Application of Vis-NIR and SWIR spectroscopy for the segregation of bison muscles based on their color stability

Md Mahmudul Hasan, Muhammad Mudassir Arif Chaudhry, Chyngyz Erkinbaev, Jitendra Paliwal, Surendranath P. Suman, Argenis Rodas-Gonzalez

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The study objective was to investigate the potential for using visible near-infrared (Vis-NIR) and short wave infrared (SWIR) spectroscopy to segregate bison portions based on muscle types and storage periods. In the Vis-NIR range, the principal component analysis showed clear segregation of the muscles based on storage at retail display d 4 whereas the discrimination based on muscle type was better portrayed in the SWIR region. Furthermore, partial least squares discriminant analysis (PLS-DA) models classified muscles based on muscle type and storage in the Vis-NIR range with the classification accuracy of 97% for calibration and 86% for cross-validation. Finally, the PLS-regression models were developed for the successful prediction of a* value with an R2 of 0.88 (RMSEC: 1.57), 0.84 (RMSECV: 1.88), and 0.90 (RMSEP: 1.41), color score with an R2 of 0.96 (0.25), 0.95 (0.27), and 0.92 (0.32), and discoloration score with an R2 of 0.96 (0.47), 0.93 (0.63), and 0.93 (0.56) for calibration, cross-validation, and prediction, respectively.

Original languageEnglish
Article number108774
JournalMeat Science
Volume188
DOIs
StatePublished - Jun 2022

Bibliographical note

Funding Information:
The authors are grateful to the Natural Sciences and Engineering Research Council (NSERC Discovery Grants Program # RGPIN-2016-06006 ) for allocating the funds to complete this study. The credit for providing the infrastructural support goes to Canada Foundation for Innovation.

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • Classification
  • Meat color
  • Partial least squares discriminant analysis
  • Principal component analysis
  • Short wave infrared
  • Visible near-infrared

ASJC Scopus subject areas

  • Food Science

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