AUTHOR=Eftekhari Maliheh , Yadollahi Abbas , Ahmadi Hamed , Shojaeiyan Abdolali , Ayyari Mahdi 

TITLE=Development of an Artificial Neural Network as a Tool for Predicting the Targeted Phenolic Profile of Grapevine (Vitis vinifera) Foliar Wastes

JOURNAL=Frontiers in Plant Science

VOLUME=9

YEAR=2018

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.00837

DOI=10.3389/fpls.2018.00837

ISSN=1664-462X

ABSTRACT=<p>High performance liquid chromatography data related to the concentrations of 12 phenolic compounds in vegetative parts, measured at four sampling times were processed for developing prediction models, based on the cultivar, grapevine organ, growth stage, total flavonoid content (TFC), total reducing capacity (TRC), and total antioxidant activity (TAA). 12 Artificial neural network (ANN) models were developed with 79 input variables and different number of neurons in the hidden layer, for the prediction of 12 phenolics. The results confirmed that the developed ANN-models (<italic>R</italic><sup>2</sup> = 0.90 – 0.97) outperform the stepwise regression models (<italic>R</italic><sup>2</sup> = 0.05 – 0.78). Moreover, the sensitivity of the model outputs against each input variable was computed by using ANN and it was revealed that the key determinant of phenolic concentration was the source organ of the grapevine. The ANN prediction technique represents a promising approach to predict targeted phenolic levels in vegetative parts of the grapevine.</p>