Rapid quality assessment of Gentianae Macrophyllae Radix based on near-infrared spectroscopy and capillary electrophoresis

The aim of this study was to establish a rapid quality assessment method for Gentianae Macrophyllae Radix (RGM) using near-infrared (NIR) spectra combined with chemometric analysis. The NIR spectra were acquired using an integrating sphere diffuse reflectance module, using air as the reference. Capillary electrophoresis (CE) analyses were performed on a model P/ACE MDQ Plus system. Partial least squares-discriminant analysis qualitative model was developed to distinguish different species of RGM samples, and the prediction accuracy for all samples was 91%. The CE response values at each retention time were predicted by building a partial least squares regression (PLSR) calibration model with the CE data set as the Y matrix and the NIR spectra data set as the X matrix. The converted CE fingerprints basically match the real ones, and the six main peaks can be accurately predicted. Transforming NIR spectra fingerprints into the form of CE fingerprints increases its interpretability and more intuitively demonstrates the components that cause diversity among samples of different species and origins. Loganic acid, gentiopicroside, and roburic acid were considered quality indicators of RGM and calibration models were built using PLSR algorithm. The developed models gave root mean square error of prediction of 0.2592% for loganic acid, 0.5341% for gentiopicroside, and 0.0846% for roburic acid. The overall results demonstrate that the rapid quality assessment system can be used for quality control of RGM.     

Multi-wavelength HPLC fingerprints from complex substances: An exploratory chemometrics study of the Cassia seed example

Multi-wavelength fingerprints of Cassia seed, a traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography (HPLC) at two wavelengths with the use of diode array detection. The two data sets of chromatograms were combined by the data fusion-based method. This data set of fingerprints was compared separately with the two data sets collected at each of the two wavelengths. It was demonstrated with the use of principal component analysis (PCA), that multi-wavelength fingerprints provided a much improved representation of the differences in the samples. Thereafter, the multi-wavelength fingerprint data set was submitted for classification to a suite of chemometrics methods viz. fuzzy clustering (FC), SIMCA and the rank ordering MCDM PROMETHEE and GAIA. Each method highlighted different properties of the data matrix according to the fingerprints from different types of Cassia seeds. In general, the PROMETHEE and GAIA MCDM methods provided the most comprehensive information for matching and discrimination of the fingerprints, and appeared to be best suited for quality assurance purposes for these and similar types of sample.     

Authentication of Radix Aucklandiae and its substitutes by GC-MS and hierarchical clustering analysis

Radix Aucklandiae (Muxiang in Chinese), the dried root of Aucklandia lappa, is used as a medicinal material for digestive system disorders in traditional Chinese medicine for centuries. Owing to the similarity of morphologies and trade names, Radix Vladimiriae (Chuan-Muxiang), the roots of Vladimiria souliei and V. souliei var. cinerea, and Radix Inulae (Tu-Muxiang), the roots of Inula helenium and Inula racemosa, as well as the renal toxic aristolochic acid containing Radix Aristolochiae (Qing-Muxiang), the roots of Aristolochia debilis and Aristolochia contorta, are often used confusedly as the substitutes of Radix Aucklandiae. In order to ensure the effective and safe utility of Radix Aucklandiae, a GC-MS method was developed to generate the chemical profiles of essential oils of Radix Aucklandiae and its substitutes. In addition, hierarchical clustering analysis was used to compare the similarities of these chemical profiles. It was found that all the samples of A. lappa have similar chemical profiles and were clustered into one group, while the samples of Radix Vladimiriae, Radix Inulae, and Radix Aristolochiae were clustered into their own independent groups, respectively, suggesting that together with hierarchical clustering analysis, chemical profiles of essential oils generated by GC-MS could objectively discriminate Radix Aucklandiae from its common substitutes.     

Chromatographic profiles of <Emphasis Type="Italic">Phyllanthus</Emphasis> aqueous extracts samples: a proposition of classification using chemometric models

Taking in consideration the global analysis of complex samples, proposed by the metabolomic approach, the chromatographic fingerprint encompasses an attractive chemical characterization of herbal medicines. Thus, it can be used as a tool in quality control analysis of phytomedicines. The generated multivariate data are better evaluated by chemometric analyses, and they can be modeled by classification methods. “Stone breaker” is a popular Brazilian plant of Phyllanthus genus, used worldwide to treat renal calculus, hepatitis, and many other diseases. In this study, gradient elution at reversed-phase conditions with detection at ultraviolet region were used to obtain chemical profiles (fingerprints) of botanically identified samples of six Phyllanthus species. The obtained chromatograms, at 275 nm, were organized in data matrices, and the time shifts of peaks were adjusted using the Correlation Optimized Warping algorithm. Principal Component Analyses were performed to evaluate similarities among cultivated and uncultivated samples and the discrimination among the species and, after that, the samples were used to compose three classification models using Soft Independent Modeling of Class analogy, K-Nearest Neighbor, and Partial Least Squares for Discriminant Analysis. The ability of classification models were discussed after their successful application for authenticity evaluation of 25 commercial samples of “stone breaker.”     

