Method For Identification Of Components Research Articles

Untargeted Profiling of Shenfu Qiangxin Pills Based on High-Resolution Mass Spectrometry and Absolute Quantitation of Multiple Components Using Parallel Reaction Monitoring.

Shenfu Qiangxin (SFQX) pills are proprietary traditional Chinese medicine used for the treatment of chronic heart failure with significant clinical effects. However, the systematic identification and quantification of complexed components in SFQX have not been reported yet. In this work, a reliable and comprehensive method for a rapid identification of chemical components was developed by data dependent acquisition using ultra performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-Q Exactive-Orbitrap-MS). A total of 104 compounds mainly including flavonoids, saponins, alkaloids, anthraquinones, coumarins, and phenolic acids were identified through database searching. The identified compounds were further ascribed to herb species in SFQX pills, such as Ginseng Radis et Rhizoma, Aconiti Lateralis Radix Praeparata (processed), Mori Cortex, Lepidii Semen, Rhei Radix et Rhizoma, and Polyporus. Thirty-two representative compounds were elaborated with cleavage pathways and characteristic fragments in MS/MS spectrum of commercially available standards. A quantification method based on parallel reaction monitoring technique was established, and absolute quantification of 28 components in SFQX pills was then carried out. This work constitutes a basis and methodological reference for the quality control, consistency evaluation, and standardization of similar proprietary Chinese medicines.

Identification and Quantitative Analysis of the Pharmacologically Active Components of Jianxin Granules by UHPLC-QTOF-MS/MS-Based Metabolomics

Jianxin Granules, a Traditional Chinese Medicine (TCM), consisting of eight flavors, including Huang Qi (astragalus), Hong Shen (red ginseng), Pu Huang (pollen typhae), Dan Shen (salvia miltiorrhiza), Zhu Ling (polyporus), Bai Zhu (atractylodes macrocephala), Gui Zhi (cassia twig), Ting Li Zi (semen lepidii). Jianxin granules has a multi-system, multi-target, and multi-directional comprehensive regulatory effect on inhibiting ventricular remodeling, which is an effective formulation for the prevention and treatment of heart failure, and has a good application prospect. However, many of the ingredients, including pharmacologically active ingredients, in the Jianxin granules remain unclear. Here, we attempted to develop a metabolomics method of component identification, quantitation, pattern recognition, and cross-comparison of Jianxin granules. Chemical analysis, component identification and quantification analyse of Jianxin granules were conducted with a combination of UHPLC-QTOF-MS/MS with bioinformatics. Assessment of the correlation between technical and bio-replicated pharmacological active ingredients was implemented by Principal Component Analysis (PCA), in addition to Partial Least Squares Discriminant Analysis (PLS-DA). UHPLC-QTOFMS/ MS, a metabolomics method, was developed and adapted to characterize Jianxin granules, which consisted of 178 to 216 molecular signatures. The quantitative analysis of 95 frequently occurring molecular signatures of Jianxin granules was carried out by a single exogenous reference internal standard. Of these, 47 have been identified using diverse databases, including 2 glycosylglycerol derivatives, 2 lipids, 2 spiro compounds, 2 cyclohexanecarboxylic acids, 2 glycosides, 5 terpenoids, 7 oligopeptides, 17 favonoids, and 8 various compounds, such as hydroxycoumarin, chalcone, benzofuran, benzodioxole, benzaldehyde, aromatic ketone, and alkyl cafeate ester. The established method demonstrates robust reliability and reproducibility, making it suitable for various applications including compositional identification, quantification, and quality assessment of the pharmacologically active constituents in Jianxin granules.

Rapid identification of cocrystal components of explosives based on Raman spectroscopy and principal component analysis

Energetic cocrystal materials are considered to be one of the important directions for the development of energetic materials, due to their high energy density and low sensitivity. However, there is still a lack of effective methods to carry out rapid structural and purity identification. Herein, we explored a method for rapid identification and identification of unknown components extracted from CL-20/MTNP and CL-20/HMX cocrystal processes based on Raman spectroscopy combined with principal component analysis (PCA). Thirty sets of cocrystal and 30 sets of mixed explosives were randomly selected as the training set and 10 sets each as the validation set. The principal components were extracted by dimensionality reduction of the collected Raman spectra using the principal component sub-featured clustering algorithm of chemometrics. The region identification structure formed by different principal components allows intelligent output of whether the sample was cocrystal or not. The results show that the cumulative contribution rate of the three principal components in the sample set was 98.7 %. The confidence ellipses of the validation set were all well distributed within the confidence ellipses of the training set. And the structure identification results of explosive cocrystals were output quickly, accurately and intelligently. Therefore, this method shows good potential application value in the rapid structural identification of other complex mixtures such as energetic even pharmaceutical cocrystals.

