Search results for "Component analysis"
showing 10 items of 562 documents
Chemometric investigation on structural changes in pine kraft lignin during pulping
2000
Abstract Various structural changes which take place in dissolved lignin during the laboratory-scale kraft pulping of Scots pine (Pinus sylvestris) were studied. Lignin samples were subjected to the alkaline cupric oxide oxidation and the analytical data further processed by various multivariate chemometric techniques (principal component analysis, PCA; principal component regression, PCR; and projection to latent structures, PLS). Several models applicable to the indirect measurement of common pine kraft pulp properties (i.e., total cooking yield, kappa number and ISO brightness) were produced.
Biodiversità in alcune specie del genere Mentha nel territorio dei Monti Nebrodi (Sicilia N-W).
2008
In Nebrodi Mountains (N-E Sicily), the genus Mentha is represented by an high number of species, in many cases typical of humid and subhumid environments. This work was addressed to investigate about the biomorphological traits of various accessions belonging to 3 Mentha species collected in the Nebrodi highlands, namely 22 accessions of Mentha spicata, 12 of Mentha suaveolens and 13 of Mentha aquatica. Plant material was picked up starting from the early spring 2007, from different sites as in altitude and in pedological and climatic traits, that were distributed over an area of 200.000 ha approx.. Later on, all collected plant individuals were transplanted in a collection field, appositel…
Molecular characterization of Sicilian lentil ecotypes using ISSR.
2013
Classification of Spanish Mosquitoes in Functional Groups
2011
Abstract We present a classification of Spanish mosquitoes according to their different life cycles. The bio-ecological parameters analyzed in our study were oviposition sites, overwintering stages, preferred hosts, and number of generations per year for each species. The results revealed 13 different functional groups. To assess the validity and robustness of the classification system, we analyzed the data from an intensive sampling carried out over a period of 4 years (2005–08) in eastern Spain. In this area, 9 of the 13 functional groups were found. The Jaccard cluster and the principal components analysis (between-group analysis method) revealed 3 different mosquito groups: the tree hol…
Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images
2014
Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlat…
How to validate similarity in linear transform models of event-related potentials between experimental conditions?
2014
Abstract Background It is well-known that data of event-related potentials (ERPs) conform to the linear transform model (LTM). For group-level ERP data processing using principal/independent component analysis (PCA/ICA), ERP data of different experimental conditions and different participants are often concatenated. It is theoretically assumed that different experimental conditions and different participants possess the same LTM. However, how to validate the assumption has been seldom reported in terms of signal processing methods. New method When ICA decomposition is globally optimized for ERP data of one stimulus, we gain the ratio between two coefficients mapping a source in brain to two…
The Scree Test and the Number of Factors: a Dynamic Graphics Approach
2015
Exploratory Factor Analysis and Principal Component Analysis are two data analysis methods that are commonly used in psychological research. When applying these techniques, it is important to determine how many factors to retain. This decision is sometimes based on a visual inspection of the Scree plot. However, the Scree plot may at times be ambiguous and open to interpretation. This paper aims to explore a number of graphical and computational improvements to the Scree plot in order to make it more valid and informative. These enhancements are based on dynamic and interactive data visualization tools, and range from adding Parallel Analysis results to "linking" the Scree plot with other g…
Genome wide linkage disequilibrium and genetic structure in Sicilian dairy sheep breeds
2014
Background The recent availability of sheep genome-wide SNP panels allows providing background information concerning genome structure in domestic animals. The aim of this work was to investigate the patterns of linkage disequilibrium (LD), the genetic diversity and population structure in Valle del Belice, Comisana, and Pinzirita dairy sheep breeds using the Illumina Ovine SNP50K Genotyping array. Results Average r2 between adjacent SNPs across all chromosomes was 0.155 ± 0.204 for Valle del Belice, 0.156 ± 0.208 for Comisana, and 0.128 ± 0.188 for Pinzirita breeds, and some variations in LD value across chromosomes were observed, in particular for Valle del Belice and Comisana breeds. Ave…
Lossless coding of hyperspectral images with principal polynomial analysis
2014
The transform in image coding aims to remove redundancy among data coefficients so that they can be independently coded, and to capture most of the image information in few coefficients. While the second goal ensures that discarding coefficients will not lead to large errors, the first goal ensures that simple (point-wise) coding schemes can be applied to the retained coefficients with optimal results. Principal Component Analysis (PCA) provides the best independence and data compaction for Gaussian sources. Yet, non-linear generalizations of PCA may provide better performance for more realistic non-Gaussian sources. Principal Polynomial Analysis (PPA) generalizes PCA by removing the non-li…