Artificial Neural Network and Fuzzy Clustering – New Tools for Evaluation of Depth Profile Data?

 Depth profiling has been performed by using Auger electron spectrometry (AES) and X-ray photoelectron spectrometry (XPS) in combination with Ar-ion sputtering. The data obtained by both surface-analytical methods have been evaluated by means of factor analysis and afterwards by applying an artificial neural network or fuzzy clustering in order to determine the compositional layering of different samples such as a Cr₂O₃/CrN sandwich layer, tarnish layers on a nickel based alloy and on steel, and the coating of a Si₃N₄ ceramic powder. The applied artificial neural network was a Kohonen network. It turned out that the method of fuzzy c-means clustering was more successful than Kohonen network due to the fact that fuzzy c-means clustering starts with more input information which can be obtained from factor analysis.     

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

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

Simultaneous qualitative and quantitative analysis of commercial bistorta rhizome and its differentiation from closely related herbs using TLC and HPLC-DAD fingerprinting

A qualitative and quantitative analysis method was established to improve quality assessment standards for Rhizoma Polygoni Bistortae (Polygonum bistorta L.) and differentiate commercial bistorta rhizome from closely related herbs by TLC and HPLC-DAD fingerprinting. Three compounds including phenolic acid and flavane were identified by comparison with standard compounds and quantified simultaneously by HPLC-DAD simultaneously. A comprehensive validation of the method that included sensitivity, linearity, repeatability and recovery was conducted. Paris polyphylla SM., a herb often mixed with Polygonum bistorta L. in China due to their same popular name "Caoheche" in history, was successfully distinguished by thin-layer chromatography (TLC) fingerprinting of the petroleum-soluble fraction. Polygonum paleaceum WALL., another herb often mixed with Polygonum bistorta L. due to their similar external appearances, was distinguished by HPLC fingerprinting.     

Mass spectrometry in the characterization of ambers. I. Studies of amber samples of different origin and ages by laser desorption ionization, atmospheric pressure chemical ionization and atmospheric pressure photoionization mass spectrometry

Amber is a fossil resin constituted of organic polymers derived through complex maturation processes of the original plant resin. A classification of eight samples of amber of different geological age (Miocene to Triassic) and geographical origin is here proposed using direct mass spectrometric techniques, i.e. laser desorption ionization (LDI), atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI), in order to obtain a fingerprint related to the amber origin. Differences and similarities were detected among the spectra with the four methods, showing quite complex spectra, full of ionic species in the mass range investigated (up to m/z 2000). The evaluation required statistical analysis involving multivariate techniques. Cluster analysis or principal component analysis (PCA) generally did not show a clear clustering with respect to the age of samples, except for the APPI method, which allowed a satisfying clustering. Using the total ion current (TIC) obtained by the different analytical approaches on equal quantities of the different amber samples and plotted against the age, the only significant correlation appeared to be that involving APPI. To validate the method, four amber samples from Cretaceous of Spain were analyzed. Also in this case a significant correlation with age was found only with APPI data. PCA obtained with TIC values from all the MS methods showed a fair grouping of samples, according to their age. Three main clusters could be detected, belonging to younger, intermediate and older fossil resins, respectively. This MS analysis on crude amber, either solid or extract, followed by appropriate multivariate statistical evaluation, can provide useful information on amber age. The best results are those obtained by APPI, indicating that the quantity of amber soluble components that can be photoionized decreases with increasing age, in agreement with the formation of highly stable, insoluble polymers.     

Study of traffic-emitted lead pollution of soil and plants using different fuzzy clustering algorithms

We discuss the clustering of 234 environmental samples resulting from an extensive monitoring program concerning soil lead content, plant lead content, traffic density, and distance from the road at different sampling locations in former East Germany. Considering the structure of data and the unsatisfactory results obtained applying classical clustering and principal component analysis, it appeared evident that fuzzy clustering could be one of the best solutions. In the following order we used different fuzzy clustering algorithms, namely, the fuzzy c-means (FCM) algorithm, the Gustafson–Kessel (GK) algorithm, which may detect clusters of ellipsoidal shapes in data by introducing an adaptive distance norm for each cluster, and the fuzzy c-varieties (FCV) algorithm, which was developed for recognition of r-dimensional linear varieties in high-dimensional data (lines, planes or hyperplanes). Fuzzy clustering with convex combination of point prototypes and different multidimensional linear prototypes is also discussed and applied for the first time in analytical chemistry (environmetrics). The results obtained in this study show the advantages of the FCV and GK algorithms over the FCM algorithm. The performance of each algorithm is illustrated by graphs and evaluated by the values of some conventional cluster validity indices. The values of the validity indices are in very good agreement with the quality of the clustering results. Figure Projection of all samples on the plane defined by the membership degrees to cluster A₂, and A₄ obtained using Fuzzy c-varieties (FCV) algorithm (expression of objective function and distance enclosed)     

