Lightweight deep learning algorithm for real-time wheat flour quality detection via NIR spectroscopy.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
PMID: 39733712

Abstract

Wheat flour quality, determined by factors such as protein and moisture content, is crucial in food production. Traditional methods for analyzing these parameters, though precise, are time-consuming and impractical for large-scale operations. This study presents a lightweight convolutional neural network designed for real-time wheat flour quality monitoring using near-infrared spectroscopy. The model incorporates Ghost bottlenecks, external attention modules, and the Kolmogorov-Arnold network to enhance feature extraction and improve prediction accuracy. Testing results demonstrate high predictive performance with R values of 0.9653 (RMSE: 0.2886 g/100 g, RPD: 5.8981) for protein and 0.9683 (RMSE: 0.3061 g/100 g, RPD: 5.1046) for moisture content. The model's robustness across diverse samples and its suitability for online applications make it a promising tool for efficient and non-destructive quality control in the food industry.

Authors

  • Yu Yang
    Department of Obstetrics & Gynecology, the First Affiliated Hospital of Xi'an Jiaotong University, Xian, Shaanxi, China.
  • Rumeng Sun
    College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
  • Hongyan Li
    Department of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, China.
  • Yao Qin
    Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou 450001, China; Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making, Henan University of Technology, Zhengzhou 450001, China; Henan Grain Big Data Analysis and Application Engineering Research Center (Henan University of Technology), Zhengzhou 450001, China; College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
  • Qinghui Zhang
    Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou 450001, China; Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making, Henan University of Technology, Zhengzhou 450001, China; Henan Grain Big Data Analysis and Application Engineering Research Center (Henan University of Technology), Zhengzhou 450001, China; College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
  • Pengtao Lv
    Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou 450001, China; Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making, Henan University of Technology, Zhengzhou 450001, China; Henan Grain Big Data Analysis and Application Engineering Research Center (Henan University of Technology), Zhengzhou 450001, China; College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
  • Quan Pan

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