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.     

High‐performance liquid chromatography based chemical fingerprint analysis and chemometric approaches for the identification and distinction of three endangered Panax plants in Southeast Asia

Among Panax genus, only three endangered species Panax notoginseng, P. vietnamensis, and P. stipuleanatus that have a similar morphology are mainly distributed in Southeast Asia. These three plants are usually misidentified or adulterated. To identify them well, their chemical chromatographic fingerprints were established by an effective high‐performance liquid chromatography method. By comparing the chromatograms, the three Panax species could be distinguished easily using the 22 characteristic peaks. Besides, the data of the chromatographic fingerprints aided by chemometric approaches were applied for the identification and investigation the relationship of different samples and species. Using similarity analysis, the chemical components revealed higher similarity between P. vietnamensis and P. stipuleanatus. The results of hierarchical clustering analysis indicated that samples belonging to the same species could be clustered together. The result of principal component analysis was similar with hierarchical clustering analysis and the three principal components accounted for >80.5% of total variability.     

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.”     

Quality evaluation of fingerprints of herbal medicine with chromatographic data

In this study, local least squares (LLS) and principal component analysis (PCA) were applied to deal with the disturbances in a data set of chromatographic fingerprints after necessary data transformations. It has been demonstrated that PCA with standard normal variate (SNV) transformation of data led to meaningful classification of 33 different Erigeron breviscapus herbal samples. The result was also corroborated by variance squares discriminant method. The quality of herbal objects was further evaluated, and the causes of this fact have been explained from a chemical point of view. At the same time, it implied an idea for qualitative evaluation of the herbal objects with a common class pattern of chromatographic fingerprints.     

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.     

A new algorithm of piecewise automated beam search for peak alignment of chromatographic fingerprints

The alignment of chromatographic signals is an important preprocessing step before further multivariate analysis. This paper presents a method, automated peak alignment by beam search (Auto-PABS), to solve the problem of peak shift in chemical chromatographic fingerprints by piecewise shifting and linearly interpolating. It is characterized by searching an adaptive range for the values of shifting and linearly interpolating of each segment. This search range is estimated by the calculation of fast Fourier transform cross correlation between the sample segment and its corresponding reference segment. Thus, arbitrary peak alignment is avoided when the real peak shifts are unknown in a large data set. Since the maximum of search range is close to the real shift, more accurate beam search is adopted to accomplish the optimization process. Simulated data and herbal medicine fingerprints of HPLC and GC are selected for evaluation. The output matrix of aligned chromatographic profiles is used directly for principal components analysis, yielding satisfactory results on real samples.     

基于挥发性组分的气相色谱指纹图谱评价沉香化气片质量

建立了沉香化气片的气相色谱指纹图谱,并结合化学模式识别评价20批沉香化气片的质量。乙醇超声提取20批沉香化气片的挥发性成分,以正十八烷为内标,分析了3个主要组分的含量,且以内标计算其他各组分的相对峰面积,建立了沉香化气片的气相色谱指纹图谱,确定了11个共有峰,得到了各批次样品的相似度,并通过气相色谱-质谱法和对照品比对对10个共有峰进行了指认。将获得的峰面积指纹图谱采用系统聚类分析和主成分分析进行化学模式识别研究,实现了不同批次沉香化气片的区分,发现了造成不同批次样品差异的主要标记物。该方法有效且综合性强,为科学评价与有效控制沉香化气片的质量提供了可靠的参考。     

Chemical Fingerprint Analysis for Quality Control of Herba Ephedrae Based on HPLC-DAD Combined with Chemometrics Methods

A method based on high performance liquid chromatography with photodiode array detector (HPLC-DAD) was developed for chemical fingerprinting analysis of Herba Ephedrae. The index of chromatographic fingerprint's information content was utilized to optimize the fingerprint detection conditions, which reduced the time of analysis and increased the veracity of analysis greatly. Then, the similarity analysis of fingerprints was used in quality consistency evaluation of Herba Ephedrae samples. Moreover, hierarchical clustering analysis (HCA) was applied to classify the samples according to their sources and varieties. In addition, the overlapped chromatographic peaks were resolved with the help of heuristic evolving latent projection (HELP) method in order to gain the better quantitative evaluation. The results indicated that the samples could be successfully grouped in accordance with their varieties and sources. Furthermore, five marker constituents were firstly screened out to be the main chemical markers, which importantly contribute to the classification of Herba Ephedrae samples. This investigation shows that the developed methodology can be generalized to the research of quality control of herbal medicines.     

