Development of a Special Two-Dimensional Fingerprint for the Quality Evaluation of Euonymus Alatu by HPLC with Diode Array Detector Coupled with Chemiluminescence Detection

A special two-dimensional fingerprint developed by a hyphenated method, HPLC–DAD coupled with flow injection analysis and chemiluminescence detection (HPLC–DAD–FIA–CL), was applied to evaluate the quality of Euonymus alatu. Chromatographic fingerprint and active characteristics of Euonymus alatu from different habitats were investigated by HPLC–DAD–FIA–CL method. The similarity of two-dimensional fingerprint obtained by a vectorial angle method was used to evaluate the quality of Euonymus alatu. The results suggested that the two-dimensional fingerprint could reveal more objective conclusions for the quality of Euonymus alatu, and might be helpful to improve the quality control ability of herbal medicines.     

Discrimination of Alismatis Rhizoma based on chromatographic fingerprint,multiple components quantification,and pattern recognition analysis

A simple, sensible, and reliable HPLC–DAD method was first developed for fingerprint analysis of Alismatis Rhizoma, and then applied to analyze 85 samples from three main cultivated areas. In all, 40 common fingerprint peaks were designated, and six of which were definitely identified. Then, the combinatory analysis using similarity evaluation, principal component analysis, and orthogonal partial least square discriminant analysis revealed clear chemical consistency between samples from Fujian and Jiangxi provinces and substantial differences between those from Fujian/Jiangxi and Sichuan provinces. Furthermore, six components were dug out as potential chemical markers for distinguishing Alismatis Rhizoma from different areas, among which five were qualified for quantitative analysis. In conclusion, the combination of chemical fingerprint, multiple components quantification, and pattern recognition analysis was rather powerful and useful in discriminating Alismatis Rhizoma from different regions, which was a benefit for quality control.     

基于水溶性浸出物及HPLC指纹图谱评价不同贮存年限半夏的品质

研究不同贮存年限半夏药材的浸出物,建立浸出物的HPLC特征指纹图谱,为半夏药材品质评控提供参考。浸出物测定方法采用药典法;HPLC指纹图谱的色谱条件:采用C_(18)色谱柱(150 mm×4.6 mm,5μm),以水–甲醇为流动相,梯度洗脱,流量为0.8 m L/min,检测波长为260 nm,柱温为25℃,进样体积为50μL。采用相似度评价及聚类分析技术揭示14批样品的相似性及差异性。14批半夏浸出物有12批合格,2批不合格。建立14批半夏浸出物样品的高效液相指纹图谱,确定了3个共有峰,共有峰保留时间的相对标准偏差小于2%,峰面积的相对标准偏差差异较大。1~#~7~#半夏样品有12个共有峰,共有峰保留时间的相对标准偏差小于1.5%,峰面积的相对标准偏差差异较大。各批次药材化学成分组成及含量均存在一定差异。以半夏浸出物数据与其高效液相色谱指纹图谱数据为基础,将指纹图谱相似度评价与聚类分析结合起来,用浸出物含量及评价软件测评结果对半夏品质进行综合评估,可以更精确地对半夏药材进行质量控制。     

红外光谱分析数据特征指纹的可视化表达方法

提出一类红外光谱分析数据特征指纹的可视化表达方法。该方法采用基于贝叶斯准则的多元统计方法对原始数据进行投影变换，再以地维灰度图对变换后数据进行可视化表征，形成基于计算机图像的虚拟化学指纹图谱。将其用于鉴别3种不同产地中药当归样品的结果表明，它能有效提取红外光谱分析数据的特征指纹，实现以虚拟指纹图谱对药材产地的分类鉴别，从而为辨识复杂化学物质体系提供了新的技术手段。     

Quality control method for commercially available wild Jujube leaf tea based on HPLC characteristic fingerprint analysis of flavonoid compounds

Flavonoids are the main active components of natural medicinal plants with many physiological functions. In this study, an HPLC fingerprinting method based on the distribution and relative amount of 11 bioactive flavonoids was established for the quality evaluation of commercially available wild Jujube leaf tea (JLT) from China. Separation of the crude flavonoid extract was achieved on a column filled with C₁₈ material with a high carbon content. The flavonoids in wild JLT were identified based on UV spectroscopy and accurate mass measurements by TOF‐MS. Twenty‐one batches of practical samples collected from different habitats were analyzed by using the developed HPLC method to construct the HPLC characteristic fingerprint of wild JLT. Then, combined with clustering and similarity analyses, the HPLC characteristic fingerprint was used for the authentication and quality evaluation of commercial wild JLT. Results indicated that the proposed HPLC characteristic fingerprint reflected the inherent characteristics of wild JLT collected from different regions. Authenticity identification and quality control of commercially available wild Jujube tea were achieved based on the HPLC characteristic fingerprint analysis. This new approach to bioactive component profiling provided a promising reference method for the quality evaluation of commercial wild flower and plant tea.     

