Search results for "Principal component"
showing 10 items of 514 documents
Biopartitioning micellar chromatography to predict skin permeability
2003
Dermal absorption of chemicals is an area of increasing interest to the pharmaceutical and cosmetic industries, as well as in dermal exposure and risk assessment processes. In this paper the capability of biopartitioning micellar chromatography (BMC) as an in vitro technique to describe compound percutaneous absorption is evaluated. A multivariate study (principal component analysis, partial least squares) is performed in order to evaluate the importance of some physicochemical variables on the skin permeability constant values. From these results, a quantitative retention-activity relationship model for predicting the skin permeability constants that uses the BMC retention data and melting…
Transition paths between phases IV, III and II of ammonium nitrate predicted from X-ray powder diffractometer and differential scanning calorimeter d…
1994
Abstract Ammonium nitrate solid phase transition paths between phases IV, III and II were explained and predicted on the basis of X-ray powder diffraction (XRD) and differential scanning calorimetry (DSC) data by applying partial least-squares regression (PLS) and principal component analysis (PCA). The samples were clustered according to their different transition paths with the PLS and PCA models, and the transition paths were predicted with PLS component clusters. The best PLS clusters were formed by a few first components. Prediction of the transition path with the PLS clusters made a semiquantitative prediction of the transition energy possible. In PCA, principal components 6 and 11, w…
Multimodality in galaxy clusters from SDSS DR8: substructure and velocity distribution
2012
We search for the presence of substructure, a non-Gaussian, asymmetrical velocity distribution of galaxies, and large peculiar velocities of the main galaxies in galaxy clusters with at least 50 member galaxies, drawn from the SDSS DR8. We employ a number of 3D, 2D, and 1D tests to analyse the distribution of galaxies in clusters: 3D normal mixture modelling, the Dressler-Shectman test, the Anderson-Darling and Shapiro-Wilk tests and others. We find the peculiar velocities of the main galaxies, and use principal component analysis to characterise our results. More than 80% of the clusters in our sample have substructure according to 3D normal mixture modelling, the Dressler-Shectman (DS) te…
Extinction law classification and lens redshift estimate by means of the principal component analysis
2007
Aims. We propose a method based on the Principal Component Analysis (PCA) to classify and estimate the redshift of an extinction law in a distant gravitational lens galaxy. Such extinction laws are very poorly known and an efficient method to characterize them is badly needed. Methods. We first compute the principal axes of an exhaustive collection of redshifted theoretical extinction laws. Then, we project on these new axes the extinction law we wish to classify. The position of its projection among those redshifted extinction laws from the collection allows us to characterize it and to estimate its redshift. Results. Monte Carlo simulations show that the method is efficient and relatively…
SDSS DR7 superclusters. Principal component analysis
2011
We apply the principal component analysis and Spearman's correlation test to study the properties of superclusters drawn from the SDSS DR7. We analyse possible selection effects in the supercluster catalogue, study the physical and morphological properties of superclusters, find their possible subsets, and determine scaling relations for superclusters. We show that the parameters of superclusters do not correlate with their distance. The correlations between the physical and morphological properties of superclusters are strong. Superclusters can be divided into two populations according to their total luminosity. High-luminosity superclusters form two sets, more elongated systems with the s…
Unsupervised deep feature extraction of hyperspectral images
2014
This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms…
Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms.
2021
Plant Ecological Unit’s (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). …
Contribution to a Taxonomic Revision of the Sicilian Helichrysum Taxa by PCA Analysis of Their Essential-Oil Compositions
2016
The chemical profile of the essential oils in ten populations of the genus Helichrysum Mill. (Asteraceae), collected in the loci classici of the nomenclatural types of the taxa endemic to Sicily, were analyzed. Our results confirm that the analysis of secondary metabolites can be used to fingerprint wild populations of Helichrysum, the chemical profiles being coherent with the systematic arrangement of the investigated populations in three main clusters, referring to the aggregates of H. stoechas, H. rupestre, and H. italicum, all belonging to the section Stoechadina. The correct nomenclatural designation of the investigated populations is discussed and the following two new combinations ar…
Molecular Clustering of Phenylurea Herbicides: Comparison with Sulphonylureas, Pesticides and Persistent Organic Pollutants
2014
Chromatographic retention times of phenylurea herbicides are modelled by structure–property relationships. Properties are hydration free energy and dipole. Bioplastic evolution is an evolutionary perspective conjugating the effect of acquired characters and relations that emerge among evolutionary indeterminacy, morphological determination and natural selection principles. Classification algorithms are proposed based on information entropy and production. Phenylureas are classified by Cl2, O2 and N2 presence; their different behaviour depends on the number of Cl atoms. When applying procedures to moderate-sized sets, excessive results appear compatible with data and suffer a combinatorial e…
Multivariate analysis of historical data (2004-2013) in assessing the possible environmental impact of the Bellolampo landfill (Palermo).
2017
Multivariate analysis was performed on a large data set of groundwater and leachate samples collected during 9 years of operation of the Bellolampo municipal solid waste landfill (located above Palermo, Italy). The aim was to obtain the most likely correlations among the data. The analysis results are presented. Groundwater samples were collected in the period 2004–2013, whereas the leachate analysis refers to the period 2006–2013. For groundwater, statistical data evaluation revealed notable differences among the samples taken from the numerous wells located around the landfill. Characteristic parameters revealed by principal component analysis (PCA) were more deeply investigated, and corr…