Fusion of Spectra and Texture in Hyperspectral Imaging for Quantification of Nutritional Content in Alfalfa-Potato Pomace

Authors

  • Wenbin Guo College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China; Inner Mongolia Autonomous Region Engineering Research Center, Hohhot 010018, Inner Mongolia, China
  • Tianyu Shi College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China; Inner Mongolia Autonomous Region Engineering Research Center, Hohhot 010018, Inner Mongolia, China
  • Xiaowei Jin College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China; Inner Mongolia Autonomous Region Engineering Research Center, Hohhot 010018, Inner Mongolia, China
  • Xuhui Zhang College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China; Inner Mongolia Autonomous Region Engineering Research Center, Hohhot 010018, Inner Mongolia, China

Keywords:

Hyperspectral image technology, Near-infrared spectroscopy, Texture, Gray-level co-occurrence matrix, Crude protein, Starch

Abstract

Rapid and accurate detection of crude protein and starch content in alfalfa-potato pomace pellets is crucial for improving their processing and enhancing nutritional quality. In this study, hyperspectral images of alfalfa-potato pomace pellets in the near-infrared (NIR) range (900 to 1700 nm) were acquired. A support vector regression (SVR) model was developed by combining various spectral preprocessing methods and effective wavelength selection techniques. Textural features from the surface of the first principal component (PC1) image sample were also extracted using the gray-level co-occurrence matrix (GLCM) and fused with the spectral data, significantly improving the model’s prediction accuracy. The results indicated that the SNV-GB-COR-SVR model performed best in predicting crude protein content, with an R2p of 0.907 and an RMSEP of 0.5548, while the SNV-CARS-ENT-SVR model was most effective in predicting starch content, with an R2p of 0.7915 and an RMSEP of 1.3970.

Downloads

Published

2025-10-13

How to Cite

Guo , W., Shi, T., Jin, X., & Zhang , X. (2025). Fusion of Spectra and Texture in Hyperspectral Imaging for Quantification of Nutritional Content in Alfalfa-Potato Pomace. BioResources, 20(4), 10249–10262. Retrieved from https://ojs.bioresources.com/index.php/BRJ/article/view/24693

Issue

Vol. 20 No. 4 (2025)

Section

Research Article or Brief Communication