Golpour Iman, Ferrão Ana Cristina, Gonçalves Fernando, Correia Paula M R, Blanco-Marigorta Ana M, Guiné Raquel P F
Department of Mechanical Engineering of Biosystems, Urmia University, Urmia P.O. Box 5756151818, Iran.
CERNAS Research Centre, Department of Food Industry, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal.
Foods. 2021 Sep 20;10(9):2228. doi: 10.3390/foods10092228.
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 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.
本研究通过应用人工神经网络（ANNs）技术，根据不同的实验提取条件，对草莓中的总酚类化合物（TPC）和抗氧化活性（AOA）进行评估。实验数据被用于训练ANNs，采用带有Levenberg-Marquardt（LM）和贝叶斯正则化（BR）算法的前馈和级联前馈反向传播模型。三个自变量（溶剂浓度、体积/质量比和提取时间）用作ANN的输入，而总酚类化合物、DPPH和ABTS抗氧化活性这三个变量被视为ANN的输出。结果表明，用于预测总酚类化合物以及DPPH和ABTS抗氧化活性因子的ANNs的最佳级联和前馈反向传播拓扑结构分别是3-9-1、3-4-4-1和3-13-10-1结构，训练算法分别为trainlm、trainbr、trainlm，阈值函数分别为tansig-purelin、tansig-tansig-tansig和purelin-tansig-tansig。预测总酚类化合物以及DPPH和ABTS抗氧化活性因子的最佳R值分别为0.9806（均方误差 = 0.0047）、0.9651（均方误差 = 0.0035）和0.9756（均方误差 = 0.00286）。根据ANNs的比较结果，对于预测TPC，级联前馈反向传播网络的性能优于前馈反向传播网络；而对于预测DPPH和ABTS抗氧化活性因子，前馈反向传播网络比级联前馈反向传播网络更精确。ANN技术是估算草莓中目标总酚类化合物和抗氧化活性的一种潜在方法。
