Grupo de Ingeniería Estadística Multivariante GIEM
Loading...
Date established
City
Country
ID
Description
86 results
Publication Search Results
Now showing 1 - 10 of 86
- PublicationDetermination of phytoplankton composition using absorption spectra(Elsevier, 2009) Martínez Guijarro, Mª Remedios; Romero Gil, Inmaculada; Pachés Giner, María Aguas Vivas; González del Rio Rams, Julio; Martí Insa, Carmen Mª; GIL SEGUÍ, GERMA; Ferrer Riquelme, Alberto José; FERRER, J.; Dpto. de Ingeniería Hidráulica y Medio Ambiente; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Instituto Universitario de Ingeniería del Agua y del Medio Ambiente; Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Generalitat ValencianaCharacterisation of phytoplankton communities in aquatic ecosystems is a costly task in terms of time, material and human resources. The general objective of this paper is not to replace microscopic counts but to complement them, by fine-tuning a technique using absorption spectra measurements that reduces the above-mentioned costs. Therefore, the objective proposed in this paper is to assess the possibility of achieving a qualitative determination of phytoplankton communities by classes, and also a quantitative estimation of the number of phytoplankton cells within each of these classes, using spectrophotometric determination. Samples were taken in three areas of the Spanish Mediterranean coast. These areas correspond to estuary systems that are influenced by both continental waters and Mediterranean Sea waters. 139 Samples were taken in 7–8 stations per area, at different depths in each station. In each sample, the absorption spectrum and the phytoplankton classes (Bacyllariophyceae (diatoms), Cryptophyceae, Clorophyceae, Chrysophyceae, Prasynophyceae, Prymnesophyceae, Euglenophyceae, Cyanophyceae, Dynophyceae and the Synechococcus sp.) were determined. Data were analysed by means of the Partial Least Squares (PLS) multivariate statistical technique. The absorbances obtained between 400 and 750 nm were used as the independent variable and the cell/l of each phytoplankton class was used as the dependent variable, thereby obtaining models which relate the absorbance of the sample extract to the phytoplankton present in it. Good results were obtained for diatoms (Bacillarophyceae), Chlorophyceae and Cryptophyceae. © 2009 Elsevier B.V. All rights reserved.
- PublicationDifferential expression in RNA-seq: A matter of depth(Cold Spring Harbor Laboratory Press, 2011-09-08) Tarazona Campos, Sonia; García-Alcalde, Fernando; Dopazo, Joaquín; Ferrer Riquelme, Alberto José; Conesa, Ana; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Escuela Técnica Superior de Ingeniería Informática; Grupo de Ingeniería Estadística Multivariante GIEM; Generalitat Valenciana; Ministerio de Ciencia e InnovaciónNext-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach-NOISeq-that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication. © 2011 by Cold Spring Harbor Laboratory Press.
- PublicationStatistical Process Control based on Multivariate Image Analysis: A new proposal for monitoring and defect detection(Elsevier, 2014-12-04) Prats Montalbán, José Manuel; Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Ministerio de Ciencia e InnovaciónThe monitoring, fault detection and visualization of defects are a strategic issue for product quality. This paper presents a novel methodology based on the integration of textural Multivariate image analysis (MIA) and multivariate statistical process control (MSPC) for process monitoring. The proposed approach combines MIA and p-control charts, as well as T2 and RSS images for defect location and visualization. Simulated images of steel plates are used to illustrate the monitoring performance of it. Both approaches are also applied on real clover images.
- PublicationMulti-synchro: a novel approach for batch synchronization in scenarios of multiple asynchronisms(Wiley, 2014-05) González Martínez, José María; de Noord, Onno; Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Ministerio de Ciencia e InnovaciónBatch synchronization has been widely misunderstood as being only needed when variable trajectories have uneven length. Batch data are actually considered not synchronized when the key process events do not occur at the same point of process evolution, irrespective of whether the batch duration is the same for all batches or not. Additionally, a single synchronization procedure is usually applied to all batches without taking into account the nature of asynchronism of each batch, and the presence of abnormalities. This strategy may distort the original trajectories and decrease the signal-to-noise ratio, affecting the subsequent multivariate analyses. The approach proposed in this paper, named multisynchro, overcomes these pitfalls in scenarios of multiple asynchronisms. The different types of asynchronisms are effectively detected by using the warping information derived from synchronization. Each set of batch trajectories is synchronized by appropriate synchronization procedures, which are automatically selected based on the nature of asynchronisms present in data. The novel approach also includes a procedure that performs abnormality detection and batch synchronization in an iterative manner. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisiae cultivation are used to illustrate the performance of the proposed approach in a context of multiple asynchronisms.
- PublicationMultivariate six sigma: A key improvement strategy in industry 4.0(Taylor & Francis, 2021-10-02) Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; AGENCIA ESTATAL DE INVESTIGACION[EN] This article aims to generate a reflection on the changes that need to be made in the Six Sigma strategy so that it continues to be a successful improvement strategy capable of facing the new challenges derived from Industry 4.0.
