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首页> 外文期刊>Drying technology: An International Journal >Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice
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Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

机译：人工神经网络（ANN）和响应面方法（RSM）在预测石榴干汁品质参数中的比较

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摘要

Response surface methodology (RSM) is a frequently used method for empirical modeling and prediction in the processing of biological media. The artificial neural network (ANN) has recently grown to be one of the most efficient methods for empirical modeling and prediction, especially for nonlinear systems. This article presents comparative studies between an ANN and RSM in the modeling and prediction of quality parameters of spray-dried pomegranate juice. In this study, the effects of the carrier type, carrier concentration, and concentration of crystalline cellulose in a pomegranate juice spray-drying process were investigated on five quality parameters-drying yield, solubility, color change, total anthocya-nin content, and antioxidant activity-using RSM and ANN methods. A central composite rotatable experimental design (CCRD) and a feed-forward multilayered perceptron (MLP) ANN trained using back-propagation algorithms for three independent variables were developed to predict the five outputs. The final selected ANN model (3-10-8-5) was compared to the RSM model for its modeling and predictive abilities. The predictive abilities of both the ANN and RSM were compared using a separate dataset of 18 unseen experiments based on RMSE (root mean square error), MAE (mean absolute error), and R~2 (correlation coefficient) for each output parameter. The results indicate the superiority of a properly trained ANN in capturing the nonlinear behavior of the system and the simultaneous prediction of five outputs.
机译：响应面方法（RSM）是生物介质处理中经验建模和预测的常用方法。人工神经网络（ANN）最近已成为经验建模和预测（尤其是非线性系统）的最有效方法之一。本文介绍了人工神经网络和RSM在喷雾干燥石榴汁质量参数的建模和预测中的比较研究。在这项研究中，研究了石榴汁喷雾干燥过程中载体类型，载体浓度和结晶纤维素浓度对五个质量参数的影响：干燥产量，溶解度，颜色变化，总蒽醌含量和抗氧化剂使用RSM和ANN方法进行活动。开发了中央复合可旋转实验设计（CCRD）和使用反向传播算法训练的前馈多层感知器（MLP）ANN，用于三个独立变量，以预测五个输出。将最终选择的ANN模型（3-10-8-5）与RSM模型进行了建模和预测，并进行了比较。分别使用RMSE（均方根误差），MAE（平均绝对误差）和R〜2（相关系数）对每个输出参数使用18个未见实验的单独数据集比较了ANN和RSM的预测能力。结果表明，经过适当训练的人工神经网络在捕获系统的非线性行为和同时预测五个输出方面的优越性。

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