可视化阵列传感器技术鉴别不同香型白酒

决定白酒香型的物质种类繁多,成分复杂.采用可视化阵列传感器技术对中国白酒五大香型的代表酒样进行检测,在可视化区分的基础上采用分层聚类分析、主成分分析等统计分析方法,对检测结果进行分析.不同香型的白酒在聚类分析中可以正确归类,利用主成分分析得到的前3个主成分所代表的白酒75.8%的信息量就可以将不同香型白酒完全区分开,表...     

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

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

Characterization of Sulfur-Compounds as Chemotaxonomic Markers in the Essential Oils of Allium Species by Solvent-Free Microwave Extraction and Gas Chromatography–Mass Spectrometry

Allium species were examined to authenticate the chemotaxonomic controversy about these plants by analyzing their extracted compound profiles. Essential oils of various species were isolated using conventional hydro-distillation and solvent-free microwave extraction (SFME). A comparison of the isolation procedures was performed. The presence of sulfur compounds in the Allium genus is a prominent characteristic for their medicinal uses. These components were characterized using two-way hierarchical cluster analysis (HCA) and principal component analysis (PCA). The variation of sulfur-compounds was performed by qualitative analysis of Allium species by gas chromatography–mass spectrometry (GC–MS). 2,4-Dimethyl-5,6-dithia-2,7-nonadienal, 4,6-diethyl-1,2,3,5-tetrathiolane, and 5,7-diethyl-1,2,3,4,6-pentathiepane were revealed as potential chemotaxonomic markers for all of the Alliums examined in this study. These markers may be used to provide improved systematics for other Allium species.     

Preliminary chemometric study of minerals and trace elements in Spanish infant formulae

The concentrations of minerals (Na, K, P, Ca and Mg) and trace elements (Fe, Zn, Cu, Mn, Se, Al, Cd and Pb) in a total of 105 different infant formulae (starter, follow-up, premature, specialised and soya formulae) marketed in Spain were determined by atomic spectrometry (flame and electrothermal) and inductively coupled plasma emission spectroscopy after acid-microwave decomposition. On the basis of the elements distribution, a preliminary chemometric study with the use of pattern recognition methods was carried out. Hierarchical cluster analysis (HCA), principal component analysis (PCA), as unsupervised exploratory techniques, and linear discriminant analysis (LDA), were applied to characterise, classify and distinguish the different types of infant formulae. The HCA results showed that mineral and trace element content data support adequate information to obtain the infant formula differentiation. PCA permitted the reduction of 13 variables to four principal components accounting for 61.9% of the total variability. This four-factor model interprets reasonably well the correlations of these studied elements. The obtained element associations may be attributed to the composition of matrix ingredients, the contamination during elaboration, the additives and mineral supplements added and the present tendency of standardization in the manufacture of infant formulae. The application of LDA gave a 77.1% of infant formulae correctly assigned with three clearly differentiated and two overlapped groups. The use of discriminant functions, as a complementary tool, to distinguish the different types depending on protein matrix of infant formula, is also discussed. This survey shows that HCA, PCA and LDA techniques appear useful tools for the characterisation and classification of infant formulae using their elemental profile.     

HPLC Fingerprint with Multi-components Analysis for Quality Consistency Evaluation of Traditional Chinese Medicine Si-Mo-Tang Oral Liquid Preparation

Si-Mo-Tang(SMT) oral liquid preparation, a traditional Chinese medicine, was prepared from four crude herbal drugs, Fructus Aurantii Submaturus, Radix Aucklandiae, Semen Arecae and Radix Linderae Aggregatae. A combinative method using HPLC fingerprint and quantitative analysis was developed and validated for quality consistency evaluation of SMT. Individual HPLC chromatograms were evaluated against the mean chromatogram generated via a similarity evaluation computer program. Data from chromatographic fingerprints were also processed with principal component analysis(PCA) and hierarchical cluster analysis(HCA). Additionally, six components (naringin, isonaringin, hesperidin, neohesperidin, norisoboldine and potassium sorbate) in SMT were simultaneously determined to interpret the quality consistency. For fingerprint analysis, 20 peaks were selected as the characteristic peaks to evaluate the similarities of 26 SMT collected from different manufacturers. Among the 20 characteristic peaks, 10 peaks were assigned to be naringin, hesperidin, neohesperidin, isonaringin, neoeriocitrin, tangeretin, nobiletin, norisoboldine, 5-(ethoxymethyl)furan-2-carbaldehyde and potassium sorbate, respectively. The results of similarity analysis, PCA and HCA, indicate that the samples from different manufacturers were consistent with each other in composition. The results from the quantitative data show that the contents of six compounds were significantly different in SMT oral liquid preparations from different manufacturers. The combinative method of chromatographic fingerprint with quantitative analysis developed here offered an efficient way for the quality consistency evaluation of the traditional Chinese medicine SMT.     

