Search results for " Principal component analysis"

Land Snails as a Valuable Source of Fatty Acids: A Multivariate Statistical Approach

2019

The fatty acid (FA) profile of wild Theba pisana, Cornu aspersum, and Eobania vermiculata land snail samples, collected in Sicily (Southern Italy), before and after heat treatment at +100 °C were examined by gas chromatography with a flame ionization detector (GC-FID). The results show a higher content of polyunsaturated fatty acids (PUFAs) in all of the examined raw snails samples, representing up to 48.10% of the total fatty acids contents, followed by monounsaturated fatty acids (MUFAs). The thermal processing of the snail samples examined determined an overall reduction of PUFA levels (8.13%, 7.75%, and 4.62% for T. pisana, C. aspersum and E. vermiculata samples, respectively) and a spe…

A more distinctive representation for 3D shape descriptors using principal component analysis

2015

Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-rigid three-dimensional shapes and Classical Multidimensional Scaling (Classical MDS) method in object classification which we quote, in particular, the example of Jian Sun et al. (2009) [1]. However, in this paper, the main focuses on classification that we propose a concise and provably factorial method by invoking Principal Component Analysis (PCA) as a classifier to improve the scheme of 3D shape classification. To avoid losing or disordering information after extracting features from the mesh, PCA is used instead of the Classical MDS to discriminate-as much as possible-feature points for each…

Image enhancement by region detection on CFA data images

2007

Learning with the kernel signal to noise ratio

2012

This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in nonlinear regression examples, dependence estimation and causal inference, nonlinear channel equalization, and nonlinear feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extract…

Explicit signal to noise ratio in reproducing kernel Hilbert spaces

2011

This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal features jointly. Results show that the proposed KMNF provides the most noise-free features when confronted with PCA, MNF, KPCA, and the previous version of KMNF. Extracted features with the explicit KMNF…

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

A new methodology based on functional principal component analysis tostudy postural stability post-stroke

2018

[EN] Background. A major goal in stroke rehabilitation is the establishment of more effective physical therapy techniques to recover postural stability. Functional Principal Component Analysis provides greater insight into recovery trends. However, when missing values exist, obtaining functional data presents some difficulties. The purpose of this study was to reveal an alternative technique for obtaining the Functional Principal Components without requiring the conversion to functional data beforehand and to investigate this methodology to determine the effect of specific physical therapy techniques in balance recovery trends in elderly subjects with hemiplegia post-stroke. Methods: A rand…

Food quality and nutraceutical value of nine cultivars of mango (Mangifera indica L.) fruits grown in Mediterranean subtropical environment

2019

Mango (Mangifera indica L.) quality is strongly influenced by genotype but individuating the most appropriate harvesting time is essential to obtain high quality fruits. In this trial we studied the influences of the ripening stage at harvest (mature-ripe or green-ripe) on quality of ready to eat mango fruits from nine cultivars (Carrie, Keitt, Glenn, Manzanillo, Maya, Rosa, Osteen, Tommy Atkins and Kensington Pride) grown in the Mediterranean subtropical climate through physicochemical, nutraceutical, and sensory analysis. Our results show a large variability among the different observed genotypes and in dependence of the ripening stage at harvest. With the exception of Rosa, mature-ripe f…

Sperm kinematic, head morphometric and kinetic-morphometric subpopulations in the blue fox (Alopex lagopus)

2016

This work provides information on the blue fox ejaculated sperm quality needed for seminal dose calculations. Twenty semen samples, obtained by masturbation, were analyzed for kinematic and morphometric parameters by using CASA-Mot and CASA-Morph system and principal component (PC) analysis. For motility, eight kinematic parameters were evaluated, which were reduced to PC1, related to linear variables, and PC2, related to oscillatory movement. The whole population was divided into three independent subpopulations: SP1, fast cells with linear movement; SP2, slow cells and nonoscillatory motility; and SP3, medium speed cells and oscillatory movement. In almost all cases, the subpopulation dis…