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Publisert 2016

Publikasjonsdetaljer

Tidsskrift : Chemometrics and Intelligent Laboratory Systems , vol. 156 , p. 89–101 , 2016

Utgiver : Elsevier

Internasjonale standardnummer :
Trykt : 0169-7439
Elektronisk : 1873-3239

Publikasjonstype : Vitenskapelig artikkel

Bidragsytere : Biancolillo, Alessandra; Liland, Kristian Hovde; Måge, Ingrid; Næs, Tormod; Bro, Rasmus

Har du spørsmål om noe vedrørende publikasjonen, kan du kontakte Nofimas bibliotekleder.

Kjetil Aune
Bibliotekleder
kjetil.aune@nofima.no

Sammendrag

The focus of the present paper is to propose and discuss different procedures for performing variable selection in a multi-block regression context. In particular, the focus is on two multi-block regression methods: Multi-Block Partial Least Squares (MB-PLS) and Sequential and Orthogonalized Partial Least Squares (SO-PLS) regression. A small simulation study for regular PLS regression was conducted in order to select the most promising methods to investigate further in the multi-block context. The combinations of three variable selection methods with MB-PLS and SO-PLS are examined in detail. These methods are Variable Importance in Projection (VIP) Selectivity Ratio (SR) and forward selection. In this paper we focus on both prediction ability and interpretation. The different approaches are tested on three types of data: one sensory data set, one spectroscopic (Raman) data set and a number of simulated multi-block data sets.

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