基于小波系数的近红外光谱局部建模方法与应用研究

局部建模方法使用与预测样本相似的样本建立模型,可解决光谱响应与浓度之间的非线性问题,扩大模型的适用范围,提高预测准确度。采用小波变换进行数据压缩并利用小波系数之间的欧氏距离作为光谱相似性的判据,实现了近红外光谱定量分析的局部建模方法,避免了样本之间的依赖性。将所建立的方法用于烟草样品中氯含量的测定,100次重复计算得到的预测集均方根误差(RMSEP)平均值为0.0665,标准偏差(σ)为0.0045,优于全局建模和基于主成分的局部建模方法。     

Adsorption of genetically engineered proteins studied by time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS). Part B: hierarchical cluster analysis (HCA)

In Part A, we adopted principal component analysis (PCA) for the analysis of TOF‐SIMS data to assess the binding specificity of GBP‐1 to metallic Au, Ag and Pd. Within a given set of data, PCA aids in the interpretation of the TOF‐SIMS spectra by capitalizing on the differences from one spectrum to another. In Part B, we introduce another multivariate statistical method called ‘hierarchical cluster analysis (HCA)’, where visualization of the similarity and difference in data is readily observed, from which a variety of adsorption conditions of GBP‐1 were characterized. Copyright © 2007 John Wiley & Sons, Ltd.     

Forensic analysis of <Emphasis Type="Italic">Salvia divinorum</Emphasis> using multivariate statistical procedures. Part II: association of adulterated samples to <Emphasis Type="Italic">S. divinorum</Emphasis>

Salvia divinorum is a plant material that is of forensic interest due to the hallucinogenic nature of the active ingredient, salvinorin A. In this study, S. divinorum was extracted and spiked onto four different plant materials (S. divinorum, Salvia officinalis, Cannabis sativa, and Nicotiana tabacum) to simulate an adulterated sample that might be encountered in a forensic laboratory. The adulterated samples were extracted and analyzed by gas chromatography–mass spectrometry, and the resulting total ion chromatograms were subjected to a series of pretreatment procedures that were used to minimize non-chemical sources of variance in the data set. The data were then analyzed using principal components analysis (PCA) to investigate association of the adulterated extracts to unadulterated S. divinorum. While association was possible based on visual assessment of the PCA scores plot, additional procedures including Euclidean distance measurement, hierarchical cluster analysis, Student’s t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores to provide a statistical evaluation of the association observed. The advantages and limitations of each statistical procedure in a forensic context were compared and are presented herein.     

Investigation of the chemical composition–antibacterial activity relationship of essential oils by chemometric methods

The antibacterial effects of Thymus vulgaris (Lamiaceae), Lavandula angustifolia (Lamiaceae), and Calamintha nepeta (Lamiaceae) Savi subsp. nepeta var. subisodonda (Borb.) Hayek essential oils on five different bacteria were estimated. Laboratory control strain and clinical isolates from different pathogenic media were researched by broth microdilution method, with an emphasis on a chemical composition–antibacterial activity relationship. The main constituents of thyme oil were thymol (59.95%) and p-cymene (18.34%). Linalool acetate (38.23%) and β-linalool (35.01%) were main compounds in lavender oil. C. nepeta essential oil was characterized by a high percentage of piperitone oxide (59.07%) and limonene (9.05%). Essential oils have been found to have antimicrobial activity against all tested microorganisms. Classification and comparison of essential oils on the basis of their chemical composition and antibacterial activity were made by utilization of appropriate chemometric methods. The chemical principal component analysis (PCA) and hierachical cluster analysis (HCA) separated essential oils into two groups and two sub-groups. Thyme essential oil forms separate chemical HCA group and exhibits highest antibacterial activity, similar to tetracycline. Essential oils of lavender and C. nepeta in the same chemical HCA group were classified in different groups, within antibacterial PCA and HCA analyses. Lavender oil exhibits higher antibacterial ability in comparison with C. nepeta essential oil, probably based on the concept of synergistic activity of essential oil components.     