Fingerprint and multicomponent quantitative analysis for the quality evaluation of Sibiraea angustata leaves by HPLC-DAD and HPLC-ESI-MS/MS combined with chemometrics

Sibiraea angustata leaves, known as a traditional Tibetan medicine, have been specially used in the treatment of indigestion and obesity. In the study, a simple and sensitive high-performance liquid chromatography (HPLC) method with a diode array detector (DAD) was established to solve the problem of lacking quality standard of S. angustata leaves, including the fingerprint analysis and quantification of six characteristic components. The analytical method was validated for linearity, repeatability, stability, recovery, and specificity. Seventeen raw samples and 1 processed sample of S. angustata leaves were collected from different locations of China to establish the fingerprint. The chemometric methods, including similarity analysis (SA), principal component analysis (PCA), and hierarchical clustering analysis (HCA), were applied to distinguish the 18 batches of S. angustata samples. The results successfully sorted these samples into five clusters and kept in line with each other. According to the result of the fingerprint analysis, 21 peaks were extracted to be the common peaks and most of them were identified by mass spectrometry (MS) with electron-spray ionization (ESI) in the negative mode. Meanwhile, the loading plot of PCA further indicated that the peaks of neochlorogenic acid, chlorogenic acid, ferulic acid, rutin, hyperin, and isoquercitrin played a greater role in the discrimination among the 21 peaks. So the six components mentioned above were investigated as index constituents to evaluate the quality of S. angustata leaves from different locations. The study demonstrated that the developed new method was a beneficial approach for authentication and quality evaluation of S. angustata leaves.     

Multiple compounds determination and fingerprint analysis of Lidanpaishi tablet and keli by high-performance liquid chromatography

The objective of this paper was to establish an efficient method for quality control of Lidanpaishi (LDPS) tablet and keli, two famous traditional Chinese medicines. A simple and reliable high-performance liquid chromatography (HPLC) coupled with photodiode array detector (DAD) method was developed both for fingerprint analysis (FA) and quantitative determination. In quantitative analysis, linear regressions, limit of detection (LOD) and quantification (LOQ), intra-day and inter-day precisions, recovery, repeatability and stability were all tested and good results were obtained to simultaneously determine the 15 marker compounds, namely chlorogenic acid, rhaponticin, 6,7-dimethoxycoumarin, naringin, hesperidin, neohesperidin, baicalin, wogonoside, baicalein, wogonin, chrysin, honokiol, magnolol, emodin, and chrysophanol in the herbal drugs. In fingerprint analysis, 34 peaks were selected as the characteristic peaks to evaluate the similarities of different samples collected from different pharmaceutical companies in China according to the State Food and Drug Administration (SFDA) requirements, and two kinds of data, relative retention time (RRT) and relative peak area (RPA) were used to identify the common peaks in 15 samples for investigation. Furthermore, hierarchical cluster analysis (HCA) was also performed to evaluate the variation of the herbal drugs. The present approach, i.e. HPLC coupled with multiple compounds determination (MCD) and FA is a powerful and meaningful tool to comprehensively conduct the quality control of traditional Chinese medicines.     

Quality assessment of cortex cinnamomi by HPLC chemical fingerprint, principle component analysis and cluster analysis

HPLC fingerprint analysis, principle component analysis (PCA), and cluster analysis were introduced for quality assessment of Cortex cinnamomi (CC). The fingerprint of CC was developed and validated by analyzing 30 samples of CC from different species and geographic locations. Seventeen chromatographic peaks were selected as characteristic peaks and their relative peak areas (RPA) were calculated for quantitative expression of the HPLC fingerprints. The correlation coefficients of similarity in chromatograms were higher than 0.95 for the same species while much lower than 0.6 for different species. Besides, two principal components (PCs) have been extracted by PCA. PC1 separated Cinnamomum cassia from other species, capturing 56.75% of variance while PC2 contributed for their further separation, capturing 19.08% variance. The scores of the samples showed that the samples could be clustered reasonably into different groups corresponding to different species and different regions. The scores and loading plots together revealed different chemical properties of each group clearly. The cluster analysis confirmed the results of PCA analysis. Therefore, HPLC fingerprint in combination with chemometric techniques provide a very flexible and reliable method for quality assessment of traditional Chinese medicines.     

