采用伏安型电子舌评价低钠盐配方中的苦味

摘要

A voltammetric electronic tongue is used to evaluate the bitterness of eight kinds of low-sodium salt samples. The signal of sample is collected by voltammetric electronic tongue system.The data are pretreated by wavelet analysis. And then the formula of low-sodium salt is classified by principal component analysis and cluster analysis. The model for predicting the bitterness of low-sodium salt is established by RBF neural network. The results show that the retained energy and the number of zeros are 99.23% and 99.42% after wavelet compression. On the score chart of principal component, the low-sodium salt formula and the bitterness reference samples have obviously differences. The results of cluster analysis and principal component analysis are consistent, and correctly reflect the relationship of samples. The root mean square error (RMSE) of the bitterness prediction model based on RBF neural network is 1.48%. The prediction results are in agreement with the actual sensory evaluation results. This research has provided a new way for the evaluation of flavor characteristics of low-sodium salt.%采用伏安型电子舌对8种低钠盐配方样品进行苦味特性评价研究.使用电子舌系统采集样品信号,利用小波分析进行数据压缩预处理,结合主成分分析、聚类分析对低钠盐配方进行区分分类,利用径向基神经网络建立低钠盐苦味预测模型.结果表明:小波压缩后剩余能量和置零系数比分别为99.23%,99.42%;主成分得分图上,低钠盐配方与苦味参比样品差异明显;聚类分析结果与主成分分析结果相一致,正确反映了样品之间的亲疏关系;径向基神经网络建立的苦味预测模型均方根误差为1.48%,预测结果与实际感官评价结果相吻合.该研究为低钠盐的呈味特性评价提供了一种新的方法和途径.