Fractal fingerprinting of chromatographic profiles based on wavelet analysis and its application to characterize the quality grade of medicinal herbs

Extracting chemical fingerprints is an important step for representing and interpreting chromatographic data. In this paper, the chromatographic profile is decomposed into components at different resolution levels using wavelet analysis, then the fractal dimensions of these components are computed as the chemical fingerprints. The chromatographic fingerprint is characterized by the vector composed of these chemical fingerprints, which can represent the chemical patterns of different categories of complex samples. Computer simulations reveal that the fractal fingerprints are more stable than the original chromatographic profile data with respect to variations of peak retention time. To demonstrate the validity of this method, the evaluation of the quality of the medicinal herb Angelica sinensis (Oliv.) diels is investigated. Principal component analysis of the fractal fingerprints indicates that samples belonging to the same quality grade are clustered together, while those belonging to different quality grades are separated. Using these fractal fingerprints taken from the chromatographic scans as inputs for an artificial neural network (ANN). The quality grades of two sets of the herbs were verified by cross-validation, indicating that 96.7% of the herbs are correctly identified with respect to their quality grades evaluated by experienced experts, and 100.0% of the herbs are correctly identified with respect to their quality grades determined by pharmacodynamical evaluation.     

Geographical Authentication of Gentiana Rigescens by High-Performance Liquid Chromatography and Infrared Spectroscopy

The chemical characteristics of Gentiana rigescens are extremely variable due to their geographical origins which should be determined to evaluate the quality of this species. Different with other herbs with official tissue for classification materials, the geographical characterization of raw herbal materials on the basis of nonmedicinal parts is rarely discussed. Chromatographic active components were used as references to characterize the chemical profiles of samples from various geographical origins. Based on spectra data matrix of different botanical parts, the chemometric methods of partial least square discrimination analysis and support vector machine discrimination analysis were used to develop mathematical models to classify samples from different geographical origins. In terms of six active components, we found that significant differences were present in the tissue of G. rigescens based on geographical origins. In addition, the region with higher content of gentiopicroside was selected to be the optimal cultivated location. Chemometric results indicated that leaves were the optimal material for geographical characterization of G. rigescens with 100% accuracy by support vector machine while the accuracies of roots, stems, and flowers were 90.91, 96.10, and 97.01%, respectively. Partial least square discrimination analysis showed that accuracy values for roots, stems, leaves, and flowers were 35.65, 67.53, 76.62, and 50.75%, respectively, which also indicated that leaves are the optimal material. In conclusion, northwest Yunnan Province with higher content of gentiopicroside was selected to be the optimal cultivation location. Furthermore, leaves should be used for the most accurate geographical authentication.     

Assessment of Polygonum capitatum Buch.‐Ham. ex D.Don by metabolomics based on gas chromatography with mass spectrometry

Polygonum capitatum is widely used in southwest China. It has considerable therapeutic efficacy for urinary tract infections. P. capitatum contains multiple components and quality assessment can be achieved by means of metabolic fingerprinting. In this paper, a new strategy for P. capitatum quality determination was developed. Eleven batches of P. capitatum were collected from five geographical areas in China including a standard batch regulated by Good Agriculture Practice. Gas chromatography with mass spectrometry was used to generate fingerprints from triplicate extractions to each batch (n = 33). Hierarchical clustering analysis was applied to assess similarities among the ten batches to the standard batches. Orthogonal projection to latent structures discriminate analysis, cross‐validated with permutation tests, was performed to investigate discriminating metabolites. Results demonstrated that the overall evaluation hierarchical clustering analysis clustered two batches with distance > 3. Orthogonal projection to latent structures discriminate analysis (R²Y (cum) = 0.997, Q² (cum) = 0.97, CV‐ANOVA = 8.48 × 10^?11) indicated that several sugars contributed to batch classification. This method is a rational approach that can classify against a regulated plant standard and distinguishes samples from different origins or processing time in a holistic manner and metabolites driving any differences can be easily identified.     