Identification and Characterization of the Chemical Constituents of Qianlie Shule Capsules by UPLC-Q-Orbitrap-MS/MS.

The Qianlie Shule capsule is a classical Chinese medicine compound preparation frequently used in therapeutic settings to alleviate astringent pain in the urethra, prostatic hypertrophy, and chronic prostatitis or urinary frequency. However, a comprehensive analysis of the chemical composition of Qianlie Shule capsules has not been reported. To establish a quick and effective analytical method based on hybrid quadrupole-orbitrap mass spectrometry ultrahigh-performance liquid chromatography (UPLC-Q-Orbitrap-MS/MS) for the identification and characterization of chemical components in Qianlie Shule capsules. Using ultrahigh-performance liquid chromatography with hybrid quadrupole-orbitrap mass spectrometry and data post-processing, the samples of Qianlie Shule capsules were examined. First, the whole extract of the Qianlie Shule capsules was separated using a UPLC machine, and the fragmentation data were collected in both positive and negative ion mode. The target molecule is then quickly identified by comparing the fragmentation information of the neutral loss (NLs) and characteristic fragments (CFs) reported in the literature. A total of 145 chemical components were identified. It includes flavonoids, triterpenoids, phenylpropanoids, organic acids, alkaloids, phenylethanoids, iridoids, and anthraquinones. This study is a method for the rapid qualitative analysis of the chemical composition of Qianlie Shule capsules, which provides a method for the rapid, sensitive, and high-throughput identification of the prescription components of Chinese medicine. Systematic identification of the chemical composition of QLSL capsules provides a theoretical basis for studying the substance basis of QLSL capsules and the improvement of the quality control level.

Developmental characteristics of fractures in deep tight sandstone reservoirs in the second Member of the Xujiahe Formation of Zhongjiang Gas Field

The development characteristics, scale and control factors of fractures are the core subjects of reservoir sweet spot prediction. The sandstone reservoir of the TX2 gas reservoir in the Zhongjiang Gas Field is a typical low porosity and low permeability tight reservoir with strong heterogeneity, but relatively high-quality reservoirs can be found in different well areas and well segments. In this paper, taking the second Member of the Xujiahe Formation (TX2) as an example, the control factors of fractures were systemically investigated via core observation, thin section, logging data, and fracture logging identifications. The results show that shear fractures are mainly developed in the cores, and they generally have high filling rate and poor effectiveness; microfractures can be found based on the vitrinite and cast thin section results. The intersection diagram (semi-quantitative) and the principal component and BP comprehensive identification (quantitative) methods can effectively identify different types of fractures. The combined application of principal component and BP comprehensive identification methods results in an 83.3 % fracture identification probability. Finally, we found that the development of fractures in TX2 is comprehensively affected by lithology, rock thickness, porosity, and faults.

Analysis of Targeted Components in the Combination Extract of Hovenia dulcis and Phyllanthus emblica by UPLC-MS/MS

This study aimed to establish a UPLC-MS/MS method for rapid analysis and identification of target components in the combination extract of Hovenia dulcis and Phyllanthus emblica. The gradient elution was carried out with methanol (A) −0.2% formic acid solution (A) with low rate of 1.0 mL · min−1, column temperature of 30 °C, detection wavelength of 285 nm. The ESI ion source was used to collect data under the negative ion mode. Through analysis and identification, five target components were inferred, namely gallic acid, dihydromyricetin, corilagine, taxifolin, and ellagic acid in the combination extract. Quantitative analysis was conducted on the combination extract. By utilizing the theory of modern supramolecular technology in traditional Chinese medicine, the functional composite product was obtained by selecting the compound extraction of Hovenia dulcis and Phyllanthus emblica as raw materials, and quantitative analysis was conducted on on the combination extract. The results showed that the flavonoid extraction rate and the content of target components were both higher when the mass ratio of Hovenia dulcis to Phyllanthus emblica was 1:1 and 1:2. This indicates that the co-extraction of Hovenia dulcis and Phyllanthus emblica not only simplified the extraction process but also enhances the extraction rate of the flavonoids in Hovenia dulcis. The developed UPLC-MS/MS method is rapid, sensitive, which provides a technical basis for the selection of raw material mass ratios and product development of Hovenia dulcis and Phyllanthus emblica combination.