Discrimination of Polygoni Multiflori radix and Cynanchi Auriculati radix using ultra‐high performance liquid chromatography fingerprints and chemical pattern recognition

In this work, a strategy was proposed to discriminate Polygoni Multiflori Radix (PMR) and its adulterant (Cynanchi Auriculati Radix, CAR). Ultra‐high performance liquid chromatography (UHPLC) fingerprints were established to analyze samples containing PMR, CAR and mixtures simultaneously. Multivariate classification methods were applied to analyze the obtained UHPLC fingerprints, including principal component analysis (PCA), partial least square discriminant analysis (PLS‐DA), soft independent modeling of class analogy (SIMCA), support vector machine discriminant analysis (SVMDA) and counter‐propagation artificial neural network (CP‐ANN). A plot of PCA score showed that PMR and CAR samples belonged to separate clusters (PMR class and CAR class), and samples of mixtures were located near PMR or CAR classes. Analysis by PLS‐DA, SVMDA and CP‐ANN performed well for recognition and prediction in terms of PMR and CAR samples. Moreover, the PLS‐DA method performed best in the detection of adulterated samples, even if the adulterant was about 25%.     

Comprehensive evaluation of chemical stability of Xuebijing injection based on multiwavelength chromatographic fingerprints and multivariate chemometric techniques

A strategy for monitoring and analyzing the chemical stability of Xuebijing injection (XBJ) by multiwavelength chromatographic fingerprints and multivariate classification techniques is presented in this paper. Multiwavelength chromatographic fingerprints were constructed using chromatographic data obtained at four wavelengths (260, 280, 320, and 400?nm). The raw chromatography data were preprocessed by noise reduction, baseline correction, data normalization, and interval correlation optimized shifting (icoshift). Using this method, fingerprints of 166 samples of XBJ subjected to different forced degradation conditions (irradiation, high temperature, and a range of pH values) were properly represented. Forty-one chemical components were identified using the iPeak program. In addition, the identified peak area profiling of chemical components were used for multivariate classification analysis. Principal component analysis (PCA) and Ward’s method were used to classify different XBJ degradation samples. The PCA score plot showed that XBJ degradation samples were clustered into four groups, and the results are confirmed by Ward’s method. Ten key chemical markers under different degradation conditions were found and identified by counterpropagation artificial neural networks (CP-ANN), statistical t-tests, and UPLC-Q-TOF-MS. The results suggest that the proposed strategy could be successfully applied to the comprehensive analysis of complex chemical systems.     

Application of HPTLC Multiwavelength Imaging and Color Scale Fingerprinting Approach Combined with Multivariate Chemometric Methods for Medicinal Plant Clustering According to Their Species

In the current study, multiwavelength detection combined with color scales HPTLC fingerprinting procedure and chemometric approach were applied for direct clustering of a set of medicinal plants with different geographical growing areas. The fingerprints profiles of the hydroalcoholic extracts obtained after single and double development and detection under 254 nm and 365 nm, before and after selective spraying with specific derivatization reagents were evaluated by chemometric approaches. Principal component analysis (PCA) with factor analysis (FA) methods were used to reveal the contribution of red (R), green (G), blue (B) and, respectively, gray (K) color scale fingerprints to HPTLC classification of the analyzed samples. Hierarchical cluster analysis (HCA) was used to classify the medicinal plants based on measure of similarity of color scale fingerprint patterns. The 1-Pearson distance measurement with Ward’s amalgamation procedure proved to be the most convenient approach for the correct clustering of samples. Data from color scale fingerprints obtained for double development procedure and multiple visualization modes combined with appropriate chemometric methods proved to detect the similar medicinal plant extracts even though they are from different geographical regions, have different storage conditions and no specific markers are individually extracted. This approach could be proposed as a promising tool for authentication and identification studies of plant materials based on HPTLC fingerprinting analysis.     