The Potential Use of Herbal Fingerprints by Means of HPLC and TLC for Characterization and Identification of Herbal Extracts and the Distinction of Latvian Native Medicinal Plants

The growing market of herbal medicines, the increase in international trade in Latvia, and the lack of adequate analytical methods have raised the question of the potential use of herbal fingerprinting methods. In this study, high-performance liquid chromatography (HPLC) and thin layer chromatography (TLC) methods were developed for obtaining chromatographic fingerprints of four taxonomically and evolutionary different medicinal plants (Hibiscus sabdariffa L., Calendula officinalis L., Matricaria recutita L., Achillea millefolium L.). Retention time shifting, principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal projections to latent structures (OPLS) analysis were used to improve and analyze the obtained fingerprints. HPLC data detection at 270 nm was determined superior to 360 nm for the distinction of medicinal plants and used data alignment method significantly increased similarity between samples. Analyzed medicinal plant extracts formed separate, compact clusters in PCA, and the results of HCA correlated with the evolutionary relationships of the analyzed medicinal plants. Herbal fingerprinting using chromatographic analysis coupled with multivariate analysis has a great potential for the identification of medicinal plants as well as for the distinction of Latvian native medicinal plants.     

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。     

基于核苷类化合物HILIC-ESI-TOF/MS分析的海洋动物药指纹图谱方法

探讨了核苷类化合物指纹图谱用于不同海洋动物药真伪鉴别的可行性, 为贵重动物药的鉴别提供了一种新方法. 采用亲水色谱-电喷雾飞行时间质谱(HILIC-ESI-TOF/MS)对不同海洋动物药中的16种核苷类化合物进行分析, 构建了基于16种核苷类化合物的特征指纹图谱, 结合相似度分析和聚类分析, 用于不同海洋动物药的鉴别. 结果表明, 基于核苷类化合物HILIC-ESI-TOF/MS分析的指纹图谱能反映不同海洋动物药各自的固有特征, 结合相似度分析和聚类分析可实现对不同海洋动物药的正确区分. 说明核苷类化合物指纹图谱有望成为动物药鉴别的新方法.     

Rapidly differentiating grape seeds from different sources based on characteristic fingerprints using direct analysis in real time coupled with time‐of‐flight mass spectrometry combined with chemometrics

The seeds of grapevine (Vitis vinifera) are a byproduct of wine production. To examine the potential value of grape seeds, grape seeds from seven sources were subjected to fingerprinting using direct analysis in real time coupled with time‐of‐flight mass spectrometry combined with chemometrics. Firstly, we listed all reported components (56 components) from grape seeds and calculated the precise m/z values of the deprotonated ions [M–H]^–. Secondly, the experimental conditions were systematically optimized based on the peak areas of total ion chromatograms of the samples. Thirdly, the seven grape seed samples were examined using the optimized method. Information about 20 grape seed components was utilized to represent characteristic fingerprints. Finally, hierarchical clustering analysis and principal component analysis were performed to analyze the data. Grape seeds from seven different sources were classified into two clusters; hierarchical clustering analysis and principal component analysis yielded similar results. The results of this study lay the foundation for appropriate utilization and exploitation of grape seed samples. Due to the absence of complicated sample preparation methods and chromatographic separation, the method developed in this study represents one of the simplest and least time‐consuming methods for grape seed fingerprinting.     

