| Article ID | Journal | Published Year | Pages | File Type | 
|---|---|---|---|---|
| 1181046 | Chemometrics and Intelligent Laboratory Systems | 2011 | 7 Pages | 
Abstract
												The multilevel multi-way technique turned out to be a much stronger tool for modeling differences between treatment groups than the ordinary method. The metabolites that contributed most to treatment differences were not only statistically, but also biologically relevant. The multilevel approach found the effects that were better interpretable, whereas the ordinary nPLS-DA failed to do so. The methodology that was described in this paper is not only limited to human intervention studies, but can be used also for studies with a similar data structure. The multilevel approach is able to investigate effects on all levels of variation of every well designed study, hence improving the interpretability of the results.
											Keywords
												PLSPLS-DANPLsOGTTGC–MSLC–MSOral glucose tolerance testinternal standardCross-validationPartial least squares discriminant analysisMulti-way analysisInterpretabilityPartial least squaresMulti-way partial least squaresliquid chromatography–mass spectroscopygas chromatography–mass spectroscopylatent variableMultilevel modeling
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											Authors
												Carina M. Rubingh, Marjan J. van Erk, Suzan Wopereis, Trinette van Vliet, Elwin R. Verheij, Nicole H.P. Cnubben, Ben van Ommen, Jan van der Greef, Henk F.J. Hendriks, Age K. Smilde, 
											