Frontiers in Nutrition (Jul 2025)

Research on non-destructive detection of chilled meat quality based on hyperspectral technology combined with different data processing methods

  • Zeyu Xu,
  • Zeyu Xu,
  • Zeyu Xu,
  • Yu Han,
  • Yu Han,
  • Yu Han,
  • Shuai Chen,
  • Shuai Chen,
  • Shuai Chen,
  • Dianbo Zhao,
  • Dianbo Zhao,
  • Dianbo Zhao,
  • Huanli Yao,
  • Huanli Yao,
  • Huanli Yao,
  • Jiale Hao,
  • Junguang Li,
  • Junguang Li,
  • Junguang Li,
  • Ke Li,
  • Ke Li,
  • Ke Li,
  • Shengjie Li,
  • Yanhong Bai,
  • Yanhong Bai,
  • Yanhong Bai

DOI
https://doi.org/10.3389/fnut.2025.1623671
Journal volume & issue
Vol. 12

Abstract

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This study utilized hyperspectral technology in conjunction with chemometric methods for the non-destructive assessment of chilled meat quality. Average spectra were extracted from regions of interest within hyperspectral images and further optimized using seven preprocessing techniques: S-G, SNV, MSC, 1st DER, 2nd DER, S-G combined with SNV, and S-G combined with MSC. These optimized spectra were then incorporated into PLSR and BPNN models to predict TVB-N and TVC. The results demonstrated that the PLSR model employing S-G smoothing in combination with SNV preprocessing yielded optimal predictions for TVB-N (Correlation coefficient = 0.9631), while the integration of S-G smoothing with MSC preprocessing achieved the best prediction for TVC (Correlation coefficient = 0.9601). This methodology presents a robust technical solution for rapid, non-destructive evaluation of chilled meat quality, thereby highlighting the potential of hyperspectral technology for accurate meat quality monitoring through precise quantification of TVB-N and TVC.

Keywords