Determination of Metabolites from Scutellaria baicalensis by GC/MS and 1H NMR

The quality of medicinal herbs and their products is steadily becoming important in tandem with growing interest in complementary alternative medicine for treatment of diseases. Chemical assays with bioactive secondary compounds are usually used for chemical standardization in herbs for quality control purpose. In this study, an analytical platform comprising of a GC-MS technique with an unsupervised multivariate analysis and a complementary one-dimensional ¹H NMR technique was used to obtain the primary metabolite profiling of Scutellaria baicalensis obtained from different medical halls in Singapore. The key primary metabolites such as sucrose, proline, phenylalanine, fructose, and butanedioic acid for the biosynthesis of bioactive secondary metabolites in this medicinal herb were successfully characterized by the combination of the chromatography and spectroscopy techniques. Their results suggested that these compounds could serve as markers for quality control of the herb. The principal component analyses of the GC-MS data reliably discriminated between the various Scutellaria samples indicating that the developed platform was comprehensive and was applicable to assess the quality of other medicinal herbs.     

Quality assessment of traditional Chinese medicine herb couple by high‐performance liquid chromatography and mass spectrometry combined with chemometrics

This study was designed to develop a simple, specific and reliable method to overall analyze the chemical constituents in clematidis radix et rhizome/notopterygii rhizome et radix herb couple using high‐performance liquid chromatography coupled with tandem mass spectrometry and multiple chemometric analysis. First, the separation and qualitative analysis of herb couple was achieved on an Agilent Zorbax Eclipse Plus C₁₈ column (250 mm × 4.6 mm, 5 μm), and 69 compounds were unambiguously or tentatively identified. Moreover, in quantitative analysis, eight ingredients including six coumarins and two triterpenoid sapogenins were quantified by high‐performance liquid chromatography coupled with tandem mass spectrometry. In terms of good linearity (r² ≥ 0.9995) with a relatively wide concentration range, recovery (85.40–102.50%) and repeatability (0.99–4.45%), the validation results suggested the proposed method was reliable, and successfully used to analyze ten batches of herb couple samples. Then, hierarchical cluster analysis and principal component analysis were used to classify samples and search significant ingredients. The results showed that ten batches of herb couple samples were classified into three groups, and six compounds were found for its better quality control.     

Determination of metabolites in Uncaria sinensis by HPLC and GC-MS after green solvent microwave-assisted extraction

Uncaria sinensis (Oliv.) Havil (Rubiaceae) has been used as an important Traditional Chinese Medicine (TCM) herb for the treatment of fevers and various nervous disorders. The major bioactive secondary metabolites from different classes of chemical compounds, i.e. organic acid, flavonoid and alkaloid, present in this TCM herb, namely catechin, caffeic acid, epicatechin and rhynchophylline, were extracted by microwave-assisted extraction (MAE) method with ultra-pure water as the extraction solvent. The optimal extraction conditions for this green solvent MAE method were found to be 100 °C for 20 min. The recoveries of the compounds were found to be comparable to that of heating under reflux using ultra-pure water for 60 min. The method precision (RSD, n = 6) was found to vary from 0.19% to 5.60% for the proposed method on different days for the secondary metabolites. Simultaneously, the key primary metabolites such as sucrose and phenylalanine for the biosynthesis of bioactive secondary metabolites were successfully characterized by GC-MS. Furthermore, an approach using the combination of primary and secondary metabolite profiling based on their chemical fingerprints with Principal Component Analysis (PCA) was successfully developed to evaluate the quality of U. sinensis obtained from different sources. This approach was shown to be feasible in discriminating U. sinensis from different origins and thus a potential application for the quality control of other medicinal herbs.     