- PublicationCalibration transfer between NIR spectrometers: new proposals and a comparative study(John Wiley & Sons, 2017) Folch-Fortuny, Abel; Vitale, Raffaele; De Noord, Onno E.; Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Ministerio de Economía y Competitividad; Shell Global Solutions International B.V.[EN] Calibration transfer between near-infrared (NIR) spectrometers is a subtle issue in chemometrics and process industry. In fact, as even very similar instruments may generate strongly different spectral responses, regression models developed on a first NIR system can rarely be used with spectra collected by a second apparatus. In this work, two novel methods to perform calibration transfer between NIR spectrometers are proposed. Both of them permit to exploit the specific relationships between instruments for imputing new unmeasured spectra, which will be then resorted to for building an improved predictive model, suitable for the analysis of future incoming data. Specifically, the two approaches are based on trimmed scores regression and joint-Y partial least squares regression, respectively. The performance of these novel strategies will be assessed and compared to that of well-established techniques such as maximum likelihood principal component analysis and piecewise direct standardisation in two real case studies.
- PublicationKernel-Partial Least Squares regression coupled to pseudo-sample trajectories for the analysis of mixture designs of experiments(Elsevier, 2018) Vitale, Raffaele; Palací-López, Daniel Gonzalo; Kerkenaar, Harmen; Postma, GJ; Buydens, Lutgarde; Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Shell Global Solutions International B.V.; Ministerio de Economía, Industria y Competitividad[EN] This article explores the potential of Kernel-Partial Least Squares (K-PLS) regression for the analysis of data proceeding from mixture designs of experiments. Gower's idea of pseudo-sample trajectories is exploited for interpretation purposes. The results show that, when the datasets under study are affected by severe nonlinearities and comprise few observations, the proposed approach can represent a feasible lternative to classical methodologies (i.e. Scheffe polynomial fitting by means of Ordinary Least Squares - OLS - and Cox polynomial fitting by means of Partial Least Squares - PLS). Furthermore, a way of recovering the parameters of a Scheffe model (provided that it holds and has the same complexity as the K-PLS one) from the trend of the aforementioned pseudo-sample trajectories is illustrated via a simulated case-study.
- PublicationEvaluation of the effect of chance correlations on variable selection using Partial Least Squares -Discriminant Analysis(Elsevier, 2013-11-15) Kuligowski, Julia; Pérez Guaita, David; Escobar, Javier; Guardia, Miguel de la; Vento, Máximo; Ferrer Riquelme, Alberto José; Quintás, Guillermo; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Ministerio de Economía y Competitividad; Ministerio de Ciencia e Innovación; Ministerio de Ciencia e Innovación; Universitat de ValènciaVariable subset selection is often mandatory in high throughput metabolomics and proteomics. However, depending on the variable to sample ratio there is a significant susceptibility of variable selection towards chance correlations. The evaluation of the predictive capabilities of PLSDA models estimated by cross-validation after feature selection provides overly optimistic results if the selection is performed on the entire set and no external validation set is available. In this work, a simulation of the statistical null hypothesis is proposed to test whether the discrimination capability of a PLSDA model after variable selection estimated by cross-validation is statistically higher than that attributed to the presence of chance correlations in the original data set. Statistical significance of PLSDA CV-figures of merit obtained after variable selection is expressed by means of p-values calculated by using a permutation test that included the variable selection step. The reliability of the approach is evaluated using two variable selection methods on experimental and simulated data sets with and without induced class differences. The proposed approach can be considered as a useful tool when no external validation set is available and provides a straightforward way to evaluate differences between variable selection methods.
- PublicationPCA model building with missing data: New proposals and a comparative study(Elsevier, 2015-08-15) Folch-Fortuny, Abel; ARTEAGA MORENO, FRANCISCO JAVIER; Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEM; Ministerio de Ciencia e Innovación; Ministerio de Economía y Competitividad[EN] This paper introduces new methods for building principal component analysis (PCA) models with missing data: projection to the model plane (PMP), known data regression (KDR), KDR with principal component regression (PCR), KDR with partial least squares regression (PLS) and trimmed scores regression (TSR). These methods are adapted from their PCA model exploitation version to deal with the more general problem of PCA model building when the training set has missing values. A comparative study is carried out comparing these new methods with the standard ones, such as the modified nonlinear iterative partial least squares (NIPALS), the it- erative algorithm (IA), the data augmentation method (DA) and the nonlinear programming approach (NLP). The performance is assessed using the mean squared prediction error of the reconstructed matrix and the cosines between the actual principal components and the ones extracted by each method. Four data sets, two simulated and two real ones, with several percentages of missing data, are used to perform the comparison. Guardar / Salir Siguiente >
- PublicationA kernel-based approach for fault diagnosis in batch processes(Wiley, 2014-08) Vitale, R.; de Noord, O. E.; Ferrer Riquelme, Alberto José; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Grupo de Ingeniería Estadística Multivariante GIEMThis article explores the potential of kernel-based techniques for discriminating on-specification and off-specification batch runs, combining kernel-partial least squares discriminant analysis and three common approaches to analyze batch data by means of bilinear models: landmark features extraction, batchwise unfolding, and variablewise unfolding. Gower s idea of pseudo-sample projection is exploited to recover the contribution of the initial variables to the final model and visualize those having the highest discriminant power. The results show that the proposed approach provides an efficient fault discrimination and enables a correct identification of the discriminant variables in the considered case studies.