Predicting wheat kernels' protein content by near infrared hyperspectral imaging

The objective of this study was to explore the potential of near infrared hyperspectral imaging combined with statistical regression models and neural networks for nondestructive prediction of protein content of wheat kernels. Seventy-nine samples from 11 breeds of wheat kernels were collected. The protein percentage of each sample measured by semimicro-Kjeldahl method was taken as the reference value. After comparing the prediction models of principal components regression (PCR) and partial least squares regression (PLSR) with various pretreatment methods, PLSR preprocessed by zero mean normalization (z score) function of MATLAB was found to obtain better prediction results than other regression models. Based on 10 latent variables of PLSR, the radial basis function (RBF) neural network was applied to improve the prediction, in which the coefficients of determination (R2) were greater than 0.92 for both the calibration set and validation set, while the corresponding RMSE values were 0.3496 and 0.4005, respectively. Therefore, hyperspectral imaging can provide a fast and non-destructive method for predicting the wheat kernels' protein content.