Identifying Meat from Grazing or Feedlot Yaks Using Visible and Near-infrared Spectroscopy with Chemometrics

Yuchao Liu, Yang Xiang, Wu Sun, Allan Degen, Huan Xu, Yayu Huang, Rongzhen Zhong, Lizhuang Hao

Research output: Contribution to journalArticlepeer-review


The quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5−year−old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight, and dressing percentage were greater in the feedlot than in grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with 100% discrimination in the 400–2500 nm band. The spectral preprocessing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways.

Original languageEnglish
Article number100295
JournalJournal of Food Protection
Issue number7
StatePublished - 1 Jul 2024


  • Farming system
  • Partial least squares discriminant analysis (PLS-DA)
  • Product traceability
  • Quality of yak meat
  • Soft independent modeling of class analogies (SIMCA)

ASJC Scopus subject areas

  • Food Science
  • Microbiology


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