Fingerprint and multicomponent quantitative analysis for the quality evaluation of Sibiraea angustata leaves by HPLC-DAD and HPLC-ESI-MS/MS combined with chemometrics

Sibiraea angustata leaves, known as a traditional Tibetan medicine, have been specially used in the treatment of indigestion and obesity. In the study, a simple and sensitive high-performance liquid chromatography (HPLC) method with a diode array detector (DAD) was established to solve the problem of lacking quality standard of S. angustata leaves, including the fingerprint analysis and quantification of six characteristic components. The analytical method was validated for linearity, repeatability, stability, recovery, and specificity. Seventeen raw samples and 1 processed sample of S. angustata leaves were collected from different locations of China to establish the fingerprint. The chemometric methods, including similarity analysis (SA), principal component analysis (PCA), and hierarchical clustering analysis (HCA), were applied to distinguish the 18 batches of S. angustata samples. The results successfully sorted these samples into five clusters and kept in line with each other. According to the result of the fingerprint analysis, 21 peaks were extracted to be the common peaks and most of them were identified by mass spectrometry (MS) with electron-spray ionization (ESI) in the negative mode. Meanwhile, the loading plot of PCA further indicated that the peaks of neochlorogenic acid, chlorogenic acid, ferulic acid, rutin, hyperin, and isoquercitrin played a greater role in the discrimination among the 21 peaks. So the six components mentioned above were investigated as index constituents to evaluate the quality of S. angustata leaves from different locations. The study demonstrated that the developed new method was a beneficial approach for authentication and quality evaluation of S. angustata leaves.     

Image evaluation with chemometric strategies for quality control of paints

In this study a complementary analytical methodology for quality of paints evaluation was developed. Four different primers applied to steel substrates and submitted to accelerated laboratory and outdoor exposure tests were taken into account. After this, digitalized images were obtained from these samples using a conventional scanner. The images were converted in gray color scale histograms, the resulting data were organized into a matrix form and analyzed with the help of principal component analysis (PCA) and hierarchical cluster analysis (HCA). It was possible to identify the best primers performance avoiding subjective interpretation.     

Forensic analysis of <Emphasis Type="Italic">Salvia divinorum</Emphasis> using multivariate statistical procedures. Part I: discrimination from related <Emphasis Type="Italic">Salvia</Emphasis> species

Salvia divinorum is a hallucinogenic herb that is internationally regulated. In this study, salvinorin A, the active compound in S. divinorum, was extracted from S. divinorum plant leaves using a 5-min extraction with dichloromethane. Four additional Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, and Salvia nemorosa) were extracted using this procedure, and all extracts were analyzed by gas chromatography–mass spectrometry. Differentiation of S. divinorum from other Salvia species was successful based on visual assessment of the resulting chromatograms. To provide a more objective comparison, the total ion chromatograms (TICs) were subjected to principal components analysis (PCA). Prior to PCA, the TICs were subjected to a series of data pretreatment procedures to minimize non-chemical sources of variance in the data set. Successful discrimination of S. divinorum from the other four Salvia species was possible based on visual assessment of the PCA scores plot. To provide a numerical assessment of the discrimination, a series of statistical procedures such as Euclidean distance measurement, hierarchical cluster analysis, Student’s t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores. The statistical procedures were then compared to determine the advantages and disadvantages for forensic applications.     