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.     

Classification of Rhizoma et Radix Notoperygii by HPLC Fingerprints with the Aid of Chemometrics

Chromatographic profiles of Rhizoma et Radix Notoperygii (RRN, “Qianghuo” in Chinese), a complex traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography with diode array detection (HPLC-DAD) at 330 nm. These data profiles were used as fingerprints to investigate quality control classification modeling of the RRN samples. In contrast to the classical methods for discrimination of TCMs, that is, just using common HPLC peaks, all chromatographic profile data were pre-processed by the correlation optimized warping method and polynomial functions; then, these data were submitted as fingerprints (variables) for classification on the basis of sample origin. Chemometrics methods used for calibration modeling and subsequent sample classification-least square support vector machine (LS-SVM), artificial neural network (ANN), and partial least square discriminant analysis (PLS-DA); all produced satisfactory calibrations as well as classification results.     

Correction of retention time shifts for chromatographic fingerprints of herbal medicines

In this study, the combination of chemometric resolution and cubic spline data interpolation was investigated as a method to correct the retention time shifts for chromatographic fingerprints of herbal medicines obtained by high-performance liquid chromatography-diode array detection (HPLC-DAD). With the help of the resolution approaches in chemometrics, it was easy to identify the purity of chromatographic peak clusters and then resolve the two-dimensional response matrix into chromatograms and spectra of pure chemical components so as to select multiple mark compounds involved in chromatographic fingerprints. With these mark components determined, the retention time shifts of chromatographic fingerprints might be then corrected effectively. After this correction, the cubic spline interpolation technique was then used to reconstruct new chromatographic fingerprints. The results in this work showed that, the purity identification of the chromatographic peak clusters together with the resolution of overlapping peaks into pure chromatograms and spectra by means of chemometric approaches could provide the sufficient chromatographic and spectral information for selecting multiple mark compounds to correct the retention time shifts. The cubic spline data interpolation technique was user-friendly to the reconstruction of new chromatographic fingerprints with correction. The successful application to the simulated and real chromatographic fingerprints of two Cortex cinnamomi, fifty Rhizoma chuanxiong, ten Radix angelicae and seventeen Herba menthae samples from different sources demonstrated the reliability and applicability of the approach investigated in this work. Pattern recognition based on principal component analysis for identifying inhomogenity in chromatographic fingerprints from real herbal medicines could further interpret it.     

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.     

发酵虫草菌粉及产品指纹图谱建立及多指标成分分析

《中国药典》收载的发酵虫草菌粉产品的质量标准中,规定以鸟苷、腺苷、尿苷的含量作为评价相关产品质量的标准。但除此之外,还有许多其他的核苷类成分对发酵虫草菌粉质量控制的影响尚未被探讨。为探究发酵虫草菌粉及产品质控指标选择的合理性,采用超高效液相色谱-紫外检测法对19批发酵虫草菌粉及产品中9种核苷成分(尿嘧啶、胞苷、鸟嘌呤、尿苷、腺嘌呤、肌苷、鸟苷、胸苷、腺苷)进行了定量分析,建立了发酵虫草菌粉样品的指纹图谱,并结合统计学提供了一种分析指标性成分的方法。通过优化样品的提取方法,选择超声提取法制备19批发酵虫草菌粉及产品的供试液;采用Agilent Eclipse Plus C18色谱柱(150 mm×4.6 mm, 3.5 μm)进行色谱分析,以甲醇-水为流动相梯度洗脱,对方法的校正曲线、准确度、精密度、重复性和回收率进行了验证。结合对照品指认了指纹图谱中的9个核苷峰,并采用外标一点法测得了各核苷成分的含量。使用化学模式识别对指纹图谱中的共有峰进行分析,聚类分析和主成分分析得到了同样的分类结果:19批样品共分为5类,其中同一发酵虫草菌粉因工艺差异可分为2类,而心肝宝胶囊、百令胶囊、宁心宝胶囊则各单独分为1类。同时,使用主成分分析获得了各样品中的指标性成分,分别为尿苷、鸟苷、腺苷、腺嘌呤、尿嘧啶,并使用聚类分析再次进行确证,验证了指标性成分的合理性。     