Quality control and original discrimination of Ganoderma lucidum based on high-performance liquid chromatographic fingerprints and combined chemometrics methods

In this paper, the feasibility and advantages of employing high-performance liquid chromatographic (HPLC) fingerprints combined with chemometrics methods for quality control of the cultured fruiting bodies of Ganoderma lucidum were investigated and demonstrated for the first time. In order to compare the HPLC fingerprints chromatograms between G. lucidum from different origins, the similarities of all the 60 samples and relative peak areas of 19 characteristic compounds were firstly calculated respectively. Then different pattern recognition procedures, including hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least squares-discrimination analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were applied to classify the G. lucidum samples according to their cultivated origins. Consistent results were obtained to show that G. lucidum samples could be successfully grouped in accordance with the province of origin. Furthermore, four marker constituents were screened out to be the most discriminant variables, which could be applied to accurate discrimination and quality control of G. lucidum by quantitative analysis. Finally, the chemical properties of those samples were also investigated to find out the differences of quality between them. Ranked in decreasing order, the quality of the G. lucidum can be arranged as Jinzhai/Huangshan, Shandong followed by Zhejiang samples. Our results revealed that the developed method has potential perspective for the original discrimination and quality control of G. lucidum.     

Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques

A rapid and nondestructive near infrared (NIR) method combined with chemometrics was used to discriminate Ganoderma lucidum according to cultivation area. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of G. lucidum samples were also investigated to find out the difference between samples from six varied origins. It could be found that the amount of polysaccharides and triterpenoid saponins in G. lucidum samples was considerably different based on cultivation area. These differences make NIR spectroscopic method viable. Principal component analysis (PCA), discriminant partial least-squares (DPLS) and discriminant analysis (DA) were applied to classify the geographical origins of those samples. The results showed that excellent classification could be obtained after optimizing spectral pre-treatment. For the discriminating of samples from three different provinces, DPLS provided 100% correct classifications. Moreover, for samples from six different locations, the correct classifications of the calibration as well as the validation data set were 96.6% using the DA method after the SNV first derivative spectral pre-treatment. Overall, NIR diffuse reflectance spectroscopy using pattern recognition was shown to have significant potential as a rapid and accurate method for the identification of herbal medicines.     

Geographical classification of Epimedium based on HPLC fingerprint analysis combined with multi‐ingredients quantitative analysis

A reliable and comprehensive method for identifying the origin and assessing the quality of Epimedium has been developed. The method is based on analysis of HPLC fingerprints, combined with similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and multi‐ingredient quantitative analysis. Nineteen batches of Epimedium , collected from different areas in the western regions of China, were used to establish the fingerprints and 18 peaks were selected for the analysis. Similarity analysis, HCA and PCA all classified the 19 areas into three groups. Simultaneous quantification of the five major bioactive ingredients in the Epimedium samples was also carried out to confirm the consistency of the quality tests. These methods were successfully used to identify the geographical origin of the Epimedium samples and to evaluate their quality.     

The geographical origin and chemical composition in <Emphasis Type="Italic">phellinus</Emphasis> mushrooms measured by instrumental neutron activation analysis

In order to expand the utilization of phellinus mushrooms as a dietary supplement, we attempted to evaluate the chemical composition by measuring its inorganic elemental content with the aid of instrumental neutron activation analysis (INAA). Twenty seven phellinus mushrooms samples were collected from Korea, Cambodia, and Vietnam. A total of 28 elements were analyzed in the phellinus mushroom samples using the INAA. The concentrations of Ca, K, and Mg are much higher than those of other elements in phellinus mushroom samples. The sum of determined elemental concentration in Cambodia samples was about 2–6 times higher than those in Korea and Vietnam samples, respectively. Based on our measurement data, we attempted to discriminate the geographical origin using principal components analysis (PCA) and linear discriminant analysis (LDA). The geogrpahical origins of all samples were clearly classified with correct classification rate of 100%.     

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.     

