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Correction

Correction: Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs)

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Notice of Republication

This article was republished on July 9, 2015, to replace incorrect figures. The publisher apologizes for the error. Please download this article again to view the correct version.

Reference

  1. 1. Hernández Suárez M, Astray Dopazo G, Larios López D, Espinosa F (2015) Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs). PLoS ONE 10(6): e0128566. pmid:26075889

Citation: The PLOS ONE Staff (2015) Correction: Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs). PLoS ONE 10(7): e0134313. https://doi.org/10.1371/journal.pone.0134313

Published: July 28, 2015

Copyright: © 2015 The PLOS ONE Staff. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited