PMC · DOI:10.1371/journal.pone.0134313·July 28, 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)
Abstract
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpectroscopy and Chemometric Analyses · Advanced Chemical Sensor Technologies · Phytochemicals and Antioxidant Activities
Full text
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.
Bibliography1
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1Hernández 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 (AN Ns). P Lo S ONE 10(6): e 0128566 doi:10.1371/journal.pone.0128566 2607588910.1371/journal.pone.0128566 PMC 4467870 · doi ↗ · pubmed ↗