Accurate discrimination of Gastrodia elata from different geographical origins using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis

Gastrodia elata from different geographical origins varies in quality and pharmacological activity. This study focused on the classification and identification of Gastrodia elata from six producing areas using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis. Before recognition analysis, a principal component analysis was applied to ascertain the discrimination possibility with high‐performance liquid chromatography fingerprints. And then, boosting partial least‐squares discriminant analysis and conventional partial least‐squares discriminant analysis were applied in this study. Experimental results indicated that the adaptive iteratively reweighted penalized least‐squares algorithm could eliminate the baseline drift of high‐performance liquid chromatography chromatograms effectively. And compared with partial least‐squares discriminant analysis, the total recognition rates using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis for the calibration sets and prediction sets were improved from 94 to 100% and 86 to 97%, respectively. In conclusion, high‐performance liquid chromatography combined with boosting partial least‐squares discriminant analysis, which has such advantages as effective, specific, accurate, non‐polluting, has an edge for discrimination of traditional Chinese medicine from different geographical origins. And the proposed methodology is a useful tool to classify and identify Gastrodia elata from different geographical origins.     

Chemical constituents and quality control of two Dracocephalum species based on high‐performance liquid chromatographic fingerprints coupled with tandem mass spectrometry and chemometrics

Two similar Dracocephalum species, namely, Dracocephalum tanguticum Maxim and Dracocephalum moldavica L, are commonly used as ethnic medicines in China. Here we describe a strategy of combining high‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry, as well as fingerprints and chemometrics for characterization and discrimination of chemical constituents on the two herbs. A total of 49 compounds including 33 flavonoids, 5 phenylethanol glycosides, 1 coumarin glycoside, 8 organic acids, and 2 other types of compounds were unambiguously or tentatively identified from the two Dracocephalum species. Among the compounds identified, 26 were characterized for the first time and 4 compounds, rosmarinic acid ( 7 ), salvianolic acid B ( 10 ), luteoloside ( 22 ), diosmetin‐7‐O‐glucoside ( 28 ), were inferred as common constituents for the two herbs. Flavonoids featured in these two Dracocephalum species while their types presented significant differences. Acacetin ( 45 ) and acacetin glycosides (acatetin‐7‐O‐glucuronide ( 30 ), acacetin‐7‐O‐(6”‐O‐malonyl) glucoside ( 33 ), buddleoside ( 34 ), tilianin ( 35 ), and agastachoside ( 42 )) were detected only in D. moldavica, which can be used to discriminate two herbal medicines. In addition, six characteristic constitutes in D. tanguticum were simultaneously quantified. Moreover, the induced chemometrics methods including similarity analysis and hierarchical clustering analysis were successfully applied for origin discrimination and quality evaluation of D. tanguticum and D. moldavica.     

Comparing chemical fingerprints of herbal medicines using modified window target-testing factor analysis

A chromatographic fingerprint of a herbal medicine is essentially its chromatographic spectrum: a characteristic representation of its chemical components, some of which are pharmacologically active. Since a wide variety of factors, such as the geographical location, the harvest season, and the part used can influence the chemical constituents (and therefore the pharmacological activity) of any particular herbal medicine and its products, these fingerprints provide a way to compare and contrast the compositions of different variants of the same herbal medicine. In particular, it is possible to ascertain whether particular components present in one herbal fingerprint are also present in another fingerprint. In this work we use a novel method—modified window target-testing factor analysis (MWTTFA), based on the use of target factor analysis (TFA), fixed-size moving window evolving factor analysis (FSMWEFA) and a Gaussian shape correction to the chromatographic profiles—to achieve this end. To demostrate the strategy, the fingerprints of samples from garlics produced in different geographical locations were compared, as well as the fingerprints of samples taken from above-ground and below-ground parts of Houttuynia cordata Thunb. The results from these comparisons clearly show that four chemical components present in Hunan common edible garlic are absent in Xingping base garlic, while seven components are present in Xingping base garlic but absent in Hunan common edible garlic. Also, eleven components are present in the sample from the above-ground part of Houttuynia cordata Thunb but not in the sample from the below-ground part, while seven components are present in the sample from the below-ground part of Houttuynia cordata Thunb that are not present in the sample from the above-ground part. These interesting conclusions should be very useful for future pharmacological and clinical research into these herbal medicines, and the novel MWTTFA technique can also be used for quality control purposes.     

