 1287, 342103. [Google Scholar] [CrossRef] [PubMed]
Molecules 29 03807 g001
Figure 1. The overall research route.
Figure 1. The overall research route.
Molecules 29 03807 g001
Molecules 29 03807 g002
Figure 2. The base peak chromatogram of blank, PC, AC, LC, and the quality control sample ((A): blank; (B): PC, batch: 20210501; (C): LC, batch: 20130701; (D): AC, batch: 20170301; (E): QC sample).
Figure 2. The base peak chromatogram of blank, PC, AC, LC, and the quality control sample ((A): blank; (B): PC, batch: 20210501; (C): LC, batch: 20130701; (D): AC, batch: 20170301; (E): QC sample).
Molecules 29 03807 g002
Molecules 29 03807 g003
Figure 3. The results of score plots of Acanthopanacis Cortex (AC), Periplocae Cortex (PC), and Lycii Cortex (LC) in PCA analysis.
Figure 3. The results of score plots of Acanthopanacis Cortex (AC), Periplocae Cortex (PC), and Lycii Cortex (LC) in PCA analysis.
Molecules 29 03807 g003
Molecules 29 03807 g004
Figure 4. The results of loading scatter plots of Acanthopanacis Cortex (AC), Periplocae Cortex (PC), and Lycii Cortex (LC) in PCA analysis (Red dots: Variables that contribute less to PC1 and PC2; blue dots: Variables that contribute more to PC1 and PC2).
Figure 4. The results of loading scatter plots of Acanthopanacis Cortex (AC), Periplocae Cortex (PC), and Lycii Cortex (LC) in PCA analysis (Red dots: Variables that contribute less to PC1 and PC2; blue dots: Variables that contribute more to PC1 and PC2).
Molecules 29 03807 g004
Molecules 29 03807 g005
Figure 5. The results of score plots of Acanthopanacis Cortex (AC), Periplocae Cortex (PC), and Lycii Cortex (LC) in PLS-DA analysis.
Figure 5. The results of score plots of Acanthopanacis Cortex (AC), Periplocae Cortex (PC), and Lycii Cortex (LC) in PLS-DA analysis.
Molecules 29 03807 g005
Molecules 29 03807 g006
Figure 6. The results of loading scatter plots of Acanthopanacis Cortex (AC), Periplocae Cortex (PC), and Lycii Cortex (LC) in PLS-DA analysis.
Figure 6. The results of loading scatter plots of Acanthopanacis Cortex (AC), Periplocae Cortex (PC), and Lycii Cortex (LC) in PLS-DA analysis.
Molecules 29 03807 g006
Molecules 29 03807 g007
Figure 7. The fragmentation ion of Lyciumin B.
Figure 7. The fragmentation ion of Lyciumin B.
Molecules 29 03807 g007
Molecules 29 03807 g008
Figure 8. Part secondary cleavage ion fragments that assisted in identification of Periplocoside C.
Figure 8. Part secondary cleavage ion fragments that assisted in identification of Periplocoside C.
Molecules 29 03807 g008
Molecules 29 03807 g009
Figure 9. The fragmentation ions of Kukoamine A.
Figure 9. The fragmentation ions of Kukoamine A.
Molecules 29 03807 g009
Table 1. The detailed information of the 17 chemical components.
Table 1. The detailed information of the 17 chemical components.
Compositionm/zFragment IonsAdduct IonMolecular Formula
3,5-Di-O-caffeoylquinic acid517.1357499.1244\355.1080\145.0284\135.0427[M+H]+C25H24O12
Lyciumin A874.3739856.3706\666.2883\503.2260\486.2028
372.1536\181.0995		[M+H]+C42H51N9O12
Lyciumin B897.3897879.3695\851.3809\689.3059\643.2662\503.2247\395.1719\181.1010\159.0917[M+H]+C44H52N10O11
Periploside H21165.6003819.4387\703.4024\657.3876
485.1868\323.1347\315.1425\203.0941\171.0657		[M+H]+C56H92O25
Periplocoside C921.5216161.0806\203.0914\305.1600\417.2136\587.3598\747.4313[M+H]+C49H76O16
Periplocoside B1065.59921035.5977\921.5239\747.4311\551.3381\439.2844\417.2163[M+H]+C56H88O19
Periplocoside719.3613665.3542\535.3222\391.2525\373.1476\355.2287\337.2187\275.1131[M+Na]+C36H56O13
3-O-(β-D-glucopyranose (1→2)-β-D-glucopyranose)-16α-ethoxy -oleanolic acid-28-O-β-D-glucopyranoside1009.5346807.4570\687.4120\371.1729\337.2332\291.1958\162.1353[M+Na]+C50H82O19