综合线性定量指纹法评价退热解毒注射液融合指纹图谱研究

该研究引入综合线性定性相似度S1、综合线性定量相似度P1％和指纹变异系数a3个参数建立了1种新颖的综合线性指纹图谱评价方法,从定性定量的角度全面评价了中药的整体质量.方法采用高效液相二极管阵列(HPLC/DAD)采集了退热解毒注射液样品在210、254、265、330、360 nm波长下的指纹图谱并进行数据融合,同时定...     

Quantitative analysis and chromatographic fingerprinting of the semen zizyphi spinosae by ultra-high-performance liquid chromatography coupled with diode-array detector

A simple and sensitive method was developed and validated for fingerprint analysis of semen zizyphi spinosae (SZS) and simultaneous determination of six flavonoids in SZS by ultra‐high‐performance liquid chromatography coupled to diode‐array detector (DAD). The analysis was performed on an Agilent ZORBAX Eclipse Plus C18 RRHD column. The column was maintained at 40°C and the eluents were monitored with DAD at 270 nm. A gradient elution of acetonitrile and water containing 20 mM sodium dihydrogen phosphate was used. The solvent flow rate was 0.4 mL/min. The method was validated. Standard calibration curves showed good linear behaviors (r=1.000) in the range of 0.33–201.00 μg/mL. Acceptable intra‐day precision (RSD<1.9%), inter‐day precision (RSD<4.0%), repeatability (RSD<4.1%) and recovery in the range of 97.4–104% were obtained. The validated method was successfully applied to obtain the chromatographic fingerprints and the contents of six flavonoids in 23 samples of SZS. The principal component analysis (PCA) had been applied for the chromatographic fingerprint analysis and quantitative analysis of six flavonoids to classify and discriminate the 23 samples of SZS. These results demonstrated that the method was very suitable in the analysis and quality control of SZS.     

Fingerprinting of complex mixtures with the use of high performance liquid chromatography, inductively coupled plasma atomic emission spectroscopy and chemometrics

The molecular and metal profile fingerprints were obtained from a complex substance, Atractylis chinensis DC—a traditional Chinese medicine (TCM), with the use of the high performance liquid chromatography (HPLC) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) techniques. This substance was used in this work as an example of a complex biological material, which has found application as a TCM. Such TCM samples are traditionally processed by the Bran, Cut, Fried and Swill methods, and were collected from five provinces in China. The data matrices obtained from the two types of analysis produced two principal component biplots, which showed that the HPLC fingerprint data were discriminated on the basis of the methods for processing the raw TCM, while the metal analysis grouped according to the geographical origin. When the two data matrices were combined into a one two-way matrix, the resulting biplot showed a clear separation on the basis of the HPLC fingerprints. Importantly, within each different grouping the objects separated according to their geographical origin, and they ranked approximately in the same order in each group. This result suggested that by using such an approach, it is possible to derive improved characterisation of the complex TCM materials on the basis of the two kinds of analytical data.

In addition, two supervised pattern recognition methods, K-nearest neighbors (KNNs) method, and linear discriminant analysis (LDA), were successfully applied to the individual data matrices—thus, supporting the PCA approach.     

纹党参与白条党参红外光谱的SIMCA聚类鉴别方法研究

以纹党参和白条党参的红外光谱为聚类分析的对象,研究了红外光谱结合SIMCA聚类分析法对纹党参和白条党参进行识别与分类的可行性.选取400 ～2 000 cm~(-1)范围内的光谱,通过基线补偿(Offset)和散射校正(MSC)等预处理后,采用SIMCA聚类分析法建立识别模型.结果表明,所建模型对纹党参和白条党参的识别率分别达92%和96%,拒绝率均为100%.用盲样对所建模型进行了测试,测试结果全部正确.该法可实现对纹党参和白条党参的快速鉴别.     

