Detection of Protein Content in Alfalfa Using Visible/ Near-Infrared Spectroscopy Technology

Authors

  • Jie Li College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China
  • Guifang Wu College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China
  • Fang Guo College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China
  • Lei Han College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China
  • Haowen Xiao Inner Mongolia Autonomous Region Agricultural and Pastoral Technology Extension Center, Hohhot, 010010, P.R. China
  • Yang Cao Inner Mongolia Autonomous Region Agricultural and Pastoral Technology Extension Center, Hohhot, 010010, P.R. China
  • Huihe Yang College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China
  • Shubin Yan College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. China

Keywords:

Quantitative detection, Near-infrared spectroscopy, Machine Learning, Protein content, Alfalfa hay

Abstract

In this study, a quantitative model was developed using near-infrared spectroscopy to analyze protein content in dried purple alfalfa, employing preprocessing methods (SG, SNV, MSC, FD) and variable selection algorithms (CARS, IRIV) to optimize spectra. Models using ELM, PLSR, SVM, and LSTM were tested; the MSC-CARS-PLSR-SVM model achieved the highest accuracy, with a calibration determination coefficient (R²) of 0.9982 and root mean square error (RMSE) of 0.1088, and a prediction R² of 0.9645 with RMSE of 0.5230, offering a precise and reliable method for protein content prediction.

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Published

2024-04-26

Issue

Vol. 19 No. 2 (2024)

Section

Research Article or Brief Communication