Variable selection for discriminating herbal medicines with chromatographic fingerprints

When discriminating herbal medicines with pattern recognition based on chromatographic fingerprints, typically, the majority of variables/data points contain no discrimination information. In this paper, chemometric approaches concerning forward selection and key set factor analysis using principal component analysis (PCA), unweighted and weighted methods based on the inner- and outer-variances, Fisher coefficient from the between- and within-class variations were investigated to extract representative variables. The number of variables retained was determined based on the cumulative variance percent of principal components, the ratio of observations to variables and the factor indicative function (IND). In order to assess the methods for variable selection and criteria levels to determine the number of variables retained, the original and reduced datasets were compared with Procrustes analysis and a weighted measure of similarity. Moreover, the tri-variate plots of the first three PCA scores were used to visually examine the reduced datasets in low dimensional space. Herbal samples were finally discriminated by use of Bayes discrimination analysis with the reduced subsets. The case study for 79 herbal samples showed that, the methods of forward selection associating the variables with the loadings closest to 0 and key set factor analysis were preferable to determine the representative variables. Procrustes analysis and the weighted measure were not indicative to extract representative variables. High matching between the original and reduced datasets did not suggest high prediction accuracy. Visually examining the PC1-PC2-PC3 scores projection plots with the reduced subsets, not all the herb samples could be separated due to the complexity of chromatographic fingerprints.     

色谱指纹图谱在中药质量控制中的应用

介绍了色谱指纹图谱在中药质量控制体系,即中药真实性鉴定(化学指纹图谱)、有效性评价(谱效学、生物指纹图谱、代谢指纹图谱)以及安全性评价方面的应用。目前化学指纹图谱在中药的真实性鉴定方面已比较成熟;谱效学、生物指纹图谱和代谢指纹图谱由于与药效活性相关,在中药的有效性评价中已体现出优势;色谱指纹图谱在中药的安全性评价方面的应用也愈来愈受到重视。     

Spectral correlative chromatography and its application to analysis of chromatographic fingerprints of herbal medicines

A signal-processing method known as spectral correlative chromatography (SCC) for two-dimensional data obtained from hyphenated chromatography is developed and applied to chemical chromatographic fingerprint data sets of herbal medicine under specific experimental conditions. The method can judge the presence or absence of a spectral correlative peak among the spectrochromatograms. A local least squares regression model (LLS) is constructed in a piecewise manner to correct the shifts of retention time of some peaks of interest in the chromatograms of various test samples. The results compare favorably with those obtained by a two-point calibrated algorithm. It is shown that performing SCC and LLS on the piecewise clusters of various chromatographic fingerprints is more helpful in practice in revealing their common nature and for characterizing the chemical constituents. This approach holds great potential for facilitating quality control of herbal medicines.     

Similarity analyses of chromatographic herbal fingerprints: A review

Herbal medicines are becoming again more popular in the developed countries because being “natural” and people thus often assume that they are inherently safe. Herbs have also been used worldwide for many centuries in the traditional medicines. The concern of their safety and efficacy has grown since increasing western interest. Herbal materials and their extracts are very complex, often including hundreds of compounds. A thorough understanding of their chemical composition is essential for conducting a safety risk assessment. However, herbal material can show considerable variability. The chemical constituents and their amounts in a herb can be different, due to growing conditions, such as climate and soil, the drying process, the harvest season, etc. Among the analytical methods, chromatographic fingerprinting has been recommended as a potential and reliable methodology for the identification and quality control of herbal medicines. Identification is needed to avoid fraud and adulteration. Currently, analyzing chromatographic herbal fingerprint data sets has become one of the most applied tools in quality assessment of herbal materials. Mostly, the entire chromatographic profiles are used to identify or to evaluate the quality of the herbs investigated. Occasionally only a limited number of compounds are considered. One approach to the safety risk assessment is to determine whether the herbal material is substantially equivalent to that which is either readily consumed in the diet, has a history of application or has earlier been commercialized i.e. to what is considered as reference material. In order to help determining substantial equivalence using fingerprint approaches, a quantitative measurement of similarity is required. In this paper, different (dis)similarity approaches, such as (dis)similarity metrics or exploratory analysis approaches applied on herbal medicinal fingerprints, are discussed and illustrated with several case studies.     

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.     

Modified secured principal component regression for detection of unexpected chromatographic features in herbal fingerprints

Secured principal component regression is modified for the qualitative analysis of chromatographic fingerprint data sets of herbal samples with residual concentrations. After chromatographic shift-correction and autoscaling are performed on the data, this modified secured principal component regression (msPCR) can detect unexpected chromatographic features in various herbal fingerprints. The successful application of msPCR to two real herbal medicines of Erigeron breviscapus from different geographical origins and Ginkgo biloba from various sources or vendors demonstrates that the proposed method can detect reasonably unexpected features differing from the regulars or not being modeled. From a chemical point of view, the causes have also been explained to corroborate the results. Moreover, it presents a viable approach for the qualitative evaluation of diverse herbal objects with a regular class of chromatographic fingerprints.     