PLUXter: rapid discovery of metal-organic framework structures using PCA and HCA of high throughput synchrotron powder diffraction data

This paper reports on a systematic method--called PLUXter--developed for screening and data mining of large numbers of potential metal-organic framework compounds that have been synthesized then subsequently analyzed with high throughput synchrotron radiation X-ray powder diffraction. The first part of the method utilizes principal components analysis (PCA), which allows materials to be ranked in order of crystallinity so that undesirable amorphous materials may be identified and eliminated. The second part allows structural grouping within and between samples to be observed using hierarchical cluster analysis (HCA). Classification using a single linkage distance produced unsatisfactory clusters however the dendrogram's structural relationships were used to establish and guide the boundaries of groups. The resultant grouping identities allowed further structure-property studies to be undertaken on representative structures from the clusters, significantly reducing time and the use of resources.     

超高效液相色谱法结合化学计量学分析评价4种商品人参的质量

建立了快速、灵敏、准确的超高效液相色谱方法,用来分析4种商品人参(人参、红参、人参叶、人参须)中12种人参皂苷的含量,并用化学计量学方法评价了商品人参的质量。采用ACQUITY UPLC^TM BEH C₁₈色谱柱(50 mm×2.1 mm, 1.7 μm),以乙腈-水为流动相进行梯度洗脱。对所建立的测定12种人参皂苷的UPLC方法进行了线性方程、准确度、重复性、回收率等方法学考察。采用聚类分析和主成分分析的化学计量学方法对4种商品人参进行分析,评价了其质量。结果表明聚类分析和主成分分析2种化学计量学方法非常适合大样本、多成分的中药材质量分析。     

Quality assessment of cortex cinnamomi by HPLC chemical fingerprint, principle component analysis and cluster analysis

HPLC fingerprint analysis, principle component analysis (PCA), and cluster analysis were introduced for quality assessment of Cortex cinnamomi (CC). The fingerprint of CC was developed and validated by analyzing 30 samples of CC from different species and geographic locations. Seventeen chromatographic peaks were selected as characteristic peaks and their relative peak areas (RPA) were calculated for quantitative expression of the HPLC fingerprints. The correlation coefficients of similarity in chromatograms were higher than 0.95 for the same species while much lower than 0.6 for different species. Besides, two principal components (PCs) have been extracted by PCA. PC1 separated Cinnamomum cassia from other species, capturing 56.75% of variance while PC2 contributed for their further separation, capturing 19.08% variance. The scores of the samples showed that the samples could be clustered reasonably into different groups corresponding to different species and different regions. The scores and loading plots together revealed different chemical properties of each group clearly. The cluster analysis confirmed the results of PCA analysis. Therefore, HPLC fingerprint in combination with chemometric techniques provide a very flexible and reliable method for quality assessment of traditional Chinese medicines.     

SVD-based principal component analysis of geochemical data

Principal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2% of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration. The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.     

Structure-Activity Relationship of Fluoroquinolones Against K:pneumoniae

The structure-activity relationship of °uoroquinolones, which show anti-K. pneumoniae activity, was studied by using principal component analysis (PCA) and hierarchical cluster analysis (HCA). The PCA results showed that the lowest unoccupied molecular orbital energy, energy difference between the highest occupied and the lowest unoccupied molecular orbital, dipole moment, net atomic charge on atom I, molecular polarizability, partition coe±cient and molecular refractivity of these compounds are responsible for the separation between high-activity and low-activity groups. The HCA results were similar to those obtained with PCA. By using the chemometric results, four synthetic compounds were analyzed through PCA and HCA, and three of them are proposed as active molecules against K. pneumoniae which is consistent with the results of clinical experiments. The methodologies of PCA and HCA provide a reliable rule for classifying new °uoroquinolones with anti-K. pneumoniae activity.     

Differentiation of opium and poppy straw using capillary electrophoresis and pattern recognition techniques

Opium samples from four different locations and poppy straw from different plant varieties have been assayed using micellar capillary electrophoresis incorporating a sweeping technique. Individual alkaloids (morphine, codeine, papaverine, noscapine, thebaine, oripavine, reticuline and narceine) were quantitatively determined in the different samples by a validated capillary electrophoresis method. Unsupervised pattern recognition of the opium samples and the poppy straw samples using hierarchical cluster analysis (HCA) and principal component analysis (PCA), showed distinct clusters. Supervised pattern recognition using soft independent modelling of class analogy (SIMCA) was performed to show individual groupings and allow unknown samples to be classified according to the models built using the CZE assay results.