Identification of Critical Scenario Components Based on Driving Database Analysis for Safety Assessment of Automated Driving Systems

A thorough safety assessment of an automated driving system (ADS) is necessary before its introduction into the market and practical application. Scenario-based assessments have received significant attention in research. However, identifying sufficient critical scenarios for ADSs is a major challenge, especially for complex urban environments with a variety of road geometries, traffic rules, and traffic participants. To identify the critical scenarios in this complex environment, it is essential to understand the environmental factors that lead to safety-critical events (e.g., accidents and near-miss incidents). Thus, this study proposes a method for identification of critical scenario components by analyzing near-miss incident data and extracting environmental factors that induce driver errors. In this study, we applied the proposed method to a scenario, in which an ego vehicle makes a right turn at a signalized intersection with an oncoming vehicle approaching the intersection in left-hand traffic, as a case study. The proposed method identified two components (dynamic occlusion caused by oncoming right-turn vehicles and change in traffic lights) that were both critical and challenging for ADSs. The case study results showed the usefulness of the identified components and the validity of the proposed method, which can extract critical scenario components explicitly.

An Identification Method for Irregular Components Related to Terminal Blocks in Equipment Cabinet of Power Substation.

Despite the continuous advancement of intelligent power substations, the terminal block components within equipment cabinet inspection work still often require loads of personnel. The repetitive documentary works not only lack efficiency but are also susceptible to inaccuracies introduced by substation personnel. To resolve the problem of lengthy, time-consuming inspections, a terminal block component detection and identification method is presented in this paper. The identification method is a multi-stage system that incorporates a streamlined version of You Only Look Once version 7 (YOLOv7), a fusion of YOLOv7 and differential binarization (DB), and the utilization of PaddleOCR. Firstly, the YOLOv7 Area-Oriented (YOLOv7-AO) model is developed to precisely locate the complete region of terminal blocks within substation scene images. The compact area extraction model rapidly cuts out the valid proportion of the input image. Furthermore, the DB segmentation head is integrated into the YOLOv7 model to effectively handle the densely arranged, irregularly shaped block components. To detect all the components within a target electrical cabinet of substation equipment, the YOLOv7 model with a differential binarization attention head (YOLOv7-DBAH) is proposed, integrating spatial and channel attention mechanisms. Finally, a general OCR algorithm is applied to the cropped-out instances after image distortion to match and record the component's identity information. The experimental results show that the YOLOv7-AO model reaches high detection accuracy with good portability, gaining 4.45 times faster running speed. Moreover, the terminal block component detection results show that the YOLOv7-DBAH model achieves the highest evaluation metrics, increasing the F1-score from 0.83 to 0.89 and boosting the precision to over 0.91. The proposed method achieves the goal of terminal block component identification and can be applied in practical situations.

Rapid identification of chemical constituents in Hugan tablets by ultra-performance liquid chromatography-quadrupole-exactive orbitrap mass spectrometry.

Hugan tablet is a Chinese medicine preparation. It is composed of Bupleuri Radix, Artemisiae Scopariae Herba, Isatidis Radix, Schisandrae Chinensis Fructus, Suis Fellis Pulvis, and Vigna radiata L. It has the effects of dispersing stagnated liver qi, strengthening the spleen and eliminating food to be used for the treatment of chronic hepatitis and early cirrhosis. However, the chemical composition of Hugan tablet is complex and not fully understood, which hampers the research in pharmacology. In this study, a reliable method for the rapid analysis and identification of the chemical components in Hugan tablet by their characteristic fragments and neutral losses using ultra-performance liquid chromatography-quadrupole-exactive orbitrap mass spectrometry was developed. A total of 144 chemical components were tentatively identified, including 57 organic acids, 19 flavonoids, 23 alkaloids, 18 lignans, 7 saponins, and 20 others. These components may be the active ingredients of Hugan tablet. The established method can systematically and rapidly analyze the chemical components in Hugan tablet, which provides a basis for the pharmacodynamic substance study and is meaningful for the quality control of Hugan tablet.

