Search results for "Principal Component Analysis"
showing 10 items of 486 documents
Comparison of canonical variate analysis and principal component analysis on 422 descriptive sensory studies
2015
International audience; Although Principal Component Analysis (PCA) of product mean scores is most often used to generate a product map from sensory profiling data, it does not take into account variance of product mean scores due to individual variability. Canonical Variate Analysis (CVA) of the product effect in the two-way (product and subject) multivariate ANOVA model is the natural extension of the classical univariate approach consisting of ANOVAs of every attribute. CVA generates successive components maximizing the ANOVA F-criterion. Thus, CVA is theoretically more adapted than PCA to represent sensory data. However, CVA requires a matrix inversion which can result in computing inst…
A multivariate approach to the study of orichalcum ingots from the underwater Gela's archaeological site
2017
Abstract In this work a careful ICP-OES and ICP-MS investigation of 38 ancient ingots has been performed to determine both major components and trace elements content to find a correlation between the observed different features and the composition. The ingots, recovered in an underwater archaeological site of various finds near Gela (CL, Italy), were previously investigated by X-Ray Fluorescence (XRF) spectroscopy to know the composition of the alloy and it was found that the major elements were copper and zinc, in a ratio compatible with the famous orichalcum similar to the contemporary brass that was considered a precious metal in ancient times. The discovery of huge amount this alloy is…
On the internal multivariate quality control of analytical laboratories. A case study: the quality of drinking water
2001
Abstract Multivariate statistical process control (MSPC) tools, based on principal component analysis (PCA), partial least squares (PLS) regression and other regression models, are used in the present study for automatic detection of possible errors in the methods used for routine multiparametric analysis in order to design an internal Multivariate Analytical Quality Control (iMAQC) program. Such tools could notice possible failures in the analytical methods without resorting to any external reference since they use their own analytical results as a source for the diagnosis of the method's quality. Pseudo-univariate control charts provide an attractive alternative to traditional univariate …
Application of multivariate statistics to the problems of upper palaeolithic and mesolithic samples
1987
Multivariate statistics (discriminant function analysis and principal component analysis) have been applied to a broad sample of Upper Paleolithic and mesolithic skulls. In addition to some methodological problems concerning the evaluation of missing data by principal component analysis, we discussed the possibility of misclassifications (14%).
Multivariate Exploratory Comparative Analysis of LaLiga Teams: Principal Component Analysis
2021
The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered reducing an extensive data matrix to improve interpretation, using PCA. Subsequently, with new components and with multiple linear regression, we have carried out a comparative analysis between the best and bottom teams of LaLiga. The sample consisted of the matches corresponding to the 2015/16, 2016/17 and 2017/18 seasons. The results showed that the best teams were characterized and differentiated from bottom teams in the realization of a great…
Background Correction and Multivariate Curve Resolution of Online Liquid Chromatography with Infrared Spectrometric Detection
2011
J.K. acknowledges the “V Segles” grant provided by the University of Valencia to carry out this study. Authors acknowledge the financial support of Ministerio de Educación y Ciencia (Projects AGL2007-64567 and CTQ2008-05719/BQU) and Conselleria d'Educació de la Generalitat Valenciana (Project PROMETEO 2010-055).
Signal-to-noise ratio in reproducing kernel Hilbert spaces
2018
This paper introduces the kernel signal-to-noise ratio (kSNR) for different machine learning and signal processing applications}. The kSNR seeks to maximize the signal variance while minimizing the estimated noise variance explicitly in a reproducing kernel Hilbert space (rkHs). The kSNR gives rise to considering complex signal-to-noise relations beyond additive noise models, and can be seen as a useful signal-to-noise regularizer for feature extraction and dimensionality reduction. We show that the kSNR generalizes kernel PCA (and other spectral dimensionality reduction methods), least squares SVM, and kernel ridge regression to deal with cases where signal and noise cannot be assumed inde…
NMR spectroscopy evaluation of direct relationship between soils and molecular composition of red wines from Aglanico grapes
2010
1H NMR spectroscopy was employed to investigate the molecular quality of Aglianico red wines from the Campania region of Italy. The wines were obtained from three different Aglianico vineyards characterized by different microclimatic and pedological properties. In order to reach an objective evaluation of “terroir” influence on wine quality, grapes were subjected to the same winemaking procedures. The careful subtraction of water and ethanol signals from NMR spectra allowed to statistically recognize the metabolites to be employed in multivariate statistical methods: Principal Component Analysis (PCA), Discriminant Analysis (DA) and Hierarchical Clustering Analysis (HCA). The three wines we…
Untargeted metabolomics of fresh and heat treatment Tiger nut ( Cyperus esculentus L. ) milks reveals further insight into food quality and nutrition
2017
Tiger nut (Cyperus esculentus L.) is a crop traditionally grown in Valencia Region (Spain) and other temperate and tropical regions in the world, where its tubers are commonly consumed as tiger nut milk (horchata). Because of their nutritive potential and original taste, these products are beginning to spread internationally and, as consequence, analytical procedures to assess nutritional profiles, quality control issues are acquiring increasing relevance. The main objective of this study was to use an advance analytical method and chemometrics tools to determine if the ultra-high temperature (UHT) treatment necessary to extend the shelf life of tiger nut milk would affect the profile of nu…
Amylase production in submerged culture using principal component analysis
1989
Abstract The aim of our work was to study the production of α-amylase and glucoamylase in submerged culture by a species of Aspergillus niger screened for its high amylase activity. Statistical analysis was not used to optimize fermentation conditions but it was employed in a new approach to describe the culture behaviour. Principal component analysis (PCA) was applied to describe the influence of three agitation speeds on the rate of amylases synthesis and on the evolution of other indirect growth factors. The results of this analysis showed that the oxygen transfer at various agitation speeds affected the levels of enzymes, their production rates and the CO 2 production rate (indirect mea…