Quantitative analysis of multicomponents by a single marker and quality evaluation of Venenum Bufonis from different geographical origins

Bufadienolides are the main bioactive components of Venenum Bufonis (VB) and have been widely used to treat different types of human cancers for decades. The bufadienolide content in VB varies significantly in materials from different geographical origins. In this work, a new strategy for the quality assessment of VB was developed through quantitative analysis of multi‐components by single marker (QAMS). Cinobufagin was selected as the internal reference substance; seven bufadienolides were separated and simultaneously determined based on relative correction factors. The correlation coefficient value (r ≥ 0.9936) between QAMS and the normal external standard method proved the consistency of the two methods. According to the outcomes of 30 batches of VB samples, the contents of the seven bufadienolides were used for further chemometric analysis. All of the samples of VB from various geographical origins were divided into three categories based on hierarchical cluster analysis and radar plot, which indicated the crucial influence of geographical origins on VB. This study showed that QAMS combined with chemometristry could be used to comprehensively evaluate and effectively control the quality of VB from different geographical origins.     

Comprehensive quality evaluation of the lateral root of Aconitum carmichaelii Debx. (Fuzi): Simultaneous determination of nine alkaloids and chemical fingerprinting coupled with chemometric analysis

Amino alcohol alkaloids are the active components in the lateral root of Aconitum carmichaelii Debx. (Fuzi), and they have a variety of pharmacological activities. However, the chemical fingerprints of the ester alkaloids reported to date were mainly obtained from high‐performance liquid chromatography coupled with ultraviolet detection, and it is difficult to obtain information about amino alcohol alkaloids in Fuzi from such chromatograms. In this paper, a comprehensive fingerprinting method was established using high‐performance liquid chromatography coupled with an evaporative light‐scattering detector for the simultaneous quantitative analysis of both the amino alcohol alkaloids and ester alkaloids. A total of 42 samples of Fuzi from four production areas were analyzed by constructing high‐performance liquid chromatography fingerprints. Then, the quantitative results of the chemical fingerprints combined with chemometrics methods were employed to reveal the factors affecting the geo‐authentic Fuzi and to determine characteristic components that can be used to identify these samples. The results indicated distinct differences in the alkaloid contents among samples from the four regions; the geographical origin may be the primary factor affecting the geo‐authentic Fuzi, and 15 major components (including songorine, neoline, and hypaconitine, which were quantitatively determined) were found to be characteristic components for the discrimination of Fuzi samples from various regions. Neoline might be a critical component for identifying geo‐authentic Fuzi. This approach is convenient, reproducible and provides a promising method for the quality evaluation of Fuzi.     

Discriminating five Polygonatum medical materials and monitoring their chemical changes associated with traditional process by FT-IR spectroscopy coupled with multivariate analysis

