Comparative study of the alignment method on experimental and simulated chromatographic data

One of the major problems in the signal comparison of chromatographic data is the variability of response caused by instrumental drifts and others instabilities. Measures of quality control and evaluation of conformity are inherently sensitive to shift. It is essential to be able to compare test samples to reference samples in an evolutionary analytical environment by offsetting the inevitable drift. Therefore, prior to any multivariate analysis, the alignment of analytical signals is a compulsory preprocessing step. During recent years, many researchers have taken a greater interest in the study of the alignment. The present paper is an updated review on the alignment algorithms, methods, and improvements used in chromatography. The study is dedicated to one‐dimensional signals. Several of the exposed methods have common theoretical bases and can differ through their optimization methods. The main issue for the operator is to choose the appropriate method according to the type of signals to be processed.     

NMR波谱仪集中优化管理模式

论述了核磁中心采用的相对集中管理模式.集中管理模式主要体现在上样辅助、中央供气、数据集中上传与下载3个方面.另外,还提出了未来可以从集中供高纯N2和LN22个方面进一步改善中心的集中管理模式,从而能够更好的优化测试效率和工作环境.     

中药微量元素数据（16）

＂中药微量元素数据＂分为植物药、动物药、生物甲壳药、矿物药和中成药刊出,其中又细分为解表药、泻下药、清热药、利水渗湿药、祛风湿药、温里祛寒药、芳香化湿药、理气药、理血药、补养药、固涩药、化痰止咳药、消导药、熄风镇痛药、养心安神药、芳香开窍药、外用药、驱虫药、各类中药煎煮液。 检测的中药747种,共6 780味。每种药包括药名、学名、科属、部位、产地、元素含量、分析方法和文献来源等8个内容。以表格形式表示。每味药物最多测定38种元素,最少测定4种元素,平均测定17种元素,共展示出的元素数据约12万个。 每一个数据含义非常深刻,如序号为113的黄芩,共测定河北、山西、辽宁、黑龙江、山东、云南、四川、河南、吉林、陕西、甘肃、内蒙古、江苏等50个不同产地黄芩中的20种元素。又如序号为114的猕猴桃,共测定其中花、果、叶、枝、茎、根、皮、汁等8个不同部位中的23种元素。从元素的角度去研究中药不同产地、不同部位的药用功效,很有科研价值和实用意义。还可以用模式识别法去研究微量元素与中药的解表、清热、理气、理血等药性的关系,指导合理用药和配伍,深入研究中药与元素数据的关系,可以发现其中许多意想不到的科学规律。 ＂数据＂所参考的1 036条文献,全是公开发表的论文和专著,是中国科研人员使用34种先进的分析测试方法所得的分析测试数据,是研究课题的＂数据＂,有高度的可靠性。对中药进行＂解剖＂测试,不但测定其＂全身＂中的元素,还测定其不同＂器官＂中的元素含量,如人参,既测定全参,又测定人参子、人参叶、人参皮、人参花、人参芦、人参茎、人参果、人参须等8个不同部位的元素含量,用元素功能指导临床用药很有帮助,甚至有些可用替代药,而且用中药治疗的阴虚、阳虚?     

Synthesis of chlorophyll-a derivatives possessing (un)substituted 13-exo-methylene moiety and their optical properties

Chlorophyll-a derivatives possessing an un/mono/disubstituted methylene moiety at the 13¹-position were prepared by (un)substituted methylation of the 13-carbonyl group and successive dehydration. Substitution of the 13¹-oxo to the methylene group slightly blue-shifted electronic absorption and emission bands in a solution and decreased chemical stability to give an oxidation product cleaved at the E-ring. Further mono/disubstitution at the methylene terminal increased wavelengths of absorption and emission maxima as well as oxidative tolerance.     

Liquid-liquid equilibria for mixtures of (ethylene carbonate + aromatic hydrocarbon + cyclohexane)

Liquid-liquid equilibrium data for mixtures of (ethylene carbonate + benzene + cyclohexane) at temperatures 303.15 and 313.15 K and (ethylene carbonate + BTX + cyclohexane) at temperature 313.15 K are reported, where the BTX is benzene, toluene and m-xylene. The compositions of liquid phases at equilibrium were determined by gas liquid chromatography. The selectivity factors and partition coefficients of ethylene carbonate for the extraction of benzene, toluene and m-xylene from (ethylene carbonate + BTX + cyclohexane) are calculated and presented. The obtained results are compared with the selectivity factors and partition coefficients of ethylene carbonate for the extraction of benzene from (ethylene carbonate + benzene + cyclohexane). The liquid-liquid equilibrium data were correlated with the UNIQUAC and NRTL activity coefficient models. The phase diagrams for the studied mixtures are presented and the correlated tie line results have been compared with the experimental data. The comparisons indicate the applicability of the UNIQUAC and NRTL activity coefficients model for liquid-liquid equilibrium calculations of the studied mixtures. The tie line data of the studied mixtures also were correlated using the Hand method.     

