Quantitative and fingerprinting analysis of Atractylodes rhizome based on gas chromatography with flame ionization detection combined with chemometrics

This study assessed the feasibility of gas chromatography with flame ionization detection fingerprinting combined with chemometrics for quality analysis of Atractylodes rhizome. We extracted essential oils from 20 Atractylodes lancea and Atractylodes koreana samples by hydrodistillation. The variation in extraction yields (1.33–4.06%) suggested that contents of the essential oils differed between species. The volatile components (atractylon, atractydin, and atractylenolide I, II, and III) were quantified by gas chromatography with flame ionization detection and confirmed by gas chromatography with mass spectrometry, and the results demonstrated that the number and content of volatile components differed between A. lancea and A. koreana. We then calculated the relative peak areas of common components and similarities of samples by comparing the chromatograms of A. lancea and A. koreana extracts. Also, we employed several chemometric techniques, including similarity analysis, hierarchical clustering analysis, principal component analysis, and partial least‐squares discriminate analysis, to analyze the samples. Results were consistent across analytical methods and showed that samples could be separated according to species. Five volatile components in the essential oils were quantified to further validate the results of the multivariate statistical analysis. The method is simple, stable, accurate, and reproducible. Our results provide a foundation for quality control analysis of A. lancea and A. koreana.     

Chemical fingerprint and quantitative analysis for the quality evaluation of Vitex negundo seeds by reversed‐phase high‐performance liquid chromatography coupled with hierarchical clustering analysis

A simple and efficient method was developed for the chemical fingerprint analysis and simultaneous determination of four phenylnaphthalene‐type lignans in Vitex negundo seeds using high‐performance liquid chromatography with diode array detection. For fingerprint analysis, 13 V. negundo seed samples were collected from different regions in China, and the fingerprint chromatograms were matched by the computer‐aided Similarity Evaluation System for Chromatographic Fingerprint of TCM (Version 2004A). A total of 21 common peaks found in all the chromatograms were used for evaluating the similarity between these samples. Additionally, simultaneous quantification of four major bioactive ingredients was conducted to assess the quality of V. negundo seeds. Our results indicated that the contents of four lignans in V. negundo seeds varied remarkably in herbal samples collected from different regions. Moreover, the hierarchical clustering analysis grouped these 13 samples into three categories, which was consistent with the chemotypes of those chromatograms. The method developed in this study provides a substantial foundation for the establishment of reasonable quality control standards for V. negundo seeds.     

Nonlinear mapping of massive data sets by fuzzy clustering and neural networks

Producing good low‐dimensional representations of high‐dimensional data is a common and important task in many data mining applications. Two methods that have been particularly useful in this regard are multidimensional scaling and nonlinear mapping. These methods attempt to visualize a set of objects described by means of a dissimilarity or distance matrix on a low‐dimensional display plane in a way that preserves the proximities of the objects to whatever extent is possible. Unfortunately, most known algorithms are of quadratic order, and their use has been limited to relatively small data sets. We recently demonstrated that nonlinear maps derived from a small random sample of a large data set exhibit the same structure and characteristics as that of the entire collection, and that this structure can be easily extracted by a neural network, making possible the scaling of data set orders of magnitude larger than those accessible with conventional methodologies. Here, we present a variant of this algorithm based on local learning. The method employs a fuzzy clustering methodology to partition the data space into a set of Voronoi polyhedra, and uses a separate neural network to perform the nonlinear mapping within each cell. We find that this local approach offers a number of advantages, and produces maps that are virtually indistinguishable from those derived with conventional algorithms. These advantages are discussed using examples from the fields of combinatorial chemistry and optical character recognition. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 373–386, 2001     

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.     

Monitoring polymorphic transformations by using in situ Raman hyperspectral imaging and image multiset analysis

Polymorphism is often encountered in many crystalline compounds. To control the quality of the products, it is relevant knowing the potential presence of polymorph transformations induced by different agents, such as light exposure or temperature changes. Raman images offer a great potential to identify polymorphs involved in a process and to accurately describe this kind of solid-state transformation in the surface scanned.     