Kukoamine A531.3184367.2711\293.1868\251.1363\222.1112\167.0721\165.0528\123.0441[M+H]+C28H42N4O6
5-Hydroxy-6,7-dimethoxyflavone-4′-O-beta-D-glucopyranoside499.1244315.0693\171.1120\163.0667\145.0286[M+Na]+C23H24O11
2-Hydroxy-4-methoxybenzaldehyde153.0555135.0420\125.0600\121.0285[M+H]+C8H8O3
Periplocoside K825.4255629.2805\485.1865\325.1138\323.1341\203.0940[M+Na]+C40H66O16
N-FeruloyltyraMine314.1392177.0548\134.0356[M+H]+C18H19NO4
N-caffeoyltyramine300.1238121.0640[M+H]+C17H17NO4
3-O-acetyl-caffeic acid223.0606123.0443\134.0367[M+H]+C11H10O5
1-Monopalmitin353.2667313.2765\239.2404[M+Na]+C19H38O4
7-methoxycoumain [36]177.0552162.0364\149.0584\133.0698\118.0383[M+H]+C10H8O3
Table 2. The nonparametric test results of the 17 chemical components.
Table 2. The nonparametric test results of the 17 chemical components.
CompoundsClass MedianKruskal-Wallis-H Valuep
LC (n = 9)AC (n = 6)PC (n = 9)
3,5-Di-O-caffeoylquinic acid5830.110 (2778.9, 9303.2)184,194.500 (181,343.5, 194,189.5)12,993.500 (11,643.9, 16396.9)20.250.000 **
Lyciumin A1,600,530.000 (355,550.0, 2,149,495.0)494.760 (487.2, 499.8)1889.880 (1871.3, 1922.7)20.250.000 **
Lyciumin B34,775.800 (26,950.8, 55,647.2)0.000 (0.0, 0.0)0.000 (0.0, 0.0)21.410.000 **
Periploside H226.202 (0.0, 24,765.5)82.655 (71.5, 107.0)292,979.000 (281,991.0, 353,838.5)16.720.000 **
Periplocoside C11,552.900 (10,465.3, 31,519.0)26,186.300 (25,263.7, 26,754.5)179,990.000 (140,397.0, 182,907.0)16.650.000 **
Periplocoside B8872.360 (8011.5, 44,087.2)23,943.500 (23,182.0, 24,265.3)152,935.000 (133,812.0, 212,033.0)16.650.000 **
Periplocoside262.189 (167.4, 11,596.4)2095.605 (1985.1, 2163.4)28,947.300 (27,909.1, 39,741.9)16.650.000 **
3-O-(β-D-glucopyranose (1→2)-β-D-glucopyranose) -16α-ethoxy-oleanolic acid -28-O-β-D-glucopyranoside0.000 (0.0, 5724.1)3487.590 (3211.4, 3559.4)31,907.300 (23,078.8, 45,327.4)16.910.000 **
Kukoamine A6,393,320.000 (4,404,335.0, 6,703,815.0)0.000 (0.0, 0.0)1055.530 (292.7, 1394.1)20.560.000 **
5-Hydroxy-6,7-dimethoxyflavone-4′-O-beta-D-glucopyranoside3283.940 (755.7, 5009.4)341,882.000 (334,246.8, 366,415.5)34,851.100 (22,416.1, 37,870.1)20.250.000 **
2-Hydroxy-4-methoxybenzaldehyde217.577 (95.0, 1380.7)3061.145 (3021.6, 3138.2)50,979.300 (20,609.9, 57,093.0)20.250.000 **
Periplocoside K3144.210 (224.8, 8289.9)2119.750 (2048.7, 2207.6)13,786.500 (12,795.0, 14,256.9)16.250.000 **
N-FeruloyltyraMine237,802.000 (146,926.5, 781,073.0)1271.425 (1259.4, 1344.9)1012.490 (786.3, 1113.7)18.890.000 **
N-caffeoyltyramine246,638.000 (85,521.4, 348,217.0)530.405 (501.3, 557.8)1011.030 (581.5, 1052.8)19.400.000 **
3-O-acetyl-caffeic acid18,414.500 (8236.1, 25,494.3)600.173 (542.5, 667.1)25,812.000 (18,788.3, 37,798.0)15.010.001 **
1-Monopalmitin377,254.000 (331,131.0, 602,292.0)63,102.600 (57,234.3, 67,633.6)36,612.100 (29,466.7, 42,631.4)20.250.000 **
7-methoxycoumain 1629.570 (1294.6, 3267.7)602.553 (579.9, 627.4)20,946.200 (13,503.5, 23,814.5)20.250.000 **
** p < 0.01.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, X.; Zhang, J.; He, F.; Jing, W.; Li, M.; Guo, X.; Cheng, X.; Wei, F. Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics. Molecules 2024, 29, 3807. https://doi.org/10.3390/molecules29163807

AMA Style

Wang X, Zhang J, He F, Jing W, Li M, Guo X, Cheng X, Wei F. Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics. Molecules. 2024; 29(16):3807. https://doi.org/10.3390/molecules29163807

Chicago/Turabian Style

Wang, Xianrui, Jiating Zhang, Fangliang He, Wenguang Jing, Minghua Li, Xiaohan Guo, Xianlong Cheng, and Feng Wei. 2024. "Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics" Molecules 29, no. 16: 3807. https://doi.org/10.3390/molecules29163807

Article Metrics

Back to TopTop