Research on Composition Analysis and Identification of Ancient Glass Products

Glassware was the witness of the ancient Silk Road trade. The paper focuses on the component analysis and identification methods of ancient glass products. The decision tree model was established and analyzed to obtain the classification rules of high potassium glass and lead barium glass. Based on the cluster analysis method, appropriate chemical elements were selected for each type to conduct cluster analysis. The result showed that high potassium glass and unweathered lead barium glass can be classified by their content of silicon dioxide. However, weathered lead barium glass needed to be classified by their content of lead oxide. The random forest regression model was used to classify glass cultural relics by their chemical elements. The chemical element data of glass cultural relics with known types were used as samples, and the chemical element data of glass cultural relics with unknown types were inputted into the trained model. Finally, the types of glass cultural relics were predicted and sensitivity analysis was conducted.

Rapid visual characterization of alkaloid changes in traditional processing of Tibetan medicine Aconitum pendulum by high-performance thin-layer chromatography coupled with desorption electrospray ionization mass spectrometry imaging.

Radix Aconiti, also known as Tie-bang-chui (TBC), Pang-a-na-bao, and Bang-na, is a typical aconitum Tibetan medicine and a perennial herb of the genus Aconitum pendulum Busch. and A. flavum Hand. -Mazz. dry roots. It has high toxicity and remarkable efficacy; as such, it is a typical "highly toxic and effective" drug that needs be processed and used. Processing methods of this Tibetan medicine include non-heating of highland barley wine (HBW) and fructus chebulae soup (FCS). This work aimed to understand differences in chemical composition between non-heat processed products and raw TBC. In this study, high-performance thin-layer chromatography (HPTLC) and desorption electrospray ionization mass spectrometry imaging (DESI-MSI) were used to analyze the chemical composition of TBC processed by FCS (F-TBC) and HBW (H-TBC). The MRM mode of HPLC-QqQ-MS/MS was selected to determine the changes of several representative alkaloids to comparison with the former results. A total of 52 chemical constituents were identified in raw and processed products, and the chemical composition of F-TBC and H-TBC changed slightly compared with that of raw TBC. The processing mechanism of H-TBC was also different from that of F-TBC, which might be related to the large amount of acidic tannins in FCS. It was found that the content of all six alkaloids decreased after processing by FCS, and all five alkaloids decreased except aconitine increased after processing by HBW. The combination of HPTLC and DESI-MSI could be an effective method for rapid identification of chemical components and changing rules in ethnic medicine. The wide application of this technology provides not only an alternative method for the traditional separation and identification of secondary metabolism but also a reference for research on the processing mechanism and quality control of ethnic medicine.

Research of critical component identification method for spacecraft dimensional stability based on contribution rate

Influence of each component in the spacecraft structure on the dimensional stability of the structural system is different. Even in the dimensional stability design under the whole satellite configuration, the influence of each component is different. Identifying components that have a great impact on dimensional stability through the contribution rate defined in this paper, and carrying out targeted dimensional stability design, is conducive to reducing costs, saving time, and focusing on design priorities.

An Enhanced Time-Delay-Based Reference Current Identification Method for Single-Phase System

This article proposes an enhanced time-delay-based current component identification method for single-phase systems to overcome the absence of filtering and poor grid frequency response of conventional method. The proposed technique integrates a delayed signal cancellation with a time-delay unit, to incorporate a dc offset rejection feature, while maintaining speedy and precise extraction of orthogonal current components. The frequency adaption is also developed through the incorporation of linear interpolation. The dynamic and steady-state performance of the proposed method is analyzed and compared with two advanced second-order generalized integrator (SOGI) based methods, namely, modified SOGI and cascaded SOGI under different operating scenarios. The simulation and experimental results substantiate the advantage of zero steady-state error and lower settling time of the proposed solution while exhibiting comparable overshoot with advanced SOGI-based methods.

Rapid classification and identification of chemical components of Astragali radix by UPLC-Q-TOF-MS.

