HPLC-ELSD法测定硫酸庆大霉素碳酸铋胶囊中庆大霉素C组分及有关物质含量

目的为硫酸庆大霉素碳酸铋胶囊中庆大霉素C组分及有关物质建立质控方法。方法采用HPLC-ELSD方法,色谱柱:GRACEApollo C18柱(250 mm×4.6 mm,5μm);流动相:0.2 mol/L三氟乙酸溶液-甲醇(96:4,V/V),流速0.6 mL/min;柱温30℃;进样量20μL;Waters低温分流型蒸发光散射检测器,载气流量40 psi,漂移管温度55℃。结果所建方法通过方法学验证,庆大霉素C组分与各杂质间分离完全,庆大霉素、西索米星和小诺霉素分别在0.5~6 mg/mL、25~100μg/mL和25~100μg/mL的浓度范围内,各浓度的对数值与对应色谱峰面积的对数值呈现出良好的线性。采用所建方法对市售16家生产企业43批次硫酸庆大霉素碳酸铋胶囊样品进行测定,获得该制剂产品中各组分及有关物质的数据。结论所建方法是在中国药典方法基础上优化并验证适用于硫酸庆大霉素碳酸铋胶囊C组分及有关物质的含量测定,方法简便准确,专属性强,稳定性好。由获得的实验数据可知,该复方制剂中C组分及有关物质的含量在各厂家间或同厂家不同批次间差异显著,可能存在影响用药安全的潜在隐患,建议在该剂型质量标准中增加相关质控项。

高效液相-蒸发光散射检测器(HPLC-ELSD)测定红参中的精氨酸单糖苷(AF)及精氨酸双糖苷(AFG)含量

目的建立一种同时测定人参加工品中精氨酸单糖苷(AF)和精氨酸双糖苷(AFG)含量的方法。[方法]用PrevailTMC18column(4.6 mm×250 mm,5μm)色谱柱进行检测。流动相A:色谱乙腈,流动相B:5.0mmol/L七氟丁酸的0.7%三氟醋酸溶液。色谱条件为[0%A(0 min);0%A(5 min);15%A(8 min);35%A(25 min)];流速0.8 mL/min;漂移管温度为115℃;气体流量3.2 L/min。[结果]AF和AFG的检测限分别为0.015和0.010 mg/mL;线性关系相关系数分别为,AF:0.9997,AFG:0.9999,表明线性关系良好。AF和AFG检测的精密度,重复性,稳定性以及回收率的相对标准偏差分别为0.43%&0.37%,0.43%&0.55%,0.43%&0.49%,0.45%&0.15%。AF和AFG检测的回收率分别为99.5%&100%,表明方法稳定。经检测,高丽红参,中国红参和生晒参中AF含量分别为0.74%,0.91%和1.14%,AFG含量分别为6.69%,5.12%和0.85%。[结论]这种方法成功用于高丽红参,中国红参及生晒参中AF及AFG的检测;且红参中AF和AFG含量明显高于生晒参。该方法测定结果可靠,缩短了测定时间,较少杂质干扰。但因为本研究考察氨基酸种类较少,当衍生物种类较多时,仍需进一步考察其分离度。

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.

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.

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.

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.

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.

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.

The significance of metabolic fingerprinting in carcinogenesis

Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)worldwide in 2020:8.8 million(48%)in females and 9.3 million(52%)in males,giving a male:female ratio of 10:9.5.It may be initiated by the action of biological,physical,or chemical agents that cause a non-lethal,permanent,DNA error on the cell with a consequence of altered cell metabolism.This altered cell metabolism include the Warburg effect,altered lipid and amino acid metabolism and production of various metabolites.It also results in unique metabolic dependencies that,in some cases,can be targeted with precision medicine and nutrition,including drugs that selectively target metabolic enzymes.Metabolic fingerprinting has been applied to the study of carcinogenesis and is particularly helpful in early diagnosis,staging and choice of treatment,thus improving health outcomes.This technology could therefore be harnessed effectively while combining with other omics technologies.

