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作 者:杨涛涛 周星宇 钱善华 俞经虎 Yang Taotao;Zhou Xingyu;Qian Shanhua;Yu Jihu(School of Mechanical Engineering,Jiangnan University,Wuxi 214122,Jiangsu;Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and Technology,Wuxi 214122,Jiangsu)
机构地区:[1]江南大学机械工程学院,江苏无锡214122 [2]江苏省食品先进制造装备技术重点实验室,江苏无锡214122
出 处:《中国食品学报》2022年第7期238-247,共10页Journal of Chinese Institute Of Food Science and Technology
基 金:国家自然科学基金项目(51775244);江苏省先进食品制造装备与技术重点实验室项目(FMZ201901,FMZ201907)。
摘 要:为建立明胶软糖的口感预测模型,针对不同配比的90个琼脂-明胶复配软糖样品,分别进行质构测定和口感评价,采用模糊数学方法得出各样品的综合口感得分,以质构参数作为输入因子,口感综合得分作为输出因子,搭建神经网络模型。通过多模型训练对比,确定最优拓扑结构为6×8×1,并利用遗传算法优化原有模型,建立GA-BP神经网络预测模型。经试验样本数据训练后,结果显示:该预测模型具有较好的拟合度,全部数据的相关系数R均大于0.9,且没有出现过拟合状态。对于市售3种明胶软糖样品预测的口感误差均在±5%(最大4.11%)以内,可满足口感预测的目的。该模型具有良好的应用和参考价值,为明胶软糖品质改善提供新手段,也为其它食品口感的预测提供方法借鉴。In order to establish a prediction model for the taste of gelatin gummy,90 agar-gelatin fondant samples with different ratios were subjected to texture profile analysis(TPA)and taste evaluation experiments.Then the comprehensive taste score of each sample was obtained by fuzzy mathematical method,using the texture parameters as input factors and the comprehensive taste score as output factors to build a neural network model.Through multi-model training comparison,the optimal topology is determined as 6×8×1,and the original model is optimized by using genetic algorithm to establish a GA-BP neural network prediction model.After the training of the experimental sample data,the results showed that the prediction model had a good fit,the correlation coefficient R of all the data was greater than 0.9,and there was no overfitting state.Meanwhile,the predicted taste errors for the three commercially available gelatin gummy samples were all within±5%(maximum 4.11%),which could meet the purpose of taste prediction.The model has good application and reference value,which can provide a new means to improve the quality of gelatinous fondant and can also give methodological reference for predicting the taste of other foods.
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