Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs).

Iman Golpour,Raquel P F Guiné,Fernando Gonçalves,Paula M R Correia,Ana M Blanco-Marigorta,Ana Cristina Ferrão

doi:10.3390/foods10092228

Abstract

This research study focuses on the evaluation of the total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberries according to different experimental extraction conditions by applying the Artificial Neural Networks (ANNs) technique. The experimental data were applied to train ANNs using feed- and cascade-forward backpropagation models with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANN inputs, whereas the three variables of total phenolic compounds, DPPH and ABTS antioxidant activities were considered as ANN outputs. The results demonstrate that the best cascade- and feed-forward backpropagation topologies of ANNs for the prediction of total phenolic compounds and DPPH and ABTS antioxidant activity factors were the 3-9-1, 3-4-4-1 and 3-13-10-1 structures, with the training algorithms of trainlm, trainbr, trainlm and threshold functions of tansig-purelin, tansig-tansig-tansig and purelin-tansig-tansig, respectively. The best R2 values for the predication of total phenolic compounds and DPPH and ABTS antioxidant activity factors were 0.9806 (MSE = 0.0047), 0.9651 (MSE = 0.0035) and 0.9756 (MSE = 0.00286), respectively. According to the comparison of ANNs, the results showed that the cascade-forward backpropagation network showed better performance than the feed-forward backpropagation network for predicting the TPC, and the FFBP network, in predicting the DPPH and ABTS antioxidant activity factors, had more precision than the cascade-forward backpropagation network. The ANN technique is a potential method for estimating targeted total phenolic compounds and the antioxidant activity of strawberries.

Highlights

More combinations were performed for the artificial neural networks (ANNs) modelling, 18 combinations to be precise, the seven presented in
The results demonstrate that using the threshold function of Purelin in the output layer and the Tansig function in the hidden layer had better performance, reducing the ANN error function in the prediction of total phenolic compounds (TPC)
The results showed that the TPC, antioxidant activity (AOA) (DPPH) and AOA (ABTS) of strawberries could be predicted with a satisfactory accuracy of more than 0.94 for the training and testing subsets of data, which is the acceptable value for the developed system to be applicable in practice

Summary

Introduction

Strawberries (Fragaria ananassa), a member of the Rosaceae family, are one of the significant sources of phenolic compounds, along with antioxidant and antiproliferative activities of fruits. They are widely consumed due to their nutritional content and flavour [1,2]. The total phenolic compounds available in strawberries have an impact on their quality, contributing to organoleptic and sensorial properties and to health properties [3] Strawberries, because of these different health advantages in addition to their nutritional value, have seen increasing worldwide production and consumption and are known as the first most significant soft fruit species [4]. Useful polyphenols such as hydrolysable (ellagitannins and gallotannins), flavonols, anthocyanins and condensed tannins are present in strawberry fruits [5,6]

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