中药黑骨藤HPLC-ELSD指纹图谱研究

目的 建立中药黑骨藤HPLC-ELSD指纹图谱并结合聚类分析和主成分分析进行药材质量评价。方法 采用通用型ELSD检测器建立17个不同产地的黑骨藤药材的指纹图谱,采用HPLC-Q-TOF-MS技术对黑骨藤中化学成分进行表征,并通过指纹图谱相似度评价系统、层次聚类分析和主成分分析评价药材质量。结果 指纹图谱共标定了14个共有峰,采用液质联用技术共鉴定了58个化合物。除2批广西产地黑骨藤指纹图谱与对照图谱的相似度为0.663和0.826,其他15批产地的指纹图谱与对照图谱相似度均在0.9以上。指纹图谱相似度评价、层次聚类分析和主成分分析结果互相印证。结论 黑骨藤HPLC-ELSD指纹图谱研究结合聚类分析和主成分分析为该药材的真伪鉴别、质量控制提供了参考。

灵芝孢子粉脂质成分HPLC-ELSD指纹图谱构建及含量测定

以16批(S1~S16)不同来源的灵芝(Ganoderma lucidum)孢子粉为材料,构建灵芝孢子粉脂质成分高效液相色谱-蒸发光散射检测(HPLC-ELSD)指纹图谱,将S1~S16的HPLC-ELSD指纹图谱数据导入中药色谱指纹图谱相似度评价系统(2012版),指认共有峰和评价相似度,采用HPLC-ELSD测定S1~S16中亚油酸、棕榈酸、油酸和麦角甾醇含量。结果表明:共指认4个共有峰,分别为亚油酸、棕榈酸、油酸和麦角甾醇;除S14外,其他15批灵芝孢子粉中脂质成分HPLC-ELSD指纹图谱相似度高,较为稳定;不同来源的灵芝孢子粉中亚油酸、棕榈酸和油酸的含量差异较大,麦角甾醇含量较为稳定。

5G Ultra-Dense Network Fingerprint Positioning Method Based on Matrix Completion

The problem of high-precision indoor positioning in the 5G era has attracted more and more attention.A fingerprint location method based on matrix completion(MC-FPL)is proposed for 5G ultradense networks to overcome the high costs of traditional fingerprint database construction and matching algorithms.First,a partial fingerprint database constructed and the accelerated proximal gradient algorithm is used to fill the partial fingerprint database to construct a full fingerprint database.Second,a fingerprint database division method based on the strongest received signal strength indicator is proposed,which divides the original fingerprint database into several sub-fingerprint databases.Finally,a classification weighted K-nearest neighbor fingerprint matching algorithm is proposed.The estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint database.The simulation results show that the MC-FPL algorithm can reduce the complexity of database construction and fingerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.

Radio Frequency Fingerprinting Identification Using Semi-Supervised Learning with Meta Labels

Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.

基于HPLC-ELSD指纹图谱和多成分定量的浙贝母与湖北贝母质量差异研究

建立浙贝母、湖北贝母HPLC-ELSD指纹图谱,结合多成分定量分析,比较两种贝母属药材的差异。采用Waters ACQUITY HSS T3(4.6 mm×250 mm,5μm)色谱柱,以乙腈-0.1%三乙胺溶液为流动相,流速为1.1 mL/min,梯度洗脱;柱温为38℃;蒸发光散射检测;建立浙贝母和湖北贝母HPLC-ELSD指纹图谱,通过化学计量学方法和5种生物碱类成分的含量测定比较浙贝母和湖北贝母的差异。结果显示,浙贝母指纹图谱标定7个共有峰,而湖北贝母有8个;指认出其中6个峰,分别为伊贝辛、贝母辛、贝母素甲、贝母素乙、异贝母甲素、湖贝甲素,其中湖贝甲素为湖北贝母的专属性成分;HCA和PCA均能很好地区分浙贝母和湖北贝母,OPLS-DA共找到4个差异性标志物,含测结果显示,浙贝母中贝母素甲的含量明显高于湖北贝母,而贝母辛、贝母素乙和异贝母甲素的含量则明显低于湖北贝母。该方法可以有效鉴别浙贝母和湖北贝母质量的差异性,为其质量控制提供参考。

