Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes |
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Authors: | Patricia Menéndez Paul Eilers Yury Tikunov Arnaud Bovy Fred van Eeuwijk |
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Institution: | (1) Biometris - Applied Statistics, Wageningen University, P.O. Box 100, 6700 AC, Wageningen, The Netherlands;(2) Centre for BioSystems Genetics, P.O. Box 98, 6700 AB, Wageningen, The Netherlands;(3) Department of Biostatistics, Erasmus Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands;(4) Plant Research International, Wageningen University, 6700 AA, Wageningen, The Netherlands |
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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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