New approach based on fuzzy logic and principal component analysis for the classification of two-dimensional maps in health and disease. Application to lymphomas

Two-dimensional (2D) electrophoresis is the most wide spread technique for the separation of proteins in biological systems. This technique produces 2D maps of high complexity, which creates difficulties in the comparison of different samples. The method proposed in this paper for the comparison of different 2D maps can be summarised in four steps: (a) digitalisation of the image; (b) fuzzyfication of the digitalised map in order to consider the variability of the two-dimensional electrophoretic separation; (c) decoding by principal component analysis of the previously obtained fuzzy maps, in order to reduce the system dimensionality; (d) classification analysis (linear discriminant analysis), in order to separate the samples contained in the dataset according to the classes present in said dataset. This method was applied to a dataset constituted by eight samples: four belonging to healthy human lymph-nodes and four deriving from non-Hodgkin lymphomas. The amount of fuzzyfication of the original map is governed by the sigma parameter. The larger the value, the more fuzzy theresulting transformed map. The effect of the fuzzyfication parameter was investigated, the optimal results being obtained for sigma = 1.75 and 2.25. Principal component analysis and linear discriminant analysis allowed the separation of the two classes of samples without any misclassification.     

Fingerprint chromatogram analysis of Pseudostellaria heterophylla (Miq.) Pax root by high performance liquid chromatography

A simple and reliable high performance liquid chromatographic (HPLC) method has been developed and validated for the fingerprinting of extracts from the root of Pseudostellaria heterophylla (Miq.) Pax. HPLC with gradient elution was performed on an authentic reference standard of powdered P. heterophylla (Miq.) Pax root and 11 plant samples of the root were collected from different geographic locations. The HPLC chromatograms have been standardized through the selection and identification of reference peaks and the normalization of retention times and peak intensities of all the common peaks. The standardized HPLC fingerprints show high stability and reproducibility, and thus can be used effectively for the screening analysis or quality assessment of the root or its derived products. Similarity index calculations based on cosine angle values or correlation methods have been performed on the HPLC fingerprints. As a group, the fingerprints of the P. heterophylla (Miq.) Pax samples studied are highly correlated with closely similar fingerprints. Within the group, the samples can be further divided into subgroups based on hierarchical clustering analysis (HCA). Sample grouping based on HCA coincides nicely with those based on the geographical origins of the samples. The HPLC fingerprinting techniques thus have high potential in authentication or source-tracing types of applications.     

Classification of vegetable oils according to their botanical origin using sterol profiles established by direct infusion mass spectrometry

A simple and quick method to classify vegetable oils according to their botanical origin, based on direct infusion of sterol extracts into a mass spectrometer, was developed. Using mass spectrometry (MS) with either an electrospray ionization or an atmospheric pressure photoionization source, followed by linear discriminant analysis of the mass spectral data, oil samples corresponding to eight different botanical origins were perfectly classified with an excellent resolution among all the categories. An excellent correlation between the sterol profiles obtained by MS and by the official gas chromatography (with flame ionization detection) method was obtained. Thus, the proposed method is a promising alternative for sterol fingerprinting of vegetable oils, with the advantage that prior chromatographic separation is not required.     

Clustering files of chemical structures using the fuzzy k-means clustering method

This paper evaluates the use of the fuzzy k-means clustering method for the clustering of files of 2D chemical structures. Simulated property prediction experiments with the Starlist file of logP values demonstrate that use of the fuzzy k-means method can, in some cases, yield results that are superior to those obtained with the conventional k-means method and with Ward's clustering method. Clustering of several small sets of agrochemical compounds demonstrate the ability of the fuzzy k-means method to highlight multicluster membership and to identify outlier compounds, although the former can be difficult to interpret in some cases.     

光谱结合主成分分析和模糊聚类方法的样品聚类与识别

针对紫外光谱结合化学计量学方法快速测定渣油烃族组成模型适应性问题,对渣油光谱进行主成分分析,以主成分得分作为聚类的特征变量进行模糊聚类,建立了光谱结合主成分分析和模糊聚类方法的样品聚类与识别方法和识别,为光谱结合化学计量分析方法中构正模型的正确选择提供了依据。     

Fingerprinting and source identification of an oil spill in China Bohai Sea by gas chromatography-flame ionization detection and gas chromatography–mass spectrometry coupled with multi-statistical analyses

