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. 2015 Jul 28;10(7):e0134313. doi: 10.1371/journal.pone.0134313

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)

The PLOS ONE Staff
PMCID: PMC4517855  PMID: 26218833

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. 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 doi:10.1371/journal.pone.0128566 [DOI] [PMC free article] [PubMed] [Google Scholar]

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