Wood identification using pressure DSC data

This work addresses the problem of supervised classification of industrial wood species (seven different types in the present study) through their thermo‐oxidative stability. This is evaluated by pressure differential scanning calorimetry (PDSC) using the ASTM E2009. The maximization of the ratio of correct classification and the reduction of the costs of this activity are intended. This supervised classification problem was carried out using two different proposals: applying novel nonparametric functional data analysis techniques, based on kernel estimation, to the original PDSC curves, and using machine learning classification approaches applied to different multivariate data sets. The multivariate data sets were obtained, on the one hand, by estimating the fractal (Hausdorff) dimension of the PDSC curves by several methods, jointly with selecting the parameters from fitting a nonlinear model to the PDSC curves and, on the other hand, applying principal component analysis or partial linear squares to the thermograms. The results obtained show that the PDSC curves can be used to discriminate wood samples when these innovative and traditional statistical techniques are applied. In the best of the cases, a probability of correct classification that equals to 0.92 was obtained. PDSC represents a new alternative to the use of images, spectra, and other thermal signals as thermogravimetric analysis for classification purposes.Copyright © 2013 John Wiley & Sons, Ltd.     

Chemical data,ways and means,past and future

A brief outline of various data handling methods, from linear learning machines, principal component analysis, experimental design, and modeling to visualization, optimization, and validation together with a personal view on the historical development of the use of these methods, is given. Some future trends in handling chemical data are proposed as well. Copyright © 2014 John Wiley & Sons, Ltd.     

Covariances Simultaneous Component Analysis: a new method within a framework for modeling covariances

In modern omics research, it is more rule than exception that multiple data sets are collected in a study pertaining to the same biological organism. In such cases, it is worthwhile to analyze all data tables simultaneously to arrive at global information of the biological system. This is the area of data fusion or multi‐set analysis, which is a lively research topic in chemometrics, bioinformatics, and biostatistics. Most methods of analyzing such complex data focus on group means, treatment effects, or time courses. There is also information present in the covariances among variables within a group, because this relates directly to individual differences, heterogeneity of responses, and changes of regulation in the biological system. We present a framework for analyzing covariance matrices and a new method that fits nicely in this framework. This new method is based on combining covariance prototypes using simultaneous components and is, therefore, coined Covariances Simultaneous Component Analysis (COVSCA). We present the framework and our new method in mathematical terms, thereby explaining the (dis)similarities of the methods. Systems biology models based on differential equations illustrate the type of variation generated in real‐life biological systems and how this type of variation can be modeled within the framework and with COVSCA. The method is subsequently applied to two real‐life data sets from human and plant metabolomics studies showing biologically meaningful results. Copyright © 2015 John Wiley & Sons, Ltd.     

Serum pharmacochemistry combined with multiple data processing approach to screen the bioactive components and their metabolites in Mutan Cortex by ultra‐performance liquid chromatography tandem mass spectrometry

Root cortex of Paeonia suffruticosa Andrews (Paeoniaceae), known as Moutan Cortex (MC), is known to have anti‐allergic and anti‐inflammatory properties. However, the constituents absorbed into blood after oral administration of MC remain unknown. A sensitive and rapid method by ultra‐high‐pressure liquid chromatography–electrospray ionization–quadrupole‐time‐of‐flight mass spectrometry (UPLC‐ESI‐Q‐TOF‐MS) technology and the MetaboLynx^TM software combined with multiple data processing approach (Mdpa) was established to investigate the absorbed constituents in rats after oral administration of MC, providing unique high‐throughput capabilities for drug metabolism study. A hyphenated electrospray ionization and quadrupole‐time‐of‐flight analyzer was used for the determination of accurate mass of the fragment ion in negative mode, with excellent MS mass accuracy and enhanced data acquisition. This rapid automated analysis method was successfully applied for screening and identification of the constituents absorbed and metabolized studies of MC after oral administration to rats. A total of 46 peaks were obtained from MC, 41 of which were tentatively characterized. In the VIP‐plot of orthogonal partial least‐squares discriminant analysis, 23 interesting ions in serum samples were extracted, and 16 parent components and seven metabolites were detected in vivo. The integrative serum pharmacochemistry technique, UPLC‐ESI‐Q‐TOF‐MS, and Mdpa method were successfully applied for rapid discovery of multiple components from MC. Copyright © 2013 John Wiley & Sons, Ltd.     

A Method for Clustering and Screening of Long-dimensional Chemical Data Based on Fingerprints and Similarity Measurements

A method for the treatment of long-dimensional chemical data arrays is presented in this work with the aim of maximising classification models. The method is based on the construction of fingerprints and the subsequent generation of a similarity matrix. The similarity calculation has been modified through a scaling process to take into account different significance shown by the variables. The method was applied to spectral measurements of wines and several aspects were studied, namely: threshold considered in the construction of fingerprints and patterns, weighting factor used for scaling, normalisation method, etc. The application of both Principal Components Analysis and Soft-Independent Modelling of Class Analogies to the similarity matrices gave better classifications of the information than those obtained using original data.