Geographical origin identification of winter jujube(Ziziphus jujuba Dongzao')by using multi-element fingerprinting with chemometrics

Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.

Source localization based on field signatures:Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services,navigation,gaming,and many other applications.This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station.The existing scatterers in the target area create unique scattered field interference at each source location.The unique field interference at each source location results in a unique field signature at the base station which is used for source localization.In the proposed method,the target area is divided into a grid with a step of less than half the wavelength.Each grid node is characterized by its field signature at the base station.Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization.The normalization of the field signatures avoids the need for time synchronization between the base station and the source.When a source transmits signals,the generated field signature at the base station is normalized and then correlated with the stored fingerprints.The maximum correlation value is given by the node to which the source is the closest.Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization.The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

Enhancing Indoor User Localization:An Adaptive Bayesian Approach for Multi-Floor Environments

Indoor localization systems are crucial in addressing the limitations of traditional global positioning system(GPS)in indoor environments due to signal attenuation issues.As complex indoor spaces become more sophisticated,indoor localization systems become essential for improving user experience,safety,and operational efficiency.Indoor localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database,but this can increase the computational burden in the online phase.Bayesian networks,which integrate prior knowledge or domain expertise,are an effective solution for accurately determining indoor user locations.These networks use probabilistic reasoning to model relationships among various localization parameters for indoor environments that are challenging to navigate.This article proposes an adaptive Bayesian model for multi-floor environments based on fingerprinting techniques to minimize errors in estimating user location.The proposed system is an off-the-shelf solution that uses existing Wi-Fi infrastructures to estimate user’s location.It operates in both online and offline phases.In the offline phase,a mobile device with Wi-Fi capability collects radio signals,while in the online phase,generating samples using Gibbs sampling based on the proposed Bayesian model and radio map to predict user’s location.Experimental results unequivocally showcase the superior performance of the proposed model when compared to other existing models and methods.The proposed model achieved an impressive lower average localization error,surpassing the accuracy of competing approaches.Notably,this noteworthy achievement was attained with minimal reliance on reference points,underscoring the efficiency and efficacy of the proposed model in accurately estimating user locations in indoor environments.

An Active Deception Defense Model Based on Address Mutation and Fingerprint Camouflage

The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.

BLS-identification:A device fingerprint classification mechanism based on broad learning for Internet of Things

The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

Analysis of GC×GC fingerprints from medicinal materials using a novel contour detection algorithm:A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm,PeakCET,designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram(GC×GC).This method innovatively combines contour edge tracking with affinity propagation(AP)clustering for peak detection in GC×GC fingerprints,the first in this field.Contour edge tracking signif-icantly reduces false positives caused by“burr”signals,while AP clustering enhances detection accuracy in the face of false negatives.The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin.PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples.Furthermore,this algorithm compares the GC×GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins.The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues.Each sample exhibits unique characteristic components alongside common ones,and vari-ations in content may influence their therapeutic effectiveness.This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional(2D)fingerprint analysis of GC×GC data.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction techniques.In the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management system.Moreover,an audio-based location distinction method is designed to detect the position change of the devices.Specifically,the audio frequency response(AFR)of several frequency points is utilized as a device signature.The device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different directions.Based on the features above,amovement detection algorithmfor device identification and location distinction is designed.Moreover,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and Severs.Extensive experiments are conducted to evaluate the performance of the proposed method.The experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.

AWeb Application Fingerprint Recognition Method Based on Machine Learning

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

Improving Optimal Fingerprinting Methods Requires a Viewpoint beyond Statistical Science

While being successful in the detection and attribution of climate change,the optimal fingerprinting method(OFM)may have some limitations from a physics-and-dynamics-based viewpoint.Here,an analysis is made on the linearity,noninteraction,and stationary-variability assumptions adopted by OFM.It is suggested that furthering OFM needs a viewpoint beyond statistical science,and the method should be combined with theoretical tools in the dynamics and physics of the Earth system,so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.

Research on the quality control technology for the Astragalus membranaceus extraction solution in cosmetics

Calycosin-7-glucoside,which is the target ingredient of the Astragalus membranaceus extraction solution,was studied by the high-performance liquid chromatography-diode array detector method(HPLC-DAD),and a high-performance liquid chromatography-electrospray ionization detector method(HPLC-CAD)was also established for fingerprint chromatogram analysis.Meanwhile,the heavy metal content was tested to build a quality control standard for Astragalus membranaceus extraction solution.Result shows a good linearity in 4-80μg/mL mass concentration range with the correlation coefficient r=0.999,RSD=2.4%,and the recovery rate is between 99.2%-102.7%.The fingerprint chromatogram analysis features with 14 specific peaks,including 5 identified components.The similarity is between 0.485 to 0.995.Content of heavy metals such as lead,mercury,and cadmium are below the detection limit,and the content of arsenic is less than 2 mg/kg.This research results can serve as process optimization basis and quality control standards for Astragalus membranaceus extraction solution,and provide reference to quality control and research guideline for other cosmetics using herbal extracts as raw materials.