Profiling analysis of volatile compounds from fruits using comprehensive two-dimensional gas chromatography and image processing techniques

An image processing approach originating from the proteomics field has been transferred successfully to the processing of data obtained with comprehensive two-dimensional gas chromatographic separations data. The approach described here has proven to be a useful analytical tool for unbiased pattern comparison or profiling analyses, as demonstrated with the differentiation of volatile patterns (“aroma”) from fruits such as apples, pears, and quince fruit. These volatile patterns were generated by headspace solid phase microextraction coupled to comprehensive two-dimensional gas chromatography (HS-SPME-GC × GC). The data obtained from GC × GC chromatograms were used as contour plots which were then converted to gray-scale images and analyzed utilizing a workflow derived from 2D gel-based proteomics. Run-to-run variations between GC × GC chromatograms, respectively their contour plots, have been compensated by image warping. The GC × GC images were then merged into a fusion image yielding a defined and project-wide spot (peak) consensus pattern. Within detected spot boundaries of this consensus pattern, relative quantities of the volatiles from each GC × GC image have been calculated, resulting in more than 700 gap free volatile profiles over all samples. These profiles have been used for multivariate statistical analysis and allowed clustering of comparable sample origins and prediction of unknown samples. At present state of development, the advantage of using mass spectrometric detection can only be realized by data processing off-line from the identified software packages. However, such information provides a substantial basis for identification of statistically relevant compounds or for a targeted analysis.     

直观推导式演进特征投影法解析多环芳烃的重叠色谱峰

用高效液相色谱法(HPLC)测定多环芳烃时,因芴、苊和菲,茚(1,2,3-cd)芘和苯并(g,h,i)苝的色谱峰严重重叠而影响测定结果。本研究用高效液相色谱-二极管阵列检测器(DAD)和荧光检测器(FLD)测定多环芳烃,在激发波长λex=230nm,发射波长λem=300～500nm范围内采集重叠峰的HPLC-FLD二维色谱数据,再用直观推导式演进特征投影法(HELP)解析它们的重叠色谱峰,分辨结果令人满意。该方法对重叠组分的分辨下限为0.02mg/L。结果表明,用二维色谱荧光数据解析色谱重叠峰,灵敏度更高,可用于环境样品中多环芳烃的测定。     

基于超高效液相色谱-紫外检测定量指纹图谱结合化学模式识别的复方金钱草颗粒质量评价

复方金钱草颗粒具有利尿、抑制泌尿系结石形成、抗炎、抗氧化作用,且具有较大的市场需求。因此,采用超高效液相色谱-紫外检测(UPLC-UV)法建立定量指纹图谱,并结合化学模式识别技术对不同年份的复方金钱草颗粒进行质量评价,可为其质量控制提供依据。采用聚类分析(HCA)和主成分分析(PCA)等化学模式识别技术对35批复方金钱草颗粒样品的指纹图谱数据进行分析,筛选出质量差异标志物芒果苷和异芒果苷,并对二者进行含量测定。在复方金钱草颗粒指纹图谱中共指认出12个共有峰,且35批样品的相似度均在0.952以上。在HCA中,将35批样品分为了两类,其中2018年和2019年的样品为一类,2020年和2021年的样品为一类。此外,PCA结果显示了与聚类分析相同的聚类趋势。在此基础上,进一步通过正交偏最小二乘法分析 (OPLS-DA)筛选出了导致2018年、2019年与2020年、2021年的样品产生差异的差异标志物芒果苷和异芒果苷。以两个差异标志物芒果苷和异芒果苷为指标进行含量测定,结果显示色谱峰的分离度良好,线性关系良好,平均加标回收率分别为101.7%~105.6%和103.4%~105.5%,且相对标准偏差(RSD)均低于1.43%。在35批样品中,2020年、2021年的样品与2018年、2019年的样品相比,芒果苷与异芒果苷含量更高且波动范围更小。该研究建立了准确、可靠的复方金钱草颗粒质控方法,实现了对不同年份的复方金钱草颗粒样品合理、有效的质量评价,可为建立更系统、更全面的质量控制标准提供借鉴与参考。     

Quality Evaluation of Atractylodis Macrocephalae Rhizoma Based on Combinative Method of HPLC Fingerprint,Quantitative Analysis of Multi-Components and Chemical Pattern Recognition Analysis

A method for the quality evaluation of Atractylodis Macrocephalae Rhizoma (AMR) based on high-performance liquid chromatography (HPLC) fingerprint, HPLC quantification, and chemical pattern recognition analysis was developed and validated. The fingerprint similarity of the 27 batches of AMR samples was 0.887–0.999, which indicates there was very limited variance between the batches. The 27 batches of samples were divided into two categories according to cluster analysis (CA) and principal component analysis (PCA). A total of six differential components of AMR were identified in the partial least-squares discriminant analysis (PLS-DA), among which atractylenolide I, II, III, and atractylone counted 0.003–0.045%, 0.006–0.023%, 0.001–0.058%, and 0.307–1.175%, respectively. The results indicate that the quality evaluation method could be used for quality control and authentication of AMR.     