Chromatographic fingerprinting analysis of Danshen root (Salvia miltiorrhiza Radix et Rhizoma) and its preparations using high performance liquid chromatography with diode array detection and electrospray mass spectrometry (HPLC-DAD-ESI/MS)

Danshen root (Salvia miltiorrhiza Radix et Rhizoma) is a representative Chinese herb containing multiple components contributing to its polyvalent bioactivities. Advanced analysis approaches are needed to obtain a comprehensive picture of the targeting constituents in complete matrix. In this study, a chromatographic fingerprinting method to monitor simultaneously the hydrophilic and lipophilic constituents was developed for the quality evaluation of Danshen root and its preparations. Ten hydrophilic and nine lipophilic components were identified using HPLC-diode array detection-electrospray-MS (HPLC-DAD-ESI/MS) by comparing with the available references and reported data. Using the established method, 13 Danshen root samples collected from different sources, and 21 batches of Danshen preparations including tablets, injections, capsules, and dropping pills produced by different manufacturers were analyzed and their chromatographic fingerprints (CFP) were constructed. The results showed that the products of Danshen roots such as the tablets and capsules contained both the hydrophilic and lipophilic components, while the injections and dropping pills contained mainly the hydrophilic components. Principal components analysis (PCA) was applied for the statistical analysis of the fingerprinting data of crude herb and its preparations. The established CFPs demonstrate the representative chemical profiling of the existing components and can be applied to the authentication and quality assessment of Danshen roots and other Danshen containing formulated preparations.     

Different fingerprinting strategies to differentiate Porana sinensis and plants of Erycibe by high‐performance liquid chromatography with diode array detection,ultra high performance liquid chromatography with tandem quadrupole mass spectrometry,and chemometrics

Plants of Erycibe are widely used in traditional Chinese medicine for the treatment of joint pain and rheumatoid arthritis. With the reduction of Erycibe resources in the wild, Porana sinensis has been widely used as a substitute. However, it is important to understand the chemical distinctions between the two kinds of plants and identify their individual chemical markers. In this study, multiwavelength chromatographic fingerprint and precursor ion fingerprint techniques were used in conjunction with chemometric tools to fingerprint and thus differentiate between plant samples. The similar results obtained from different fingerprints prove the reliability of the two fingerprints. Results obtained from principal component analysis and orthogonal projection to latent structures discriminant analysis identified similarities between the chemical components of P. sinensis and plants of Erycibe. However, concentrations of 4‐caffeoylquinic acid, 3,5‐dicaffeoylquinic acid, 3,4‐dicaffeoylquinic acid, and 4,5‐dicaffeoylquinic acid were higher in P. sinensis than in plants of Erycibe, suggesting that P. sinensis may be more effective in medical treatments of some diseases than Erycibe.     

Data Preprocessing for Chromatographic Fingerprint of Herbal Medicine with Chemometric Approaches

Abstract

Recently, the fingerprinting approach using chromatography has become one of the most potent tools for quality assessment of herbal medicine. Due to the complexity of the chromatographic fingerprint and the irreproducibility of chromatographic instruments and experimental conditions, several chemometric approaches such as variance analysis, peak alignment, correlation analysis, and pattern recognition were employed to deal with the chromatographic fingerprint in this work. To facilitate the data preprocessing, a software named Computer Aided Similarity Evaluation (CASE) was also developed. All programs of chemometric algorithms for CASE were coded in MATLAB5.3 based on Windows. Data loading, removing, cutting, smoothing, compressing, background and retention time shift correction, normalization, peak identification and matching, variation determination of common peaks/regions, similarity comparison, sample classification, and other data processes associated with the chromatographic fingerprint were investigated in this software. The case study of high pressure liquid chromatographic HPLC fingerprints of 50 Rhizoma chuanxiong samples from different sources demonstrated that the chemometric approaches investigated in this work were reliable and user friendly for data preprocessing of chromatographic fingerprints of herbal medicines for quality assessment.     