This paper describes a case study in which advanced chemical fingerprinting and data interpretation techniques were used to characterize the chemical composition and determine the source of an unknown spilled oil reported on the beach of China Bohai Sea in 2005. The spilled oil was suspected to be released from nearby platforms. In response to this specific site investigation need, a tiered analytical approach using gas chromatography–mass spectrometry (GC–MS) and gas chromatography-flame ionization detection (GC-FID) was applied. A variety of diagnostic ratios of “source-specific marker” compounds, in particular isomers of biomarkers, were determined and compared. Several statistical data correlation analysis methods were applied, including clustering analysis and Student's t-test method. The comparison of the two methods was conducted. The comprehensive analysis results reveal the following: (1) The oil fingerprinting of three spilled oil samples (S1, S2 and S3) positively match each other; (2) The three spilled oil samples have suffered different weathering, dominated by evaporation with decrease of the low-molecular—mass n-alkanes at different degrees; (3) The oil fingerprinting profiles of the three spilled oil samples are positive match with that of the suspected source oil samples C41, C42, C43, C44 and C45; (4) There are significant differences in the oil fingerprinting profiles between the three spilled oil samples and the suspected source oil samples A1, B1, B2, B3, B4, C1, C2, C3, C5 and C6.     

Optimum Ultrasound Assisted Extraction Conditions of Some Flavonoids from Green Tea Leaves. Control Quality of Green Tea Product by TLC Fingerprinting

The optimization of the green tea flavonoid extraction conditions was investigated. The experiments were carried out with two extraction methods: ultrasound assisted extraction (UAE) and reflux extraction (RE). The parameters that were varied in this study were: the extraction solvent system composition, the type of organic modifier of the extraction mixture, temperature, and time. The highest efficiency was obtained with an extraction mixture of ethanol: water, 80:20, v/v. An extraction performed at temperature of 45°C in 50–60 minutes led to optimum results. Moreover, a new fingerprinting procedure based on thin layer chromatography (TLC) image analysis was employed in order to compare the chemical composition of green tea in comparison with white and black tea.     

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.     

Simultaneous determination of methylxanthines in different types of tea by a newly developed and validated TLC method

The aim of this paper is to develop a new simple, fast and economical method for simultaneous quantitative determination of methylxanthine compounds based on TLC combined with image analysis. To obtain certain results, both extraction and chromatographic separation were optimized. The optimum extraction conditions were maceration in ethanol-water 8:2, v/v. The chromatographic separations were done on the silica gel F(254) TLC plates developed with chloroform-dichloromethane-isopropanol, 4:2:1 v/v/v. Detection was performed under UV lamp at 254?nm and the evaluation of the chromatographic plate was based on digital processing of chromatographic images. The developed TLC method was validated for parameters such as specificity, linearity and range, LOD and LOQ, precision, robustness and accuracy. This method was then applied for determination of caffeine, theobromine and theophylline in different types of tea, commercially available. Moreover, the content of methylxanthines detected and determined in commercial tea samples can be used as chemical marker in quality control.     

Characterisation of particulate matter from the Kara Sea using electron probe X-ray micro analysis

Automated electron probe X-ray micro-analysis (EPXMA) was used to characterise 17 600 individual particles collected in the Kara Sea and in the estuary of the Yenisey River. Samples were obtained by filtering and centrifugation of particles in suspension and from bottom sediments. Multivariate techniques were used to reduce the enormous data sets. By testing hierarchical, non-hierarchical and fuzzy clustering on the centrifuged suspension samples, it was shown that hierarchical clustering is most suitable. The two other clustering techniques are very time consuming and in most cases do not add any additional information. This hierarchical clustering of the data matrix results in different particle types which can be apportioned to their possible sources. The aluminosilicate particle types dominate in all samples and suppress the relative abundances of other interesting groups like metal-rich particles. However, Fe-rich and Ti-rich particles are still frequently found. Since the nearest industry is 200 km from the sampling site, it is especially surprising to find high contributions for Ti-rich particles which, because of their small diameter (i.e. between 0.6 μm and 1.1 μm), are most likely of anthropogenic origin. To complete this study the homogeneity and morphology of 100 particles from water suspension samples were analysed using manual EPXMA.     

Source allocation of organic air pollutants by application of fuzzy c-varieties pattern recognition

Data for polynuclear aromatic hydrocarbons (PAH) from 44 air samples are processed by unsupervised clustering techniques in oder to resolve the contributions from two sources (domestic and motor vehicles). The fuzzy c-varieties (FCV) clustering algorithms are applied. The cluster configuration which best describes the characteristic properties of the samples is selected by computation of validity discriminant coefficients. the FCV method permits the data samples to belong partially to different clusters, and source apportionments are estimated by multiplying the membership values by the PAH concentrations of the individual samples. The results are compared to those obtained by other methods of dispersion or receptor modelling in the same areas. The FCV method is valuable for estimating contributions from two types of emission sources.