色谱数据可视化及天然植物药指纹特征发现方法

提出一类色谱分析数据可视化方法,并用于发现天然植物药的化学指纹特征。选取中药材川芎作为典型研究对象,采用核主成分分析和空间投影变换法对色谱分析数据进行预处理,提取特征信息,再利用二维灰度映像对变换后的数据进行可视化表达,发现其化学指纹特征,从而直接反映出药材质量类别间的化学模式差异。将该方法用于辨识34个不同产地及等级的川芎样品,结果令人满意,证明其具有视觉模式分辨优点,是表达隐含特征指纹和辨识复杂化学物质体系的有力工具。     

Recent developments in chromatographic fingerprints from herbal products: set-up and data analysis

The use of chromatographic fingerprints from herbal products where the whole chromatographic profile is applied to evaluate the quality of the investigated product. In this paper, recent developments in the set-up and data analysis of chromatographic fingerprints for herbal products are discussed. First different set-ups for fingerprint development are reviewed. Prior to fingerprint development, a suitable sample preparation, e.g. extraction, should be considered. In a second instance, this review focuses on the data analysis with regards to the different applications of fingerprints. Usually, chemometric data pretreatment is necessary. This is discussed first, followed by a short overview of the data handling techniques used in the two main application areas of herbal fingerprints, i.e. quality assurance and classification or calibration. The quality assurance, which involves the identification and quality control of the herbal products, is reviewed, followed by the use of fingerprints in classification or modelling. The different application areas are illustrated and discussed with several case studies.     

Liquid chromatographic separation of alkaloids in herbal medicines: Current status and perspectives

Alkaloids are a widespread group of basic compounds in herbal medicines and have attracted great interest due to various pharmaceutical activities and desirable druggability. Their distinctive structures make chromatographic separation fairly difficult. Peak tailing, poor resolution, and inferior column‐to‐column reproducibility are common obstacles to overcome. In order to provide a valuable reference, the methodologies and/or strategies on liquid chromatographic separation of alkaloids in herbal medicines proposed from 2012 to 2019 are thoroughly summarized.     

Multiple chromatographic fingerprinting and its application to the quality control of herbal medicines

Recently, chromatographic fingerprinting has become one of the most powerful approaches to quality control of herbal medicines. However, the performance of reported chromatographic fingerprinting constructed by single chromatogram sometimes turns out to be inadequate for complex herbal medicines, such as multi-herb botanical drug products. In this study, multiple chromatographic fingerprinting, which consists of more than one chromatographic fingerprint and represents the whole characteristics of chemical constitutions of the complex medicine, is proposed as a potential strategy in this complicated case. As a typical example, a binary chromatographic fingerprinting of “Danshen Dropping Pill” (DSDP), the best-sold traditional Chinese medicine in China, was developed. First, two HPLC fingerprints that, respectively, represent chemical characteristics of depsides and saponins of DSDP were developed, which were used to construct binary chromatographic fingerprints of DSDP. Moreover, the authentication and validation of the binary fingerprints were performed. Then, a data-level information fusion method was employed to capture the chemical information encoded in two chromatographic fingerprints. Based on the fusion results, the lot-to-lot consistency and frauds can be determined either using similarity measure or by chemometrics approach. The application of binary chromatographic fingerprinting to consistency assessment and frauds detection of DSDP clearly demonstrated that the proposed method was a powerful approach to quality control of complex herbal medicines.     

A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships(QSRR) models.First,the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties(SGBP),involving physicochemical,quantum chemical,topological,spatial structural properties,etc.;thereafter,the sequence data are transformed into a uniform matrix by auto cross covariance(ACC).ACC accounts for the ...     

Automated alignment of one-dimensional chromatographic fingerprints

A general framework for the automatic alignment of one-dimensional chromatographic signals is presented in this article. The alignment of signals was achieved by explicitly modeling the warping function. Its shape was estimated using a linear combination of several B-spline functions. The coefficients of the spline functions were found in the course of an optimization procedure to maximize the Pearson's correlation coefficient between a target chromatogram and aligned chromatogram(s). The computational requirements of the method are discussed with respect to the correlation optimized warping method, frequently used for the alignment of chromatographic signals. As illustrated with two sets of one-dimensional chromatographic fingerprints, the automatic alignment approach performs well even when non-linear peak shifts need to be corrected. It can be applied in an on-the-fly manner since the alignment of signals is rapid.     

