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Advances in Data Analysis and Classification - The STAR model is widely used to represent the dynamics of a certain variable recorded at several locations at the same time. Its advantages are often...  相似文献   
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This work proposes a novel method for the direct flow injection profiling of acylglycerols in edible oils and fats without preliminary extraction and consequent reconstitution in the injection solvent. The work exploits the outstanding performance of high‐resolution mass spectrometry to target unique elemental compositions even in the most complex matrices. The performance of isopropanol as the unique solvent for both the solubilization and analysis of acylglycerols was investigated in comparison with other classical methods involving preliminary extractions, sample recovery, and analysis. The calibrations of two triglyceride standards (triolein and trilinolenin) were successfully performed in presence and absence of oil matrix. As final application, the effects on the acylglycerol fraction of a heat treatment on three different fat samples (extra virgin olive oil, lard oil, and fat from dry cured ham—speck) were monitored. The proposed method is therefore suitable for a rapid evaluation of acylglycerol fractions in food lipid samples.  相似文献   
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A series of six new Fe(II)NHC-carboxylic sensitizers with their ancillary ligand decorated with functions of varied electronic properties have been designed with the aim to increase the metal-to- surface charge separation and light harvesting in iron-based dye-sensitized solar cells (DSSCs). ARM130 scored the highest efficiency ever reported for an iron-sensitized solar cell (1.83 %) using Mg2+ and NBu4I-based electrolyte and a thick 20 μm TiO2 anode. Computational modelling, transient absorption spectroscopy and electrochemical impedance spectroscopy (EIS) revealed that the electronic properties induced by the dimethoxyphenyl-substituted NHC ligand of ARM130 led to the best combination of electron injection yield and spectral sensitivity breadth.  相似文献   
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The authors are glad for the opportunity to contribute to a volume in honour of professor Hartshorne, whose works always stands as a basic reference for researchers in Algebraic Geometry.  相似文献   
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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.  相似文献   
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