分析试验室

正《分析试验室》主要报道冶金、地质、石油、化工、环保、医药卫生、食品、农业等领域中分析化学专业的研究成果及具有实用推广价值的创新分析方法。本刊所设栏目有:"研究报告","研究简报","仪器装置与实验技术"     

金属材料分析

评述了2002年7月至2005年12月期间国内在金属材料分析领域的现状及进展概况。内容包括标准和标准样品、重量分析法、滴定分析法、分光光度法和荧光光度法、催化动力学光度法、原子吸收光谱法和原子荧光光谱法、原子发射光谱法、ICP-质谱、X-射线荧光光谱法、气体元素的分析、电化学方法等,涉及文献621篇。     

金属材料分析

本文是《分析试验室》定期评述中“金属材料分析”的第一篇论述,评述了１９９７年７月至１９９９年６月间国内金属材料分析的进展,包括概述、试样前处理,各种分析方法及化学计量学的应用研究。引用文献６７６篇。     

滴定分析

这是本刊定期评述中“滴定分析”的第３篇。内容含目视滴定、物理化学滴定、示波滴定、非水滴定等，覆盖１９９４．９～１９９７．６在国内发表的文献３５３篇。     

发光分析

本文是《分析试验室》定期评述专号中“发光分析”专题的第一篇评述文章。它评述了国内1984～1988年期间发光分析的工作进展。内容包括:概述、荧光分析、燐光分析、化学发光和生物发光分析等方面。引用文献436篇。     

药物分析

本文是在前一评述［１］的基础上，对国内药物分析在１９９５．１１～１９９７．１０年间的主要进展作了评述。内容包括：分光光度法、高效液相色谱法、气相色谱法、薄层色谱法、电泳法和其它方法等。共引用文献８７０篇。     

对应分析用于牛黄解毒片中As控制线的研究

用石墨炉原子吸收法分析测定了牛黄解毒片中的As、Hg、Cd等微量元素.并试图将对应因子分析法（CFA）应用于中成药牛黄解毒片中As化学分析数据的直接处理,探讨研究牛黄解毒片中As的控制线,以确保药品质量.结果表明,不论对数据施行交换与否,用对应因子分析方法得出的控制线很接近,证明化学计量学进入医药质量管理行业是可行的     

Sparse canonical correlation analysis applied to ‐omics studies for integrative analysis and biomarker discovery

With the rapid development of new ‐omics measurement methods, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods such as canonical correlation analysis (CCA) have been proposed to analyze the intrinsic correlation relationship by integrating two data sets. However, because of the high dimensionality of data and the relative scarcity of samples, the ordinary CCA is usually faced with variable selection problems and thereby fails to obtain a satisfactory relationship. Here, we explored the potential of sparse CCA (SCCA) to find the correlative components in two sparse views. SCCA aims at finding sparse projection directions to well extract the correlation between two data sets. We applied this method to one simulation data and one real ‐omics data to illustrate the performance of SCCA. The results from two studies show that SCCA could effectively find the correlated patterns between two data sets, which are of high importance for understanding the relationship between two underlying chemical or biological processes. The corresponding variable subsets selected by sparse weight vectors can assist in a better interpretation of the chemical or biological process. The integrative analysis from two views by SCCA helps in improving the discriminative ability of classification models for various ‐omics studies. Copyright © 2015 John Wiley & Sons, Ltd.     

Study of the influence of electronic effects on the retention of substitutedN-benzylideneanilines in normal-phase liquid chromatography

Summary A series of 72 substitutedN-benzylideneanilines (NBA) has been studied by normal-phase liquid chromatography by use of an experimental design based on variation of the composition of mobile phases prepared from heptane and three modifiers, tetrahydrofuran, 1-octanol and ethyl acetate, for each of which the specific interactions are different. Seven mobile phases were defined in the experimental design. The chromatographic data obtained are used to discuss the behavior of NBA by application of complementary chemometric methods—hierarchical ascending classification (HAC) and correspondence factor analysis (CFA). Although solute polarity has the greatest effect on retention, construction of HAC and CFA plots shows that solute behavior is also influenced by second-order electronic effects arising as a result of specific interactions between the solutes and the different modifier solvents. A quantitative structure-retention relationship has been established between Hammett's constants and the solutes projection on the first factorial axis of CFA.     