High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis

A rapid retention time alignment algorithm was developed as a preprocessing utility to be used prior to chemometric analysis of large datasets of diesel fuel profiles obtained using gas chromatography (GC). Retention time variation from chromatogram-to-chromatogram has been a significant impediment against the use of chemometric techniques in the analysis of chromatographic data due to the inability of current chemometric techniques to correctly model information that shifts from variable to variable within a dataset. The alignment algorithm developed is shown to increase the efficacy of pattern recognition methods applied to diesel fuel chromatograms by retaining chemical selectivity while reducing chromatogram-to-chromatogram retention time variations and to do so on a time scale that makes analysis of large sets of chromatographic data practical. Two sets of diesel fuel gas chromatograms were studied using the novel alignment algorithm followed by principal component analysis (PCA). In the first study, retention times for corresponding chromatographic peaks in 60 chromatograms varied by as much as 300 ms between chromatograms before alignment. In the second study of 42 chromatograms, the retention time shifting exhibited was on the order of 10 s between corresponding chromatographic peaks, and required a coarse retention time correction prior to alignment with the algorithm. In both cases, an increase in retention time precision afforded by the algorithm was clearly visible in plots of overlaid chromatograms before and then after applying the retention time alignment algorithm. Using the alignment algorithm, the standard deviation for corresponding peak retention times following alignment was 17 ms throughout a given chromatogram, corresponding to a relative standard deviation of 0.003% at an average retention time of 8 min. This level of retention time precision is a 5-fold improvement over the retention time precision initially provided by a state-of-the-art GC instrument equipped with electronic pressure control and was critical to the performance of the chemometric analysis. This increase in retention time precision does not come at the expense of chemical selectivity, since the PCA results suggest that essentially all of the chemical selectivity is preserved. Cluster resolution between dissimilar groups of diesel fuel chromatograms in a two-dimensional scores space generated with PCA is shown to substantially increase after alignment. The alignment method is robust against missing or extra peaks relative to a target chromatogram used in the alignment, and operates at high speed, requiring roughly 1 s of computation time per GC chromatogram.     

Prediction of gas chromatographic retention time via an additive thermodynamic model

A straightforward group contribution model based on thermodynamic parameters was developed to predict retention times for a series of alcohols and ketones on three different stationary phases. Thermodynamic parameters determined from gas chromatographic retention data for structurally similar compounds via a three-parameter model were used to predict the retention times of test molecules consisting of ketones and alcohols. The model worked well for the compounds tested with a root mean square error of prediction of 5.50 s across all compounds, phases, and temperature ranges studied. Considering just the alcohols, the error of prediction was 2.79 s across all phases and temperatures.     

Overcoming retention time shifts as a source of error in HPLC analysis

When used for physiologic fluid analysis, shifts in retention times, even at constant flow rates, can cause integrators to function inadequately as tools for peak identification and thus result in concentration errors. A system capable of data collection, identification, quantification, and long-term storage that primarily uses retention times only for identification of elution sequence is described. Operator identification, aided by manipulation of x and y coordinates of the chromatograph, enhances positive identification of compounds for the experienced chromatographer.     

Chip-based high-throughput screening of herbal medicines

At present, high-throughput screening (HTS) programs in drug discovery rely mainly on compound libraries from combinational chemistry. Similarly, natural flora has been used as a prominent origin for new and potent herbal drugs. Herbal medicines have been used worldwide for thousands of years to cure many diseases. As such, herbal secondary metabolites show a remarkable structural diversity that supplements chemically synthesized compound analogs in drug discovery screening. Unfortunately, there is often a considerable deterioration in the quality of herbal drugs in such screening programs as there are time-consuming manual processes involved in the isolation of active ingredients from the highly complex mixtures of herbal plant products. The quality and quantity of herbal samples are critical for the success of HTS programs. In the recent past, there have been substantial improvements in HTS due to the miniaturization and integration of microchip (e.g., Herbochip(?), DNA chip, protein chip, cell chip, etc.)-based technologies so as to design herbal drugs that compete with synthetic drug analogs. Here we will review various technologies used for HTS of herbal medicines. Finally, we will summarize our efforts to develop a novel chip-based HTS assay to explore the antioxidant and radioprotective properties of herbal plants.     

知母药材高效液相指纹图谱研究

用高效液相色谱法建立了知母药材的指纹图谱分析方法。采用ZorbaxEclipseXDB C18色谱柱(5μm,4.6×250mm,Agilent),流动相为乙腈和25mmol/LKH2PO4缓冲液,梯度洗脱;流速:1.0mL/min;柱温为25℃;检测波长257nm。确定了8批不同产地知母药材甲醇提取部分6个共有峰,各产地药材指纹图谱与对照指纹图谱相似度良好(>0.9),可以用来作定性研究。方法准确可靠,为提高知母药材质量控制标准提供参考。     

Improved chromatographic fingerprints for facile differentiation of two Ganoderma spp

This paper addresses a comprehensive and comparative study of six phytochemical extraction methods for triterpenes from the fruiting body of Ganoderma spp. Quantitative analysis of extracts was performed by HPLC with photodiode array detection. In general, pressurized liquid extraction and microwave-assisted extraction under optimized conditions produce better yields, and the former also significantly reduces the total time of extraction and manipulation of a sample, as well as the amount of solvent used in comparison with conventional soxhlet, reflux, ultrasonic, and methanol-CO(2) supercritical fluid extractions. Based on the improved extraction protocol, the fingerprinting profiles for two species of Lingzhi were established using the consistent chromatographic features of 12 authentic samples. Eleven common peaks of ganoderic/ganoderenic acids were identified using LC-ESI-MS-MS. These specific triterpene groups were adopted as chemical markers for Lingzhi. Using chemometric analysis, the developed fingerprinting was successfully applied to differentiate between the two species under the Ganoderma genus and is applicable as a method for quality evaluation of this valuable medicinal fungus and its related proprietary products.