Evaluation of the famous classic formula Sanhua decoction based on network pharmacology and multi-component quantitative analysis

Background:Sanhua decoction has significant effects in the treatment of stroke.The study of the Sanhua decoction material benchmark was carried out to analyze the value transfer relationship between the Chinese herbal pieces and the substance benchmark.Methods:Network pharmacology was employed to investigate the potential active components and molecular mechanisms of Sanhua decoction in the treatment of stroke.15 batches of Sanhua decoction lyophilized powder were prepared using traditional formulas and subjected to high-performance liquid chromatography analysis to generate fingerprints of the Sanhua decoction substance benchmarks.Then,a multi-component quantitative analysis method was established,allowing for the simultaneous determination of ten components,to study the transfer of quantity values between pieces and substance benchmarks.Results:60 active ingredients were screened from Sanhua decoction by network pharmacology,of which gallic acid,magnolol honokiol,physcion,and aloe-emodin may have a greater effect than other active components.63 key targets and 134 pathways were predicted as the potential mechanism of Sanhua decoction in treating stroke.The fingerprint similarity of the Sanhua decoction substance benchmarks was found to be good among the 15 batches,confirming the 19 common peaks.The content of the 10 components was basically consistent.The components’transfer rates were within 30%of their respective means.Conclusions:This study provided a comprehensive and reliable strategy for the quality evaluation of Sanhua decoction substance benchmarks and held significant importance in improving its application value.

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

A novel method for integrating chromatographic fingerprint analytical units of Chinese materia medica:the matching frequency statistical moment method

Objective To facilitate the quality evaluation suitable for the unique characteristics of Chinese materia medica(CMM)by developing and implementing a novel approach known as the matching frequency statistical moment(MFSM)method.Methods This study established the MFSM method.To demonstrate its effectiveness,we applied this novel approach to analyze Danxi Granules(丹膝颗粒,DXG)and its constituent herbal materials.To begin with,the ultra-performance liquid chromatography(UPLC)was applied to obtain the chromatographic fingerprints of DXG and its constituent herbal materi-als.Next,the MFSM was leveraged to compress and integrate them into a new fingerprint with fewer analytical units.Then,we characterized the properties and variability of both the original and integrated fingerprints by calculating total quantum statistical moment(TQSM)parameters,information entropy and information amount,along with their relative standard deviation(RSD).Finally,we compared the TQSM parameters,information entropy and infor-mation amount,and their RSD between the traditional and novel fingerprints to validate the new analytical method.Results The chromatographic peaks of DXG and its 12 raw herbal materials were divided and integrated into peak families by the MFSM method.Before integration,the ranges of the peak number,three TQSM parameters,information entropy and information amount for each peak or peak family of UPLC fingerprints of DXG and its 12 raw herbal materials were 95.07−209.73,9390−183064μv·s,5.928−21.33 min,22.62−106.69 min^(2),4.230−6.539,and 50530−974186μv·s,respectively.After integration,the ranges of these parameters were 10.00−88.00,9390−183064μv·s,5.951−22.02 min,22.27−104.73 min^(2),2.223−5.277,and 38159−807200μv·s,respectively.Correspondingly,the RSD of all the aforementioned pa-rameters before integration were 2.12%−9.15%,6.04%−49.78%,1.15%−23.10%,3.97%−25.79%,1.49%−19.86%,and 6.64%−51.20%,respectively.However,after integration,they changed to 0.00%,6.04%−49.87%,1.73%−23.02%,3.84%−26.85%,1.17%−16.54%,and 6.40%−48.59%,respectively.The results demonstrated that in the newly integrated fingerprint,the analytical units of constituent herbal materials,information entropy and information amount were significantly reduced(P<0.05),while the TQSM parameters remained unchanged(P>0.05).Additionally,the RSD of the TQSM parameters,information entropy,and information amount didn’t show significant difference before and after integration(P>0.05),but the RSD of the number and area of the integrated analytical units significantly decreased(P<0.05).Conclusion The MFSM method could reduce the analytical units of constituent herbal mate-rials while maintain the properties and variability from their original fingerprint.Thus,it could serve as a feasible and reliable tool to reduce difficulties in analyzing multi-compo-nents within CMMs and facilitating the evaluation of their quality.

