Predicting wheat kernels’ protein content by near infrared hyperspectral imaging

Authors

  • Yang Shuqin College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
  • He Dongjian College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
  • Ning Jifeng College of Information Engineering, Northwest A&F University, Yangling 712100, China

Keywords:

wheat kernels, protein, nondestructive prediction, near infrared hyperspectral imaging, partial least squares regression, radial basis function neural network

Abstract

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. Keywords: wheat kernels, protein, nondestructive prediction, near infrared hyperspectral imaging, partial least squares regression, radial basis function neural network DOI: 10.3965/j.ijabe.20160902.1701 Citation: Yang S Q, He D J, Ning J F. Predicting wheat kernels’ protein content by near infrared hyperspectral imaging. Int J Agric & Biol Eng, 2016; 9(2): 163-170.

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Published

2016-03-31

How to Cite

Shuqin, Y., Dongjian, H., & Jifeng, N. (2016). Predicting wheat kernels’ protein content by near infrared hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 9(2), 163–170. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1701

Issue

Vol. 9 No. 2 (2016): IJABE

Section

Agro-product and Food Processing Systems

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