Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 415002
Title Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes
Author(s) Menendez, P.; Eilers, P.; Tikunov, Y.M.; Bovy, A.G.; Eeuwijk, F. van
Source Euphytica 183 (2012)3. - ISSN 0014-2336 - p. 379 - 387.
Department(s) WUR Plant Breeding
PRI BIOS Applied Metabolic Systems
Publication type Refereed Article in a scientific journal
Publication year 2012
Keyword(s) lycopersicon-esculentum - nonvolatile components - organoleptic quality - selection - flavor - volatiles - lasso - identification - cultivars - traits
Abstract The search for models which link tomato taste attributes to their metabolic profiling, is a main challenge within the breeding programs that aim to enhance tomato flavor. In this paper, we compared such models calculated by the traditional statistical approach, stepwise regression, with models obtained by the new generation of regression techniques, known as penalized regression or regularization methods. In addition, for penalized regression, different scenarios and various model selection criteria were discussed to conclude that classical crossvalidation, selects models with many superfluous variables whereas model selection criteria such as Bayesian information criterion, seem to be more suitable, when the goal is to find parsimonious models, to explain tomato taste attributes based on metabolic information. An exhaustive comparison of the discussed methodology was done for six sensory traits, showing that the most important covariates were identified by the stepwise regression as well as by some of the penalized regression methods, despite the general disagreement on the size of the regression coefficients between them. In particular, for stepwise regression the coefficients are inflated due to their high variance which is not the case with penalized regression, showing that this new methodology, can be an alternative to obtain more accurate models.
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