期刊论文

Chen, Jiwang(jiwangchen1970@126.com)

中文

食品科学

1002-6630

2019

40

16

249-255

10.7506/spkx1002-6630-20180607-093

国家自然科学基金面上项目（31471612）； 国家大宗淡水鱼产业技术体系建设专项（CARS-45）； 湖北省农业成果转化基金项目（NZZ2018000014）；

本校为第一机构

为实现对油炸外裹糊鱼块的丙烯酰胺含量的预测,采用响应面试验设计收集数据,建立以黄原胶和大豆纤维复配比例、外裹糊鱼块干燥时间、大豆油品质、油炸温度、油炸时间为输入值,油炸外裹糊鱼块的丙烯酰胺含量为输出值的反向传播人工神经网络(back propagation artificial neural network,BP-ANN),预测外裹糊鱼块深度油炸过程丙烯酰胺含量的变化,并用训练集拟合,测试集评估模型的预测能力。结果显示,黄原胶和大豆纤维复配比例、外裹糊鱼块干燥时间、油炸温度、油炸时间对油炸外裹糊鱼块的丙烯酰胺含量均有显著影响,大豆油品质对油炸外裹糊鱼块中丙烯酰胺含量影响不显著。训练后的BP-ANN模型的相关系...

In this paper, the prediction of acrylamide contents in fried battered and breaded fish nuggets (BBFNs) was performed through the combination of response surface methodology (RSM) and back propagation artificial neural network(BP-ANN). RSM was utilized to collect the experimental data and a BP-ANN model was established to predict the changes in acrylamide content in BBFNs during deep-fat frying, where the ratio of xanthan gum to soybean fiber, drying time of BBFNs, soybean oil quality, frying temperature and time were considered as the input while acrylamide content was regarded as the output....