Prediction of Rheological Properties of Flour From Physicochemical Properties Using Multiple Regression Techniques and Artificial Neuronal Networks

IF 2.7 3区 农林科学 Q3 ENGINEERING, CHEMICAL
Ali Cingöz, Sinan Nacar
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Abstract

This study has two main objectives: (i) to determine the physicochemical and rheological properties of different flours and (ii) to estimate the alveograph parameters obtained as a result of experimental studies. In this context, physicochemical (protein, ash, falling number, wet gluten, gluten index, Zeleny, and delayed sedimentation) and alveograph parameters (P, L, G, W, P/L, and IE) of 150 different bread and pastry flours were determined. Multiple regression analysis (MRA) and artificial neural network (ANN) methods were then used to predict alveograph results from this experimentally obtained data set. Root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe (NSEC), and relative error (RE) performance statistics were used to evaluate the CS prediction capabilities of the methods. It was found that the flours were in the range of 11.01%–13.82% protein, 325–403 s falling number, and 30–61 mL Zeleny and delayed sedimentation values. The ANN method showed better predictive performance than the regression-based method. W was the best estimated parameter in the ANN model. This was followed by G, L, Ie, P/L, and P values. Considering the RMSE value of the W parameter, it was observed that the ANN method provided an improvement of 5.16, 1.76, and 2.15 times compared to the regression method for the training, validation, and test sets, respectively.

Abstract Image

利用多重回归技术和人工神经元网络从理化特性预测面粉的流变特性
这项研究有两个主要目标:(i) 确定不同面粉的理化和流变特性;(ii) 估算实验研究获得的流变学参数。在此背景下,测定了 150 种不同面包和糕点面粉的理化(蛋白质、灰分、降落数值、湿面筋、面筋指数、Zeleny 和延迟沉降)和流变学参数(P、L、G、W、P/L 和 IE)。然后使用多元回归分析(MRA)和人工神经网络(ANN)方法,从实验获得的数据集中预测凹陷图结果。使用均方根误差 (RMSE)、平均绝对误差 (MAE)、纳什-萨特克利夫 (NSEC) 和相对误差 (RE) 性能统计来评估这两种方法的 CS 预测能力。结果发现,面粉的蛋白质含量在 11.01%-13.82% 之间,降落数值在 325-403 s 之间,泽勒尼和延迟沉降值在 30-61 mL 之间。与基于回归的方法相比,ANN 方法显示出更好的预测性能。W 是 ANN 模型中的最佳估计参数。其次是 G、L、Ie、P/L 和 P 值。考虑到 W 参数的 RMSE 值,在训练集、验证集和测试集上,与回归法相比,ANN 方法分别提高了 5.16 倍、1.76 倍和 2.15 倍。
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来源期刊
Journal of Food Process Engineering
Journal of Food Process Engineering 工程技术-工程:化工
CiteScore
5.70
自引率
10.00%
发文量
259
审稿时长
2 months
期刊介绍: This international research journal focuses on the engineering aspects of post-production handling, storage, processing, packaging, and distribution of food. Read by researchers, food and chemical engineers, and industry experts, this is the only international journal specifically devoted to the engineering aspects of food processing. Co-Editors M. Elena Castell-Perez and Rosana Moreira, both of Texas A&M University, welcome papers covering the best original research on applications of engineering principles and concepts to food and food processes.
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