Several Polygonatum species are important medicinal materials as tonic to cure disorders in China. Because of their different medical effects, it is desired to distinguish them at species level. In addition, to ensure and control their medical quality, it is also important to monitor their chemical changes associated with traditional process. Taking the advantages of Fourier transform infrared spectroscopy (FT-IR) and multivariate analysis, we developed a convenient, fast and reliable approach to discriminate and quality control these materials. Despite similar absorption patterns, each species also presented spectral differences, especially on the FT-IR fingerprint range of 1800-600 cm⁻¹. Second derivative method obviously enlarged those differences and then showed more species-specific features. These spectral differences could be used as powerful discriminating points to distinguish them. PCA results showed that each species separated clearly with their biological replicates grouped together, which indicated that the variance between species is greater than within species, therefore, these species could be distinguishable. Using this approach, the five herbal materials were discriminated successfully in their raw, processed and ethanol extracted formats. On the other hand, visual inspecting infrared spectra of samples from 1 to 9 process steps, absorbance near 1737, 1259, 817 and 780 cm^-1 increased gradually but decreased gradually at 927 cm⁻¹. Besides, spectral contour near 1050 cm⁻¹ changed sharply with process treatment. These spectral changes indicated that hydrolyzing polysaccharides into oligo- and mono-saccharides, especially glucose and fructose, are the main chemical changes associated with traditional process. This is consistent with the traditional experience that the processed materials are dark as night and sweet as malt sugar. Meanwhile, our results also indicated that their chemical constituents changed profoundly after process, which might be the chemical basis for raw and processed materials have different medical effects. Based on absorbance at 817 and 780 cm⁻¹ and the color, taste, smell of processed materials followed by energy efficacy, raw materials had to be processed more than 21 h to ensure their quality. This research shows the potential of FT-IR spectroscopy coupled with multivariate analysis to discriminate different herbs and to monitor chemical changes with process and then control their quality. This could be very helpful to ensure the quality, safety, and efficacy of herbs on clinical practices.     

Qualitative and quantitative evaluation of Oroxylum indicum (L.) Kurz by HPLC and LC‐qTOF‐MS/MS

Oroxylum indicum, as a popular functional Chinese herbal medicine for reducing hyperactivity, relieving sore throat, smoothing the liver and adjusting stomach, mainly contains flavonoids. In this study, we aimed to establish a fast and sensitive method that enables to analyze the chemical components in O. indicum qualitatively and quantitatively. First, a total of 42 components were characterized by LC‐quadrupole time‐of‐flight (qTOF)‐tandem mass spectrometry (MS/MS), including 23 flavonoid glycosides, 13 flavonoids and six other types of compounds. Then, 17 characteristic components of the 19 common peaks in the chromatographic fingerprints of O. indicum were confirmed. Fifty samples were classified into two groups by hierarchical clustering analysis and orthogonal partial least squares‐discriminant analysis, which also identified the 10 main chemical markers responsible for differences between samples. Last, the quantitative analysis of multiple components with a single marker method was established for simultaneous determination of six main active components in O. indicum by LC‐UV with oroxin B was chosen as internal reference substance. Finally, a rapid and efficient method integrating HPLC with LC‐electrospray ionization‐qTOF‐MS/MS analysis was established to comprehensively discriminate and assess the quality of O. indicum samples.     

Rapid Determination and Quality Control of Pharmacological Volatiles of Turmeric (Curcuma longa L.) by Fast Gas Chromatography–Surface Acoustic Wave Sensor

Introduction: A novel analytical method using fast gas chromatography combined with surface acoustic wave sensor (GC-SAW) was developed for rapid determination of the pharmacological volatiles of turmeric (Curcuma longa L.). Methods: The volatile compounds in 20 turmeric samples, collected from different parts and different origins, were assessed by the fast GC-SAW. In addition, gas chromatography–mass spectrometry (GC-MS) was employed to confirm the chemical composition of the main volatiles. The digital fingerprint of turmeric was established and analysed by principal component analysis and cluster analysis. Results: Curcumene (9.1%), β-sesquiphellandrene (5.1%) and ar-turmerone (69.63%) were confirmed as the main pharmacological volatiles of turmeric. The content of ar-turmerone in lateral rhizome turmeric was significantly higher than that of top rhizome and ungrouped turmeric. The contents of curcumene and β-sesquiphellandrene in top rhizome turmeric were higher than those in lateral and ungrouped turmeric. The 20 turmeric samples were divided into four categories, which reflected the quality characteristics of the turmeric from different parts and origins. Conclusion: The GC-SAW method can rapidly and accurately detect pharmacologically volatiles of turmeric, and it can be used in the quality control of turmeric.