Pharmacological studies indicate that Astragalus (AR) has various bioactivities, including anticancer, antiaging, anti-inflammatory, antiviral, and antioxidant activities. Flavonoids, saponins, amino acids, and polysaccharides are the main active components in AR. However, its complex chemical compositions bring certain difficulties to the analysis of this traditional Chinese medicine (TCM). Therefore, there is an urgent need to establish a method for rapid classification and identification of the chemical constituents in AR. To establish a method for rapid classification and identification of the main components of flavonoids, saponins, and amino acids in AR. The samples were analysed with ultra-high-performance liquid chromatography time-of-flight quadrupole mass spectrometry (UPLC-Q-TOF-MS) and data post-processing techniques. Firstly, fragmentation information was obtained in the positive and negative ion modes. Then, to realize the rapid classification and identification of AR components, the characteristic fragmentations (CFs) and neutral losses (NLs) were compared with information described in the literature. A total of 45 chemical constituents were successfully screened out, including 22 flavonoids, 13 saponins, and 10 amino acids. The established method realised the efficient classification and identification of flavonoids, saponins, and amino acid compounds in AR, which provided a basis for further study on AR.

Identification of Key Components of CNC Lathe Based on Dynamic Influence of Fault Propagation

Identifying the key components of CNC lathe and analyzing the fault propagation behavior is a powerful guarantee for the fault diagnosis and health maintenance of CNC lathe. The traditional key component identification studies are mostly based on the feature parameter evaluation of the fault propagation model, disregarding the dynamics and influence of fault propagation. Therefore, this paper proposes a key component identification method based on the dynamic influence of fault propagation. Based on the CNC lathe architecture and fault data, the cascaded faults are analyzed. The improved Floyd algorithm is used to iterate and transform the direct correlation matrix expressing the cascaded fault information, and the fault propagation structure model of each component is constructed. The coupling degree function is introduced to calculate the dynamic impact degree between components, and the dynamic fault propagation rate of each component is calculated with the dynamic fault rate model. Based on this, the dynamic influence value of fault propagation is obtained by using the improved ASP algorithm. The key components of the system are identified by synthesizing the fault propagation structure model and the dynamic influence value of fault propagation. Taking a certain type of CNC lathe as an example, the proposed method is verified to be scientific and effective by comparing with the traditional identification method of key components based on fault propagation intensity.

Method for Function-Based Identification of Potential AM Components in Conventional Product Architectures

AbstractThe implementation of additive manufacturing enables the the re-thinking of a product towards function-oriented design. This study proposes a method, which uses a set of rules and indicators to implement functional integration, part consolidation, part separation and on-demand manufacturing onto a conventional prodcut architecture to restructure it into an AM-oriented product architecture. The feasibility of the method is demonstrated on an assembly from the field of high temperature applications.

A Distributionally Robust Scheme for Critical Component Identification to Bolster Cyber-Physical Resilience of Power Systems

With the increasing risks of malicious cyber-physical attacks and natural disasters, it is urged for power grids to improve the resilience performance against such high-impact, low-frequency events. The critical component identification is an important problem for the effective planning, operation and asset management of power systems. Thus, it is highly necessary and beneficial to develop a resilience oriented critical component identification method for defending power systems against extreme events. In this paper, a tri-level optimization model is developed to identify the critical components while the coordinated cyber-physical attacks on power systems are formulated considering the resources of attackers, the optimal attack strategy, and the actions of the system operator to enhance the system resilience against potential attacks. Meanwhile, a distributionally robust optimization (DRO) based model is proposed to consider the impacts of distribution uncertainty of the resource or ability of the malicious attackers in the criticality identification framework. A constraint-generation based algorithm is developed to solve the overall multi-level DRO model with a master and sub-problem scheme. Further, a criticality index is proposed to quantify the importance of key components to the system resilience against malicious cyber-physical attacks. In order to validate the proposed method, case studies were conducted on the IEEE reliability test system (RTS-79). The obtained results show that the proposed critical component identification method can provide effective and robust importance evaluation of system components to bolster the grid resilience against potential cyber-physical threats.

