Study on the multicomponent quantitative analysis using near infrared spectroscopy based on building Elman model

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 1000. 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.