Identification of Protein Content in Corn Based on Partial Least Squares Regression

Applying the partial least squares regression methodology, the protein contents of corn measured by using near infrared at 10 000-4 000cm-1 wave band and the conventional biochemical method were compared.The near infrared spectra were firstly de-noised and smoothed for dimensionality reduction, and then, the data was used as the input and protein content as the output for the model establishment.The simulation results showed that the protein content of corn could be accurately predicted by the regression model.The predictive expression regression coefficients and the variable importance in projection obtained on the spectra at a certain wavelengths highly correlated with the protein content.The model appeared to provide a significant quantitative relationship between the biochemical and near infrared determinations on the protein content of corn.