作　　者： ;

机构地区： 北京理工大学珠海学院

出　　处： 《中国调味品》 2019年第12期144-147,154,共5页

摘　　要： 基于近红外光谱技术,分别采用马氏距离聚类分析法与自组织映射神经网络两种模式识别方法,构建了茶油与掺有橄榄果籽油、花生油的掺伪油的定性判别模型。面对与茶油脂肪酸组成较为相似的复杂掺伪体系,马氏距离聚类分析法和自组织映射神经网络的预测准确率分别为83.33%和95.33%,经偏最小二乘法处理后,自组织映射神经网络建模参数为:前11个主成分的84个吸收峰数据作为输入向量,竞争层结构为[30×5],训练步数744步时,模型预测精度良好。 Based on near infrared spectroscopy,the qualitative discriminant models of camellia oil and adulterated oil mixed with olive seed oil and peanut oil are established by using Markov distance clustering analysis and self-organizing mapping neural network.Facing the complex adulteration system that is similar to the fatty acid composition of tea oil,the prediction accuracy of Markov distance clustering analysis and self-organizing mapping neural network is 83.33%and 95.33%respectively.After partial least squares processing,the modeling parameters of self-organizing mapping neural network are as follows:84 absorption peaks of the first 11 principal components are transported as data.The prediction accuracy of the model is good when the competition layer structure is[30×5]and the training steps is 744.

关 键 词： 自组织映射神经网络 近红外光谱 茶油 掺伪油

领　　域： []