Quality Standard of Tibetan Medicine Nardostachys jatamansi Herba Based on"An Integrated Plant but Multi-purpose"

[Objectives]To establish the quality standard of Nardostachys jatamansi Herba.[Methods]The characters and microscopical identification of N.jatamansi Herba were carried out.The contents of moisture,total ash,acid-insoluble ash and extract were determined according to the relevant methods of the Chinese Pharmacopoeia(2020 edition).Using chlorogenic acid and 3,5-O-dicaffeoylquinic acid as quality control indexes,TLC and HPLC methods were established for qualitative and quantitative determination,and HPLC fingerprints were established.[Results]The characteristics of character identification,microscopic identification and thin layer identification were obvious.The moisture content ranged from 2.7%to 7.8%,with an average value of 5.4%.The total ash content ranged from 6.7%to 16.2%,with an average of 11.0%.The acid-insoluble ash content ranged from 0.7%to 8.5%,with an average of 3.6%.Extractives content ranged from 20.9%to 34.4%,with an average of 29.7%.Chlorogenic acid content was between 0.45%and 1.30%,with an average value of 0.77%.The content of 3,5-O-dicaffeoylquinic acid ranged from 0.18%to 0.58%,with an average of 0.31%.The similarity of each batch was between 0.930 and 0.994,indicating that the quality of medicinal materials from different producing areas was stable.[Conclusions]The quality standard of N.jatamansi Herba was established,which could provide quality control basis for rational,comprehensive and efficient utilization of N.jatamansi DC.resources and clinical use.

Fingerprint Study of Polygonati Rhizoma with Steaming and Exposing to the Sun Alternatively for Different Times

[Objectives]To explore the influence of different times of steaming and exposing to the sun on the fingerprint of Polygonati Rhizoma by studying the HPLC fingerprint of Polygonati Rhizoma processed products with different times of steaming and exposing to the sun,and to provide a basis for the determination of the best processing technology of Polygonati Rhizoma.[Methods]SETSAIL II AQ-C 18(5μm×250 mm×4.6 mm)was used as the column,the column temperature was 30℃,pure water(A)and acetonitrile(B)were eluted gradually,0-10 min,B(5%-10%),10-30 min,B(10%-35%),30-40 min,B(35%-60%),40-45 min,B(60%-100%),flow rate 1 mL/min,absorption wavelength 200 nm.[Results]The relative retained peak area RSDs of the common peaks in the precision,reproducibility and stability tests were all less than 5%.There were 17 common peaks in the fingerprint of nine batches of samples,and the retention time of Peak 2 was basically the same as that of the reference peak of 5-HMF.Peak 4 mainly existed in the chromatogram of Sample 3 to Sample 5,peaks 5 and 11 mainly existed after Sample 3,peaks 9,14 and 16 mainly existed after Sample 6,and peaks 12 and 17 mainly existed after Sample 4.[Conclusions]A total of 17 common peaks were obtained,and the Peak 2 was the designated peak,and the chemical components of each processed product were different.

Screening of Anti-inflammatory Active Ingredients from Gancao Qinlian Extract and Study of Its Efficacy

[Objectives]To establish the chromatographic fingerprint of Gancao Qinlian Extracts(GQE)and reveal the possible material basis of the anti-inflammatory effect of GQE by the correlation analysis between the fingerprint chromatographic peaks of different components of GQE and its anti-inflammatory activity.[Methods]Ultra-performance liquid chromatography(UPLC)was used to detect the different ingredients of GQE to establish its chromatographic fingerprint and analyze the differences among the three medicine components;LPS stimulated RAW264.7 cells to construct an inflammatory cell model.The NO secretion of cells was detected by the Griess method.ELISA was used to detect the changes in TNF-αand IL-10 contents.RT-qPCR tested the mRNA expression levels of TNF-αand IL-10.Grey relational analysis was carried out by combining fingerprint chromatographic peak data and anti-inflammatory activity data.[Results]The GQE fingerprint was established,34 fingerprint characteristic peaks were calibrated,and 33 related chromatographic peaks were screened out.The corresponding chromatographic peaks in the three components were obtained,and the content of the components was calculated;the anti-inflammatory results showed that the content of NO,TNF-α,and the expression of TNF-αmRNA in the high and medium-dose groups of GQE were significantly lower than those in the blank group(P<0.01).The NO content and TNF-αmRNA expression in the high-dose group of GQE I was considerably lower than those in the blank group(P<0.01).The secretion of NO,TNF-α,and the expression of TNF-αmRNA in the high,medium,and low dose groups of GQE II were significantly lower than those in the blank group(P<0.01);the results of grey relational analysis showed that the correlation degree of the three components was GQE II>GQE>GQE I,and the characteristic fingerprint peaks 12,15,22,23,28,31,33 may be closely related to the anti-inflammatory effect.[Conclusions]The best component of the anti-inflammatory effect in GQE is water-soluble component,and its main components are flavonoids and alkaloids.These components can alleviate cellular inflammatory damage by inhibiting the excessive secretion of NO and reducing the expression of TNF-αmRNA.