摘要
研究了Elman神经网络(反馈神经网络,Recurrent Network)在近红外光谱定量分析中的应用。以饲料样品为实验材料,采用Elman网络建立了饲料中苯丙氨酸(Phe)、赖氨酸(Lys)、酪氨酸(Tyr)和胱氨酸(Cys)四种氨基酸含量的近红外光谱定量分析模型。用偏最小二乘法(partial least squares,PLS)将原始数据压缩为主成分,取前3个主成分的12个吸收峰值输入Elman网络,网络中间层神经元个数为47。Elman网络模型对样品4个氨基酸含量的预测决定系数(r2)分别为0.960,0.981,0.979,0.952。表明所建Elman网络预测模型通过近红外光谱能够较准确预测饲料中苯丙氨酸、赖氨酸、酪氨酸和胱氨酸四种氨基酸的含量,为通过近红外光谱技术进行多组分定量分析提供了新思路。
The present paper introduces an application of near infrared spectroscopy（NIRS） multi-component quantitative analysis by building a kind of recurrent network（Elman）model. Elman prediction model for phenylalanine（Phe）, lysine（Lys）, tyrosine（Tyr） and cystine（Cys） in 45 feedstuff samples was established with good veracity, Twelve peak value data from 3 principal components straight forward compressed from the original data by PLS were taken as inputs of Elman, while 4 predictive targets as outputs. Forty seven nerve cells were taken as hidden nodes with the lowest error compared with taking 43 and 45 nerve cells. Its training iteration times was supposed to be 1 000. Predictive correlation coefficients by the model are 0. 960, 0. 981, 0.979 and 0. 952. The results show that Elman using in NIRS is a rapid, effective means for measuring Phe, Lys, Tyr and Cys in feedstuff powder, and can also be used in quantitative analysis of other samples.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2007年第12期2456-2459,共4页
Spectroscopy and Spectral Analysis
基金
教育部南昌大学食品科学重点实验室开放基金项目(NCU200404)
江西省星火计划项目(2005年)资助
