A general deep learning model for predicting and classifying pea protein content via visible and near-infrared spectroscopy.

Journal: Food chemistry
PMID: 40049135

Abstract

Rapid and accurate detection of pea protein content is crucial for breeding and ensuring food quality. This study introduces the PeaNet model, which employs an improved convolutional neural network architecture to predict and classify pea protein content. The model was developed using 156 visible and near-infrared spectral datasets from 52 varieties cultivated under varied conditions. The data were preprocessed with Savitzky-Golay smoothing and multiplicative scatter correction to improve quality. The results revealed that the model achieved an R of 0.84 for predicting protein content and a classification accuracy of 85.33 % on the test set. On an independent validation set comprising different pea varieties, the model maintained an R above 0.80 and a classification accuracy of 83.33 %. It significantly outperformed traditional machine learning models and conventional deep learning architectures. This study introduces a universal, accurate, and efficient method for detecting pea protein content, thereby advancing food nutrition assessment and quality control.

Authors

  • Tianpu Xiao
    College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
  • Chunji Xie
    College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key Laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
  • Li Yang
    Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Xiantao He
    College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
  • Liangju Wang
    Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA.
  • Dongxing Zhang
    College of Engineering, China Agricultural University, Beijing 100083, China; The Soil-Machine-Plant key laboratory of the Ministry of Agriculture of China, Beijing 100083, China.
  • Tao Cui
    Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Kailiang Zhang
    Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
  • Hongsheng Li
  • Jiaqi Dong
    Department of Obstetrics, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai, China.

Keywords

External Resources

Popular Topics
Recent Journals