Validation of green‐solvent extraction combined with chromatographic chemical fingerprint to evaluate quality of Stevia rebaudiana Bertoni

An approach that combined green‐solvent methods of extraction with chromatographic chemical fingerprint and pattern recognition tools such as principal component analysis (PCA) was used to evaluate the quality of medicinal plants. Pressurized hot water extraction (PHWE) and microwave‐assisted extraction (MAE) were used and their extraction efficiencies to extract two bioactive compounds, namely stevioside (SV) and rebaudioside A (RA), from Stevia rebaudiana Bertoni (SB) under different cultivation conditions were compared. The proposed methods showed that SV and RA could be extracted from SB using pure water under optimized conditions. The extraction efficiency of the methods was observed to be higher or comparable to heating under reflux with water. The method precision (RSD, n = 6) was found to vary from 1.91 to 2.86% for the two different methods on different days. Compared to PHWE, MAE has higher extraction efficiency with shorter extraction time. MAE was also found to extract more chemical constituents and provide distinctive chemical fingerprints for quality control purposes. Thus, a combination of MAE with chromatographic chemical fingerprints and PCA provided a simple and rapid approach for the comparison and classification of medicinal plants from different growth conditions. Hence, the current work highlighted the importance of extraction method in chemical fingerprinting for the classification of medicinal plants from different cultivation conditions with the aid of pattern recognition tools used.     

Ganoderma species discrimination by dual-mode chromatographic fingerprinting: A study on stationary phase effects in hydrophilic interaction chromatography and reduction of sample misclassification rate by additional use of reversed-phase chromatography

Acetonitrile–water extracts of several Ganoderma species – a mushroom being used in Traditional Chinese Medicine – were analysed by liquid chromatography–UV detection in hydrophilic interaction chromatography (HILIC) and reversed-phase (RP) elution modes. A set of six polar stationary phases was used for HILIC runs. These columns had remarkably different separation properties under binary gradient conditions as evinced by hierarchical cluster analysis on retention patterns of seven test compounds. Complementary measurements of RP chromatograms were carried out on a C₁₈ packing. Injection precision (n = 5) and intra-day precision (n = 5) were each <2.0% RSD (HILIC) and <0.7% RSD (RP) for relative retention times of main characteristic peaks of a sample extract while for relative peak areas RSD values were max. 6.8%. Repetitive analysis (n = 7) of a processed sample stored in the autosampler tray for 48 h was used to confirm within-sequence sample stability. Eleven Ganoderma lucidum samples served as training set for the construction of column-specific simulated mean chromatograms. Validation with twelve samples comprising G. lucidum, Ganoderma sinense, Ganoderma atrum, and Ganoderma tsugae by correlation coefficient based similarity evaluation of peak patterns showed that a discrimination of G. lucidum from other Ganoderma species by means of chromatographic fingerprints is conceptually possible on all columns, except of a bare silica packing. The importance of the combined use of RP and HILIC fingerprints to improve the rate of correct sample classification was demonstrated by the fact that each one G. sinense specimen was wrongly assigned being G. lucidum by all HILIC fingerprints but not the RP fingerprint and vice versa. The present data revealed that (i) the analysis of complex biological materials by quasi orthogonal chromatographic modes such as HILIC and RP may deliver more discriminative information than single-mode approaches which strengthens the reliability of fingerprint-based sample classification and (ii) different retention and selectivity characteristics of polar bonded silica packings in the HILIC elution mode may only have a minor impact on chemometric sample discrimination capabilities in such kind of pattern-oriented metabolomics separation problems.     

Structure-related clustering of gene expression fingerprints of thp-1 cells exposed to smaller polycyclic aromatic hydrocarbons

This study was undertaken to test the hypothesis that structurally similar PAHs induce similar gene expression profiles. THP-1 cells were exposed to a series of 12 selected PAHs at 50 µM for 24 hours and gene expressions profiles were analyzed using both unsupervised and supervised methods. Clustering analysis of gene expression profiles revealed that the 12 tested chemicals were grouped into five clusters. Within each cluster, the gene expression profiles are more similar to each other than to the ones outside the cluster. One-methylanthracene and 1-methylfluorene were found to have the most similar profiles; dibenzothiophene and dibenzofuran were found to share common profiles with fluorine. As expression pattern comparisons were expanded, similarity in genomic fingerprint dropped off dramatically. Prediction analysis of microarrays (PAM) based on the clustering pattern generated 49 predictor genes that can be used for sample discrimination. Moreover, a significant analysis of Microarrays (SAM) identified 598 genes being modulated by tested chemicals with a variety of biological processes, such as cell cycle, metabolism, and protein binding and KEGG pathways being significantly (p < 0.05) affected. It is feasible to distinguish structurally different PAHs based on their genomic fingerprints, which are mechanism based.     

