Journal
FOODS
Volume 10, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/foods10092228
Keywords
strawberry; total phenolic compounds; antioxidant activity; artificial neural networks (ANNs)
Categories
Funding
- Portuguese Foundation for Science and Technology (FCT) [UIDB/00681/2020]
- Polytechnic Institute of Viseu [CIDETS/2015/0004]
- CIDETS [CIDETS/2015/0004]
- CERNAS-IPV [UIDB/00681/2020]
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This research study focused on evaluating the total phenolic compounds and antioxidant activity of strawberries under different experimental extraction conditions using Artificial Neural Networks (ANNs). The results indicated that ANN is a potential method for estimating the targeted compounds and activity of strawberries, showcasing its effectiveness in predicting health benefits.
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 R-2 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.
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