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)

Title
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)
Authors
Keywords
Tomatoes, Artificial neural networks, Fruit crops, Neurons, Forecasting, Neural networks, Fructoses, Glucose
Journal
PLoS One
Volume 10, Issue 6, Pages e0128566
Publisher
Public Library of Science (PLoS)
Online
2015-06-16
DOI
10.1371/journal.pone.0128566

Ask authors/readers for more resources

Reprint

Contact the author

Find Funding. Review Successful Grants.

Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.

Explore

Find the ideal target journal for your manuscript

Explore over 38,000 international journals covering a vast array of academic fields.

Search