The use of multiple correspondence analysis for the evaluation of inter-laboratory comparisons

Participation in inter-laboratory comparisons (ILC) is one of the recommended means of external quality control according to ISO/IEC 17025:2005. Providers of ILC or proficiency test (PT) schemes collect, besides the measurement results on the test samples, information on the sample treatment and measurement procedure. The objective of this paper is to evaluate in a non-traditional way, using numerical and non-numerical data provided by the participants in IMEP-20 (trace elements in tuna fish), the additional information concerning the applied analytical methods and the accreditation/nomination status. Arsenic was taken as an example. The basic statistical procedure for the evaluation of questionnaire information was the multiple correspondence analysis (MCA). Univariate clustering techniques were applied for the categorization of the numerical data (measurement values). The methodology of the evaluation of supplementary non-numeric information used in this paper might serve (a) to providers of ILC (PT) schemes to modify/improve their questionnaires and (b) to give laboratories better guidance in the methods used for the determination of various analytes in different matrices. This paper is meant serve as a guide for the possible interpretation of the questionnaires accompanying ILC schemas. Presented at the 3rd International Conference on Metrology, November 2006, Tel Aviv, Israel     

Interpretation of geochemical data by the use of multivariate data analysis

Different strategies of multivariate data analysis are used to interpret a data base from geological samples. Cluster and correspondence analysis are applied to classify properly 34 chemical elements from 10 representative rock samples (volcanic series from Borovitsa, Rhodopa mountains, Bulgaria). Principal components analysis is also used as display method to visualize the relation between the variables and objects of interest. The multivariate data analysis applied makes it possible to interpret the origin and orogenesis of the samples.     

Characterization of stationary phases based on monosubstituted benzene retention indices using correspondence factor analysis and linear solvation energy relationships in RPLC

In reversed-phase liquid chromatography (RPLC), the comparison of experimental results obtained from different columns is a complex problem. A correspondence factor analysis (CFA) and a linear solvation energy relationship (LSER) were applied on retention data to characterize second-order intermolecular interactions responsible for retention on a set of RPLC columns. Seven octadecyl-C₁₈ columns with different packing materials are obtained from different manufacturers and one octyl-C₈ column. The retention data were determined under isocratic conditions using a methanol–water (65:35, v/v) mobile phase. The chromatographic retention indices based on alkan-2-ones and alkyl aryl ketones retention index scales are calculated using a multiparametric least-squares regressions iterative method. The CFA and LSER results permitted to highlight that the retention indices were appropriate for studying the second-order retention mechanisms on the eight chromatographic systems investigated and exhibited the best reproducibility. Although many earlier studies have reported the use of chemometric methods to characterize chemical factors affecting retention in RPLC using retention factors as retention parameters, this is the first study based on retention indices.     

基于近红外光谱技术与化学计量学的绿茶无损鉴别方法研究

该文利用近红外光谱技术结合化学计量学方法开发了不同品种绿茶的无损鉴别方法。通过近红外光谱技术得到了8个品种绿茶样品的近红外光谱,比较了单一以及优化组合光谱预处理方法对光谱的影响,利用无监督的主成分分析(PCA)与有监督的线性判别分析方法(LDA)分别构建了茶叶品种鉴别模型。结果表明：对比单一预处理方法,优化组合预处理具有更优的鉴别准确性。标准正态变量变换预处理消除了茶叶样品大小不均造成的光谱散射影响,一阶导数预处理实现了变动背景的消除,减少了基线漂移的影响,突出了图谱中的有效信息,采用二者相结合的预处理方式并结合无监督的主成分分析法可实现较为准确的绿茶样品种类鉴别分析,准确率达75.0%。此外,采用有监督的线性判别分析方法处理原始光谱数据,可达到100%的鉴别准确率,但该方法需提供类别的先验知识。因此,采用近红外光谱技术和化学计量学相结合的手段可实现不同品种绿茶的快速无损鉴别。     