HPLC-ELSD法测定发酵乳中木糖醇含量

本研究目的是建立一种发酵乳中木糖醇含量的检测方法,样品经沉淀剂沉淀蛋白质,采用固相萃取技术净化富集,利用高效液相蒸发光散射检测器检测,外标法定量。采用该方法木糖醇能得到很好的分离,在0.1~2mg/mL范围内线性关系良好,相关系数高于0.999,方法检出限0.05 g/100g,定量限0.2 g/100g,精密度(RSD)为4.93%,添加量为0.2、1、3g/100g时,平均回收率为81.9%~102.1%。该方法结果准确且重复性好,适用于发酵乳中木糖醇的检测。

保元抗疲咀嚼片HPLC-ELSD特征图谱研究及4种功效成分的含量测定

目的建立保元抗疲咀嚼片HPLC-ELSD特征图谱和4种代表性功效成分的含量测定方法。方法采用HPLC-ELSD法,色谱柱为Agilent 5 HC C_(18)柱(250 mm×4.6 mm,5μm),流动相为乙腈-0.1%甲酸水溶液,梯度洗脱,柱温25℃,流速1.0 mL·min^(-1),蒸发光散射检测器(ELSD)蒸发温度为120℃,气体流量为1.6 SLM。结果建立了保元抗疲咀嚼片HPLCELSD特征图谱,标定了11个特征峰,并鉴定出甘草苷、人参皂苷Rg_(1)、人参皂苷Re、人参皂苷Rb_(1)、甘草酸铵5个成分,15批样品特征图谱的相似度均>0.920。建立了保元抗疲咀嚼片中4种功效成分的含量测定方法,人参皂苷Rg_(1)、人参皂苷Re、人参皂苷Rb_(1)、甘草酸的含量分别为1.01~1.09 mg·g^(-1)、0.85~0.96 mg·g^(-1)、1.42~1.70 mg·g^(-1)、1.53~1.84mg·g^(-1)。结论采用HPLC-ELSD特征图谱结合多功效成分含量测定,可全面评价与控制保元抗疲咀嚼片的质量。

HUID:DBN-Based Fingerprint Localization and Tracking System with Hybrid UWB and IMU

High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.

Payload Symbol-Based Nonlinear RF Fingerprint for Wireless QPSK-OFDM Devices

Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the performance of RF Fingerprint(RFF)based on preamble with fixed duration, a nonlinear RF fingerprinting method based on payload symbols is proposed for the wireless OFDM communication with the bit mapping scheme of QPSK. The wireless communication system is modeled as a Hammerstein system containing the nonlinear transmitter and multipath fading channel. A parameter separation technique based on orthogonal polynomial is presented for the estimation of the parameters of the Hammerstein system. The Hammerstein system parameter separation technique is firstly used to estimate the linear parameter with the training signal, which is used to compensate the adverse effect of the linear channel for the demodulation of the successive payload symbols. The demodulated payload symbols are further used to estimate the nonlinear coefficients of the transmitter with the Hammerstein system parameter separation technique again, which is used as the novel RFF for the authentication of the QPSK-OFDM device. Numerical simulations have verified the proposed method, which can also be extended to the OFDM signals with other bit mapping schemes.

Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.

Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

A robust radio map is essential in implementing a fingerprint-based indoor positioning system(IPS).However,the offline site survey to manually construct the radio map is time-consuming and labour-intensive.Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys.This paper presents a novel fingerprint interpolator using a multi-path loss model(MPLM)to create the virtual fingerprints from the collected sample data based on different signal paths from different access points(APs).Based on the historical signal data,the poor signal paths are identified using their standard deviations.The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths.By consideringmultipath signal propagations from different APs,the inherent noise from these signal paths can be alleviated.Firstly,locations of the signal data with standard deviations higher than the threshold are identified.The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints.The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance.Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44%compared to the conventional interpolation techniques such as the Inverse DistanceWeighting,Kriging,and single Path LossModel.As a result,we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.