Functional contents and antioxidant potency of Chinese Wenguan flower tea

Wenguan Tea, composed of dried buds or flowers of Xanthoceras sorbifolia, has become popular in the north of China because of its special flavor and health benefits. The present study aims to propose a method for rapid identification of bioactive components in different edible parts of X. sorbifolia, to modulate the quality control. And a bioactivity-guided phytochemical study was further performed, directly providing the functional components in Chinese Wenguan Tea. Compared with four common commercial flower teas, Wenguan tea with the second greatest total flavonoids (0.261%) and phenolic acid (1.441%), exhibited comparable scavenging radical values and ferric-reducing power. UPLC-MS/MS analyses revealed the chemical profile of the flowers and buds. Furthermore, antioxidant potency-oriented phytochemical research led to the critical phenolic constituents (W1–W7). Among them, flavonoids W3 (myricitrin), W4 (isoquercitrin), W7 (quercitrin) could significantly inhibit intracellular ROS generation, reduce cell apoptosis, and decrease oxidative stress damage in H2O2-induced Hep3B cells in a manner of Nrf2-dependent mechanism. Overall, findings from this study exemplify a clear classification of original species for producing Wenguan Tea using flowers not buds of X. sorbifolia, and further clarified the potential pharmaceutical agents against oxidative stress as a functional tea.

Application of Various Methods to Extract Microplastic from Typical Soils in China

Microplastics have been found in many environmental systems, such as oceans, terrestrial soils, sediments, and bodies of freshwater. Microplastic pollution in soils has received extensive international attention; however, there is currently no unified standard extraction method. To identify appropriate extraction and component identification methods for microplastics in typical soils in China, samples were identified and their polymer component properties examined using ATR-FTIR(Thermo Nicolet IS 20). The effects of three treatments of water+oil(T1), a saturated NaCl solution+oil(T2), and a saturated NaCl solution(T3) on the detection of four types of microplastics[polyethylene terephthalate(PET), polypropylene(PP), polystyrene(PS), and polyethylene(PE)]in four typical soils(loess, black soil, red soil, and purple soil) were examined. The results showed that:① The extraction rates of the T3 treatment decreased with microplastic density. For PP, PE, and PS, the extraction rates exceeded 86.67%, and the extraction rates of PET were 0%-13.30%. ② The extraction rates of the four kinds of microplastics were 86.67%-100.00% in loess, black soil, and purple soil using the T1 and T2 solutions; however, the residual oil on the surface of microplastics influenced the identification using FTIR. Furthermore, the cleaning effect of PE and PS was poor after wiping with anhydrous ethanol. ③ In red soil, the extraction rates of PET were 56.60% and 50.00% in T1 and T2, respectively; in the T3 treatment, the extraction rates of PET, PE, and PP were 3.33%, 10.00%, and 56.67%, respectively. These results show that the extraction rates of four types microplastics are highest for loess, black soil, and purple soil using T1 and T2, although these two methods need to be combined with anhydrous ethanol cleaning. T3 dispose making the leak of microplastics PET with the density higher than 1 g·cm-3. Methods to extract microplastics from red soils require further research. Overall, this study can inform the investigation and treatment of microplastic pollution in typical regional soils in China.

WITHDRAWN: Rapid identification of chemical constituents in Physalis alkengi L. var. franchetii by UHPLC-ESI-Q-Obritrap MS/MS

Abstract Physalis alkengi L. var. franchetii (PA) is a type of edible and medicinal plant in Solanaceae. Its chemical components mainly include steroids, flavonoids, alkaloids, amino acids, and phenolic acids. Comprehensive component identification is essential for elucidation of its pharmacological mechanism and quality control. However, its complex chemical composition has caused certain difficulties in the analysis of this traditional Chinese medicine (TCM). Therefore, there is an urgent need to establish a method for rapid classification and identification of chemical components in PA. In this study, the samples were analyzed by ultrahigh-performance liquid chromatography with quadrupole orbitrap mass spectrometry (UHPLC-Q-Orbitrap-MS). The UHPLC system used a C18 column to separate the total extract of PA. The mobile phase consisted of 0.1% formic acid in water and acetonitrile, and the PA extract was analyzed by gradient elution at a flow rate of 0.3 mL/min. In both the positive and negative ion modes, the fragment information was obtained and compared with those of the characteristic fragmentations and neutral losses described in the literature. Mass Frontier™ simulates its fragmentation to quickly identify the target compounds. The mzVault, mzCloud, and ChemSpider databases were utilized to fully analyze the chemical composition in PA. Finally, we successfully screened out 112 chemical components, including 49 steroids, 9 alkaloids, 4 carboxylic acids and derivatives, 8 amino acids, 7 flavonoids, 4 phenols and phenolic acids, 16 phenylpropanoids, 6 terpenoids, 2 piperazines, 1 antibiotic, and 5 others. This study comprehensively analyzed the chemical composition of PA and provides scientific basis and data support for further pharmacodynamic substance studies of PA.