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dc.contributor.author
Godoy, José Luis  
dc.contributor.author
Vega, Jorge Ruben  
dc.contributor.author
Marchetti, Jacinto Luis  
dc.date.available
2016-12-13T19:12:24Z  
dc.date.issued
2014-01  
dc.identifier.citation
Godoy, José Luis; Vega, Jorge Ruben; Marchetti, Jacinto Luis; Relationships between PCA and PLS-regression; Elsevier Science; Chemometrics And Intelligent Laboratory Systems; 130; 1-2014; 182-191  
dc.identifier.issn
0169-7439  
dc.identifier.uri
http://hdl.handle.net/11336/9285  
dc.description.abstract
This work aims at comparing several features of Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR), as techniques typically utilized for modeling, output prediction, and monitoring of multivariate processes. First, geometric properties of the decomposition induced by PLSR are described in relation to the PCA of the separated input and output data (X-PCA and Y-PCA, respectively). Then, analogies between the models derived with PLSR and YX-PCA (i.e., PCA of the joint input-output variables) are presented; and regarding to process monitoring applications, the specific PLSR and YX-PCA fault detection indices are compared. Numerical examples are used to illustrate the relationships between latent models, output predictive models, and fault detection indices. The three alternative approaches (PLSR, YX-PCA and Y-PCA plus X-PCA) are compared with regard to their use for statistical modeling. In particular, a case study is simulated and the results are used for enhancing the comprehension of the PLSR properties and for evaluating the discriminatory capacity of the fault detection indices based on the PLSR and YX-PCA modeling alternatives. Some recommendations are given in order to choose the more appropriate approach for a specific application: 1) PLSR and YX-PCA have similar capacity for fault detection, but PLSR is recommended for process monitoring because present a better diagnosing capability; 2) PLSR is more reliable for output prediction purposes (e.g., for soft sensor development); and 3) YX-PCA is recommended for the analysis of latent patterns imbedded in datasets.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier Science  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/  
dc.subject
Pls-Regression  
dc.subject
Pca  
dc.subject
Latent Models  
dc.subject
Prediction Models  
dc.subject
Fault Detection Indices  
dc.subject.classification
Otras Ingenierías y Tecnologías  
dc.subject.classification
Otras Ingenierías y Tecnologías  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
Relationships between PCA and PLS-regression  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2016-12-12T13:44:41Z  
dc.journal.volume
130  
dc.journal.pagination
182-191  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina. Universidad Tecnológica Nacional. Facultad Regional Paraná; Argentina  
dc.description.fil
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina. Universidad Tecnologica Nacional; Argentina  
dc.description.fil
Fil: Marchetti, Jacinto Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina  
dc.journal.title
Chemometrics And Intelligent Laboratory Systems  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.chemolab.2013.11.008  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743913002189