摘要
使用法国Astree电子舌对大豆分离蛋白的苦味进行分析研究。利用主成分分析(PCA)和判别因子分析(DFA)对采集到的味觉信息进行定性分析,基于偏最小二乘法和RBF神经网络建立苦味定量预测模型。结果表明:主成分分析和判别因子分析均可判别配方的苦味程度,RBF神经网络预测模型预测集的RMSE为0.010和0.007,偏最小二乘预测模型预测集的RMSE为0.035和0.093。表明采用RBF神经网络建立的预测模型预测效果更好,研究结果为大豆分离蛋白苦味评价体系提供了一种全新的方法。
The bitterness of the isolated soybean protein was analyzed using Astree electronic tongue. Principal component analysis (PCA) and discriminant factor analysis (DFA) were used for qualitative analysis of the acquired taste information. Based on partial least squares and RBF neural network, the quantitative prediction model of bitterness was established. The results showed that both principal component analysis and discriminant factor analysis could be used to determine the bitterness degree of the formula. RMSE of RBF neural network prediction model were 0.010 and 0.007, respectively;and RMSE of partial least square prediction model were 0.035 and 0.093, respectively. The results showed that the prediction model established by RBF neural network was very effective. The results also provided a new method for the bitterness evaluation system of the isolated soybean protein.
作者
芦建超
惠延波
胡晓利
布冠好
LU Jianchao;HUI Yanbo;HU Xiaoli;BU Guanha(School of Electrical Engineering;School of Food Science and Technology,Henan University of Technology,Zhengzhou 450001,China)
出处
《河南工业大学学报（自然科学版）》
CAS
北大核心
2019年第6期65-69,79,共6页
Journal of Henan University of Technology：Natural Science Edition
基金
河南省科技厅自然科学项目(182102210089)
关键词
大豆分离蛋白
电子舌
苦味
主成分分析
判别因子分析
偏最小二乘法
RBF
神经
网络
isolated soybean protein
electronic tongue
bitterness
principal component analysis
discriminant factor analysis
partial least squares
RBF neural network
