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used to determine the contents of gallic acid and ellagic acid in the antioxidant-active parts of E. angustifolia L. leaves at different dates and from different origins in Xinjiang. In this work, R software hierarchical clustering analysis method was

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Abstract  

Aerosol samples collected around the Chilean site Lonquimay during major volcanic activities in January 1989 have been subjected to microPIXE measurements of 1 mm lateral resolution in the Debrecen Institute. Elemental concentrations relative to calcium have been determined for Al, Si, P, S, K, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, and Ba in 187 individual aerosol particles with the particle sizes between 15 mm and 1 mm. On the basis of a cluster analysis performed on the data set we defined eight clusters. Scatter plots for selected pairs of elements as Si/Al, K/Si, S/Cl, and Al/S elemental ratios that are considered as signatures characterizing types and mechanisms in volcanic eruption - have been compared with published data available in the literature for various volcanic sites.

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The Interpretation of Analytical Data by the Use of Cluster Analysis Wiley New York . [7]. W. Vogt

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Cluster Analysis , John Wiley & Sons, New York, 1999 . [29] H.C. Romesburg , Cluster Analysis for Researchers, Lifetime Learning

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shells preparations. The chemical fingerprints of Moringa seed shells from various regions were established and investigated by similarity analysis (SA), principal component analysis (PCA) and hierarchical clustering analysis (HCA). The combination of

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142 388 396 L. Kaufman and P.J. Rousseeuw Finding groups in data; An introduction to cluster analysis, John Wiley & Sons, New York, 1990

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program. For each sample five spectra were collected. Chemometrics Similarities between FT-Raman spectra were studied using Hierarchical Cluster Analysis (program Opus/Bruker package 5.1). The spectra were not baseline

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Summary

Chemometrics involves strategies to analyse multivariate data using interdisciplinary approaches aiming to extract relevant information from complex data. Chemometric strategies comprise both the pre-processing of the data, where the choice of methodology is domain-specific, and analysis of the resulting data after preprocessing using multivariate methodology. Although use of multivariate data analysis for gel electrophoresis images has increased substantially in the last decade, its use is still much less frequent than use of univariate approaches. Considering the complexity of the electrophoresis gel images and the multivariate nature of the proteome, applying multivariate data analysis for gel electrophoresis images gives information which is otherwise lost. This paper is written as a review and guideline of chemometric strategies used for analysis of gel electrophoresis images. The multivariate data analyses described are, however, also relevant for other proteome data, for example mass spectrometry, and for functional genomics in general.

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[ 41 , 42 ]. Capone et al. applied multivariate statistical methods (PCA and hierarchical cluster analysis [HCA]) to find key features involving molecules, descriptors, and anticancer activity [ 43 ]. Mehl et al. used hyperspectral image analysis (HIS

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Application of microcalorimetry and chemometric analysis

Effects evaluation of angle and nail animal drugs on Bacillus subtilis growth

Journal of Thermal Analysis and Calorimetry
Authors: Xiaoyan Xing, Yanling Zhao, Weijun Kong, Yanwei Zhong, Dan Yan, Ping Zhang, Yumei Han, Lei Jia, Cheng Jin, and Xiaohe Xiao

.0 (SAS, USA). Hierarchical clustering analysis (HCA) The HCA is a chemometric method that is used to sort samples into groups and typically illustrated by a dendrogram [ 17 – 19 ]. This technique classifies

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