A simple and effective method for identification of Fraxini Cortex from different sources by multi-mode fingerprint combined with chemometrics

Fraxini Cortex has a long history of being used as a medicinal plant in traditional Chinese medicine. However, it is challenging to differentiate and make quality evaluations for Fraxini Cortex from different origins due to their similarities in morphological features, as well as general chemical composition using traditional chemical analytical methods. In this study, a simple and effective method was developed to identify Fraxini Cortex from different origins by multi-mode fingerprint combined with chemometrics. Digital images of the high-performance thin-layer chromatography profiles were converted to grayscale intensity, and the common patterns of high-performance thin-layer chromatography fingerprints were generated with ChemPattern software. Authentication and quality assessment were analyzed by similarity analysis, hierarchical cluster analysis, principal component analysis, and multivariate analysis of variance. The ultra-high-performance liquid chromatography fingerprints were analyzed by similarity analysis, principal component analysis, and orthogonal partial least square-discriminant analysis. When combined with chemometrics, high-performance thin-layer chromatography and ultra-high-performance liquid chromatography fingerprint provided a simple and effective method to evaluate the comprehensive quality of Fraxini Cortex, and to distinguish its two original medicinal materials (Fraxinus chinensis Roxb. and Fraxinus rhynchophylla Hance.) recorded in the Chinese Pharmacopeia and its three adulterants (Fraxinus mandschurica Rupr., Fraxinus pennsylvanica Marsh., and Juglans mandshurica Maxim.). A similar workflow may be applied to establish a differentiation method for other medicinal and economic plants.     

Application of genetic algorithm‐support vector regression (GA‐SVR) for quantitative analysis of herbal medicines

In this paper, a genetic algorithm‐support vector regression (GA‐SVR) coupled approach was proposed for investigating the relationship between fingerprints and properties of herbal medicines. GA was used to select variables so as to improve the predictive ability of the models. Two other widely used approaches, Random Forests (RF) and partial least squares regression (PLSR) combined with GA (namely GA‐RF and GA‐PLSR, respectively), were also employed and compared with the GA‐SVR method. The models were evaluated in terms of the correlation coefficient between the measured and predicted values (Rp), root mean square error of prediction, and root mean square error of leave‐one‐out cross‐validation. The performance has been tested on a simulated system, a chromatographic data set, and a near‐infrared spectroscopic data set. The obtained results indicate that the GA‐SVR model provides a more accurate answer, with higher Rp and lower root mean square error. The proposed method is suitable for the quantitative analysis and quality control of herbal medicines. Copyright © 2012 John Wiley & Sons, Ltd.     

用高效液相色谱组方指纹图谱智能预测中药质量的新模式

建立大黄、黄芩、黄连的高效液相色谱(HPLC)组方指纹图谱,确定其融合模型,观察组方融合指纹图谱(CSF)与一清片复方样品指纹图谱的一致性,从而以CSF代替复方整体来智能预测复方制剂质量。用二极管阵列检测器(DAD)同时测定黄芩、大黄、黄连和一清片在268 nm波长下的HPLC指纹图谱,并使用系统指纹定量法进行定性、定量评价。结果 CSF涵盖各单味药主要色谱峰信息,即CSF共有峰(55个)涵盖一清片样品共有峰(50个)的主要指纹图谱信息。15批样品的质量除YQT-S01为5级外,其他质量均为3级及以上。各组合模式CSF质量除CSF-2为6级外,其余均为2级或1级。该文探讨了标准指纹图谱和中药组方融合指纹图谱的相关性,可以与相应的计算机评价软件相结合,通过组方融合指纹图谱所代表复方制剂的整体指纹图谱来实现智能预测中药成方制剂质量的新模式。     

Discrimination among Panax species using spectral fingerprinting

Spectral fingerprints of samples of three Panax species (P. quinquefolius L., P. ginseng, and P. notoginseng) were acquired using UV, near-infrared (NIR), and MS. With principal component analysis, all three methods allowed visual discrimination among the three species. All three methods were able to discriminate between white and red ginseng, and showed distinctive subgroupings of red ginseng related to root quality (age/size). Analysis of variance was used to evaluate the relative variance arising from the species, run, and analytical uncertainty, and was used to identify the most information-rich portions of the spectrum for NIR and UV. Accurate classification of the three species was obtained by using partial least squares-discriminant analysis and a fuzzy rule-building expert system. Relatively poor accuracy was obtained using soft independent modeling of class analogy when a single component was used.     