Evaluation of the Quality of Angelica sinensis by High-Performance Liquid Chromatography

Quality control and consistent quality are prerequisites to guarantee the safety and efficacy of pharmaceutical preparations and dietary supplements. In this paper, the intra- and inter-annual consistency and stability of Angelica sinensis (Oliv.) Diels, cultivated according to Good Agricultural Practices (GAP) in the main production areas of China, were evaluated by high-performance liquid chromatographic fingerprints composed of twenty-four common components. Subsequently, thirteen compounds were identified in the chromatograms using diode array detection and mass spectrometry. Z-Ligustilide, senkyunolide H, senkyunolide I, and ferulic acid were determined to evaluate the distribution of chemical components. In addition, robust principal component analysis was applied to further evaluate the samples, and four discriminatory “markers” among the common twenty-four were pinpointed by the loading plots that were used to distinguish quality differences of Angelica sinensis from different harvest years.     

Quality consistency evaluation of commercial transfer factor injections by chromatographic fingerprint combined with multivariate statistical analysis

The current quality control methods relying mainly on chromogenic reaction can hardly ensure the quality and safety of the biochemical drug with complex chemical composition. Therefore, a chromatographic fingerprint method was developed for the quality evaluation of a multicomponent biochemical drug, transfer factor injection. High‐performance liquid chromatography fingerprint was measured by using a C₁₈ column (250 × 4.6 mm, 5 µm) with a mobile phase composed of 0.1% trifluoroacetic acid–water and 0.085% trifluoroacetic acid–acetonitrile under gradient elution. The developed method was validated and was subsequently applied to 57 batches of commercial products which were sampled by National Drug Assessment Program. High‐resolution mass spectrometry analysis was performed on characteristic peaks of fingerprints, and a series of amino acids, nucleosides, and deoxynucleosides were identified. In the fingerprint assessments, principal component analysis and Hotelling T² analysis yielded the best results. The results generally indicated that there was a significant difference among products of batch‐to‐batch or from different manufacturers. Abnormal samples and its discriminatory components were also explored. In summary, the established fingerprinting method with multivariate statistical analysis could offer an efficient, reliable, and practical approach for quality consistency evaluation of transfer factor injection, providing a reference for the quality control of other multicomponent biochemical drugs.     

Liquid chromatography/mass spectrometry-based metabolic profiling to elucidate chemical differences of tobacco leaves between Zimbabwe and China

An approach was developed for extracting and analyzing the chemical components of tobacco leaves based on solvent extraction and rapid & resolution liquid chromatography/quadrupole time-of-flight mass spectrometry analysis. Two solvents with different polarities were used to extract hydrophilic components and hydrophobic components, respectively, the combined analytical data can provide a "global" view of metabolites. Based on the evaluation of parallel samples, it was found that this approach provided good repeatability, accurate and reliable profiling data, and is suitable for the metabolomics study of tobacco leaves. In order to find the chemical component differences of tobacco leaves, 56 samples from Zimbabwe and China were analyzed using the developed method. The metabolite data were processed by multivariate statistic technique; an obvious group classification between Zimbabwe and China was observed, 14 significantly changed compounds were found, and 9 of them were identified.     

GC–MS combined with chemometric techniques for the quality control and original discrimination of Curcumae longae rhizome: Analysis of essential oils

Curcumae longae rhizome is a widely used traditional herb in many countries. Various geographical origins of this herb might lead to diversity or instability of the herbal quality. The objective of this work was to establish the chemical fingerprints for quality control and find the chemical markers for discriminating these herbs from different origins. First, chemical fingerprints of essential oil of 24 C. longae rhizome from four different geographical origins in China were determined by GC–MS. Then, pattern recognition techniques were introduced to analyze these abundant chemical data in depth; hierarchical cluster analysis was used to sort samples into groups by measuring their similarities, and principal component analysis and partial least‐squares discriminate analysis were applied to find the main chemical markers for discriminating these samples. Curcumae longae rhizome from Guangxi province had the highest essential oil yield (4.32 ± 1.45%). A total of 46 volatile compounds were identified in total. Consistent results were obtained to show that C. longae rhizome samples could be successfully grouped according to their origins, and turmerone, ar‐turmerone, and zingiberene were the characteristic components for discriminating these samples of various geographical origins and for quality control. This finding revealed that fingerprinting analysis based on GC–MS coupled with chemometric techniques could provide a reliable platform to discriminate herbs from different origins, which is a benefit for quality control.