Factor analytical approaches for evaluating groundwater trace element chemistry data

The multivariate statistical techniques principal component analysis (PCA), Q-mode factor analysis (QFA), and correspondence analysis (CA) were applied to a dataset containing trace element concentrations in groundwater samples collected from a number of wells located downgradient from the potential nuclear waste repository at Yucca Mountain, Nevada. PCA results reflect the similarities in the concentrations of trace elements in the water samples resulting from different geochemical processes. QFA results reflect similarities in the trace element compositions, whereas CA reflects similarities in the trace elements that are dominant in the waters relative to all other groundwater samples included in the dataset. These differences are mainly due to the ways in which data are preprocessed by each of the three methods.

The highly concentrated, and thus possibly more mature (i.e. older), groundwaters are separated from the more dilute waters using principal component 1 (PC 1). PC 2, as well as dimension 1 of the CA results, describe differences in the trace element chemistry of the groundwaters resulting from the different aquifer materials through which they have flowed. Groundwaters thought to be representative of those flowing through an aquifer composed dominantly of volcanic rocks are characterized by elevated concentrations of Li, Be, Ge, Rb, Cs, and Ba, whereas those associated with an aquifer dominated by carbonate rocks exhibit greater concentrations of Ti, Ni, Sr, Rh, and Bi. PC 3, and to a lesser extent dimension 2 of the CA results, show a strong monotonic relationship with the percentage of As(III) in the groundwater suggesting that these multivariate statistical results reflect, in a qualitative sense, the oxidizing/reducing conditions within the groundwater. Groundwaters that are relatively more reducing exhibit greater concentrations of Mn, Cs, Co, Ba, Rb, and Be, and those that are more oxidizing are characterized by greater concentrations of V, Cr, Ga, As, W, and U.     

Metabolomics data exploration guided by prior knowledge

In metabolomics research, it is often important to focus the data analysis to specific areas of interest within the metabolome. In this paper, we describe the application of consensus principal component analysis (CPCA) and canonical correlation analysis (CCA) as a means to explore the relation between metabolome data and (i) biochemically related metabolites and (ii) an amino acid biosynthesis pathway. CPCA searches for major trends in the behavior of metabolite concentrations that are in common for the metabolites of interest and the remainder of the metabolome. CCA identifies the strongest correlations between the metabolites of interest and the remainder of the metabolome.CPCA and CCA were applied to two different microbial metabolomics data sets. The first data set, derived from Pseudomonas putida S12, was relatively simple as it contained metabolomes obtained under four environmental conditions only. The second data set, obtained from Escherichia coli, was much more complex as it consisted of metabolomes obtained under 28 different environmental conditions. In case of the simple and coherent P. putida S12 data set, CCA and CPCA gave similar results as the variation in the subset of the selected metabolites and the remainder of the metabolome was similar.In contrast, CCA and CPCA yielded different results in case of the E. coli data set. With CPCA the trends in the selected subset - the phenylalanine biosynthesis pathway - dominated the results. The main trends were related to high and low phenylalanine productivity, and the metabolites showing a similar behavior in concentration were metabolites regulating the phenylalanine biosynthesis route in the subset and metabolites related to general amino acid metabolism in the remainder of the metabolome. With CCA, neither subset truly dominated the data analysis. CCA described the differences between the wild type and the overproducing strain and the differences between the succinate and glucose grown cells. For the difference between the wild type and the overproducing strain, metabolites from the beginning and the end of aromatic amino acid pathways like erythrose-4-phosphate, tryptophan, and phenylalanine were important for the selected metabolites.CCA and CPCA proved to be complementary data analysis tools that enable the focusing of the data analysis on groups of metabolites that are of specific interest in relation to the remainder of the metabolome. Compared to an ordinary PCA, focusing the data analysis on biologically relevant metabolites lead especially for the complex E. coli data to a better biological interpretation of the data.