A preliminary matrix‐assisted laser desorption/ionization time‐of‐flight approach for the characterization of Italian lentil varieties

Matrix‐assisted laser desorption/ionization mass spectrometry (MALDI‐MS) was used in this study to obtain protein fingerprints of seven different lentil varieties, to characterize their differences and similarities. Two different matrices have been tested in order to obtain reproducible and significant mass spectra. Extraction with water containing 0.1% of trifluoroacetic acid has been used as preparative step to obtain hydrophilic protein samples of lentil seeds. The obtained MALDI protein profiles identified clear differences between the seven studied lentil varieties. Moreover, considering the high complexity of the obtained MALDI spectra, multivariate techniques of data analysis were employed to find further classification details. These multivariate analyses confirmed the possibility of a clear classification of the seven lentil varieties, indicating that the proposed procedure can be a valid taxonomic tool, and a method to certify the origin of lentils, useful for high added value lentils (Italian lentils). Copyright © 2010 John Wiley & Sons, Ltd.     

Characterization of Paris polyphylla var. yunnanensis by Infrared and Ultraviolet Spectroscopies with Chemometric Data Fusion

Paris polyphylla var. yunnanensis has been used for its anti-tumor, anthelmintic, and hemostatic properties. In this investigation, Fourier transform infrared and ultraviolet spectroscopy combined with chemometrics were used for qualitative analysis of P. polyphylla var. yunnanensis from different geographical origins in Yunnan Province. A total of 82 samples for each region were divided into 57 in the calibration set and 25 in the validation set by Kennard–Stone algorithm. Support vector machine and partial least square discrimination on the basis of Fourier transform infrared, ultraviolet, and low- and mid-level data fusion were investigated. Different pretreatments were compared for the appropriate model. The results indicated that the combination of Savitzky–Golay (11 points), second derivative, and standard normal variation has the best performance for support vector machine and partial least square discrimination with the lowest root mean square error of estimation and root mean square error of cross validation and the highest cross validation accuracy rate. The accuracies of calibration and validation for mid-level data fusion in the model of support vector machine were 84.21 and 96% for the partial least square discrimination values of 96.49 and 84%, which was better performance than a single technique or low-level data fusion for the classification. Moreover, the chemical information of sample collected from Kunming and Xishuangbanna was distinguishable from the others. These results provide a rapid and robust strategy for quality control of P. polyphylla var. yunnanensis for further analysis.     

Genetic and Phytochemical Analysis of Armillaria mellea by RAPD,ISSR, and HPLC

Abstract

Molecular genetic and phytochemical methods were combined to investigate 17 Chinese strains of Armillaria mellea. Ten random amplified polymorphic DNA (RAPD) primers yielded 106 bands, of which 94 were polymorphic; 80 out of the 84 bands produced by nine inter-simple sequence repeats (ISSR) markers were polymorphic. Contents of water-soluble constituents of mycelia were characterized by high-performance liquid-chromatography–diode array detection (HPLC-DAD) analyses. Cluster analyses of the genetic and phytochemical variation revealed that Armillaria mellea exhibited four chemotypes and four clusters from phytochemical contents. Phylogenetic groupings displayed in tree plots calculated from the RAPD and ISSR data matrix correlated with the geographical origin of the fungi materials. Genetic profiles partially correlated with the chemotypes. Phytochemical contents mainly correlated with the strains. A method based on RAPD and HPLC is described to establish genetic and chemical quality control of Armillaria mellea simultaneously. Ten RAPD primers and a coefficient of >0.95 can be used in authentication of Armillaria mellea. The fingerprints obtained with HPLC consist of 22 common peaks within 45 min. This method could be readily utilized as a quality-control method for pharmaceutical-grade Armillaria mellea.     

Enhanced chromatographic fingerprinting of herb materials by multi-wavelength selection and chemometrics

A strategy for multi-wavelength chromatographic fingerprinting of herbal materials, using high performance liquid chromatography with a UV–Vis diode array detector is presented. Valeriana officinalis was selected to show the proposed methodology since it is a widely used commercially available herbal drug, and because misfit with other valerian species is a current issue. The enhanced fingerprints were constructed by compiling into a single data vector the chromatograms from four wavelengths (226, 254, 280 and 326 nm), at which characteristic chemical constituents of studied herbs presented maximum absorbance. Chromatographic data pretreatment included baseline correction, normalization and correlation optimized warping. A simplex optimization was performed to retrieve the optimal values of the parameters used in the warping. General success rates of a classification above 90% were achieved by soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The sensitivity and specificity of constructed models were above 94%. Tests on laboratory-made mixtures showed that it is possible to detect adulterations or counterfeits with 5% foreign herbal material, even if it is from the Valerianaceae family. The results suggest that the proposed enhanced fingerprinting approach can be used to authenticate herb materials with complex chromatographic profiles.