Method development for fingerprinting of biodiesel blends by solid-phase extraction and gas chromatography-mass spectrometry

A method based on the combination of solid-phase extraction (SPE) with gas chromatography-mass spectrometry (GC/MS) for detailed chemical fingerprinting of biodiesel/petrodiesel blends was developed in the present study. Forensic identification, commonly referred to as chemical fingerprinting, is based on the relative distributions of individual aliphatic hydrocarbons, aromatic hydrocarbons, fatty acid alkyl esters, and free sterols. Fractionation of fuel samples is optimized for the separation of fatty acid esters and free sterols from petroleum hydrocarbons into four fractions: aliphatic, aromatic, fatty acid ester, and polar components. The final recoveries of aliphatic and aromatic hydrocarbons were determined to be in the range of 65-103%, 73-105% for FAMEs, and 78-103% for free sterols in the polar fraction. Excellent separation with negligible crossover of components with different polarities between fractions was observed. Quantitative analysis of blend levels and individual chemical distribution were achieved. The method has great potential for the identification of biodiesel in diesel fuel blends and could form the basis of a method for characterization of biodiesel-contaminated environmental samples.     

Profiling propolis flavonoids by means of micellar electrokinetic capillary chromatography, capillary gas chromatography and bactericidal action

Summary There is no proportional correlation between increasing organic solvent polarity from hexane to methanol and the extractability of propolis solids by the solvents or the bactericidal action of the propolis extracts obtained. Hexane (17.7%) and chloroform (62.1%) were the poorest and the best extractants, respectively, for propolis solids. The antibiotic activity of the extracts againstStaphylococcus strains decreased in the order hexane≥ethanol>methanol. Different capillary GC profiles were obtained for persilyl derivatives of propolis extracted with hexane and methanol and for propolis collected in different Brazilian provinces, suggesting the influence of flora variability on propolis composition. Wavelength-selected DAD detection revealed MEKC to be an innovative and sensitive method for monitoring the occurrence in propolis of the flavonoids and phenolcarboxylic acids thought to be responsible for the antimicrobial activity of propolis.     

Monitoring of water quality in South Paris district by clustering and SIMCA classification

With the aim of obtaining a monitoring tool to assess the quality of water, a multivariate statistical procedure based on cluster analysis (CA) coupled with soft independent modelling class analogy (SIMCA) algorithm, providing an effective classification method, is proposed. The experimental data set, carried out throughout the year 2004, was composed of analytical parameters from 68 water sources in a vast southwest area of Paris. Nine variables carrying the most useful information were selected and investigated (nitrate, sulphate, chloride, turbidity, conductivity, hardness, alkalinity, coliforms and Escherichia coli). Principal component analysis provided considerable data reduction, gathering in the first two principal components the majority of information representing about 92.2% of the total variance. CA grouped samples belonging to different sites, distinctly correlating them with chemical variables, and a classification model was built by SIMCA. This model was optimised and validated and then applied to a new data matrix, consisting of the parameters measured during the year 2005 from the same objects, providing a fast and accurate classification of all the samples. The most of the examined sources appeared unchanged during the 2-year period, but five sources resulted distributed in different classes, due to statistical significant changes of some characteristic analytical parameters.     

Principal component analysis versus fuzzy principal component analysis A case study: the quality of danube water (1985-1996)

Principal component analysis (PCA) is a favorite tool in environmetrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approach to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of the outliers. In this paper we discuss and apply a robust fuzzy PCA algorithm (FPCA). The efficiency of the new algorithm is illustrated on a data set concerning the water quality of the Danube River for a period of 11 consecutive years. Considering, for example, a two component model, FPCA accounts for 91.7% of the total variance and PCA accounts only for 39.8%. Much more, PCA showed only a partial separation of the variables and no separation of scores (samples) onto the plane described by the first two principal components, whereas a much sharper differentiation of the variables and scores is observed when FPCA is applied.     

Combining multiset resolution and segmentation for hyperspectral image analysis of biological tissues

Hyperspectral images can provide useful biochemical information about tissue samples. Often, Fourier transform infrared (FTIR) images have been used to distinguish different tissue elements and changes caused by pathological causes. The spectral variation between tissue types and pathological states is very small and multivariate analysis methods are required to describe adequately these subtle changes.     

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、...     

UV–化学模式识别不同产地的丹参

建立由UV–化学模式识别法评价丹参质量的方法。分别用正己烷、乙酸乙酯、水、乙醇提取不同产地的丹参,并测绘其紫外光谱。取紫外光谱各波长的吸光度为特征数据,进行主成分分析、聚类分析,对不同产地丹参质量的差异进行了评价。不同溶剂提取液的光谱聚类结果有所差异,可将不同产地丹参聚为3类。UV–化学模式识别技术可以从整体上反应丹参所含成分的差异,可为丹参质量控制与评价提供依据。     

Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo

Multivariate analysis of thin‐layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the ‘PRISMA’ optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet‐converted TLC image and 2,2‐diphynyl‐picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x‐ and y‐variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC‐MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed. Copyright © 2015 John Wiley & Sons, Ltd.     