Efficient recovery of electrophoretic profiles of nucleoside metabolites from urine samples by multivariate curve resolution

Chemometric techniques usually employed in purity assessment and resolution of multicomponent peaks have been applied to analytical data from complex biological samples obtained with CE‐DAD. In the assessment of the purity of the electrophoretic peaks, the orthogonal projection approach, the orthogonal projection approach with Durbin–Watson criterion, and the simple‐to‐use interactive self‐modeling mixture analysis method have been employed. Multivariate curve resolution with alternating least squares has been successfully implemented to resolve co‐migrating peaks of metabolites in CE‐DAD and to recover qualitative and quantitative information about co‐migrating components of urine extract. The main challenge consisted of developing high‐quality multivariate curve resolution with alternating least squares models of multicomponent peaks acquired during the CE analysis of nucleoside patterns in 18 urine samples. The recovered ultraviolet visible (UV–Vis) spectra have been employed to identify additional nucleosides, such as 1‐methylinosine, 2‐methylguanosine, and 1‐methylguanosine, whose presence in the metabolic profile produced by the applied CE‐DAD method has not yet been recognized. Concentration profiles of these compounds can be used in metabonomic studies.     

Comparison of high-speed counter-current chromatography and high-performance liquid chromatography on fingerprinting of Chinese traditional medicine

Active constituents of Chinese traditional medicine (CTM) are influenced by soils, climates, and growth stages. Therefore, fingerprint is important in quality control of CTM. Our studies showed for the first time that high-speed counter-current chromatography (HSCCC) could be applied in the development of fingerprint of CTM. Twelve components were separated from extracts of three crude samples of Salvia miltiorrhiza Bunge, which were collected from different growth locations. All 12 components of each sample were isolated within 13 h coming with good correspondence by using HSCCC. Relative standard deviation (R.S.D.) values of the retention time in HSCCC separation was less than 3% satisfied the precision requirements of national standards in the fingerprint development. As a new approach, HSCCC was compared to the conventional approach-HPLC in our experiments. The fingerprint developed by HPLC comprised 11 peaks that collected within 45 min. R.S.D. values of retention times of the corresponding peaks in HPLC analysis were very small (maximum 0.66% and average 0.10%). In conclusion, HSCCC was a feasible method to be used in the development of fingerprint of CTM.     

Prediction of Molecular Properties Using Molecular Topographic Map

Prediction of molecular properties plays a critical role towards rational drug design. In this study, the Molecular Topographic Map (MTM) is proposed, which is a two-dimensional (2D) map that can be used to represent a molecule. An MTM is generated from the atomic features set of a molecule using generative topographic mapping and is then used as input data for analyzing structure-property/activity relationships. In the visualization and classification of 20 amino acids, differences of the amino acids can be visually confirmed from and revealed by hierarchical clustering with a similarity matrix of their MTMs. The prediction of molecular properties was performed on the basis of convolutional neural networks using MTMs as input data. The performance of the predictive models using MTM was found to be equal to or better than that using Morgan fingerprint or MACCS keys. Furthermore, data augmentation of MTMs using mixup has improved the prediction performance. Since molecules converted to MTMs can be treated like 2D images, they can be easily used with existing neural networks for image recognition and related technologies. MTM can be effectively utilized to predict molecular properties of small molecules to aid drug discovery research.     

Rapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis

Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect.Near-infrared(NIR) and mid-infrared(MIR) technology combined with supervised pattern recognition based on partial least-squares discriminant analysis (PLSDA) was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L.samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work.Recognition rates of 99.24%,100%and 99.49%for original fingerprint,multiple scatter correct(MSC) fingerprint and second derivative(2nd derivative) fingerprint of NIR spectra were achieved by PLSDA models,respectively.Meanwhile,a perfect recognition rate of 100%was obtained for the above three fingerprint models of MIR spectra.In conclusion.PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces of A.catechu.