Blind image analysis for the compositional and structural characterization of plant cell walls

A new image analysis strategy is introduced to determine the composition and the structural characteristics of plant cell walls by combining Raman microspectroscopy and unsupervised data mining methods. The proposed method consists of three main steps: spectral preprocessing, spatial clustering of the image and finally estimation of spectral profiles of pure components and their weights. Point spectra of Raman maps of cell walls were preprocessed to remove noise and fluorescence contributions and compressed with PCA. Processed spectra were then subjected to k-means clustering to identify spatial segregations in the images. Cell wall images were reconstructed with cluster identities and each cluster was represented by the average spectrum of all the pixels in the cluster. Pure components spectra were estimated by spectral entropy minimization criteria with simulated annealing optimization. Two pure spectral estimates that represent lignin and carbohydrates were recovered and their spatial distributions were calculated. Our approach partitioned the cell walls into many sublayers, based on their composition, thus enabling composition analysis at subcellular levels. It also overcame the well known problem that native lignin spectra in lignocellulosics have high spectral overlap with contributions from cellulose and hemicelluloses, thus opening up new avenues for microanalyses of monolignol composition of native lignin and carbohydrates without chemical or mechanical extraction of the cell wall materials.     

The use of a new,modified Dittmer–Lester spray reagent for phospholipid determination by the TLC image analysis technique

A new phospholipid‐specific spray reagent is described. A new phospholipid‐specific spray reagent, which is a modification of the Dittmer–Lester reagent, is described in authors' studies. The difference between these two reagents is in the addition of tin (II) chloride to the proposed spray reagent. The use of the described spray reagent together with an image analysis technique allows not only for qualitative, but also for quantitative, determination of major phospholipid classes. Separation of phosphatidylserine (PS), phosphatidylethanolamine (PE) and phosphatidylcholine (PC) was conducted on an HPTLC plate with a mixture of chloroform, methanol and 25% ammonia solution in a volume ratio of 65:25:4 as mobile phase. The calibration curves were linear in the ranges of 5.0–25.0, 1.5–15.0 and 1.0–20.0 µg/spot for PC, PS and PE, respectively. The use of the new spray reagent resulted also in lower limits of detection than the standard molybdenum method for the investigated phospholipids. The proposed method was used to determine the amount of PS in the dietary supplement ‘Session’, and of PS, PE and PC in biological samples, with good results. Copyright © 2012 John Wiley & Sons, Ltd.     

Application of interactive image analysis to light scattering patterns of some polymer systems

In this paper we describe a system for interactive picture analysis useful for extraction of various types of physical information from both real- and Fourier-space images. The possibility of hardware and software operations are illustrated, with examples of applications such as analysis of light scattering patterns from phase separated systems, semicrystalline polymer films with significant supermolecular morphology and from less regular polymer systems with a pronounced speckle effect. The analysis concentrates on both spatial and time dependent scattering processes and measurements of intensity correlation of scattered light. Although the present examples of application are limited to light scattering patterns of polymer systems, the technique of interactive picture analysis is useful for quantitative extraction of information from real-and Fourierspace images of arbitrary structure.     

Flexible and powerful strategy for qualitative and quantitative analysis of valepotriates in Valeriana jatamansi Jones using high‐performance liquid chromatography with linear ion trap Orbitrap mass spectrometry

Valeriana jatamansi Jones is an important medicinal plant and its quality is closely related to its region of origin. In the current study, we utilized a flexible and powerful strategy for comprehensive evaluation of the quality diversity for 15 regions in China. The method was based on a hybrid linear ion trap‐Orbitrap mass spectrometry platform. For structure characterization, fragmentation patterns were detected by analyzing a series of standard compounds using data dependent multistage mass spectrometry acquisition. A fragment ion database for valepotriates was established, and the acquired data were high throughput filtered by fragment ion search for compound identification. For quantitative purposes, we normalized the mass spectrometry data of 15 samples using SIEVE 2.0 and the differences in composition were analyzed using principal component analysis combined with hierarchical clustering analysis. The results identified a total of 92 compounds from Valeriana jatamansi Jones. Samples from Dali, Kunming, and Baoshan have better qualities and concentrations of the main active constituents. To verify our strategy, we compared the valtrate, acevaltrate, and baldrinal contents using high‐performance liquid chromatography with diode array detector. We developed and validated a comprehensive qualitative and quantitative analytical method to achieve quality control of Valeriana jatamansi Jones.