Optical scattering with hyperspectral imaging to classify longissimus dorsi muscle based on beef tenderness using multivariate modeling

Kim Cluff, Govindarajan Konda Naganathan, Jeyamkondan Subbiah, Ashok Samal, Chris R. Calkins

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

The objective of this study was to develop a non-destructive method for classifying cooked-beef tenderness using hyperspectral imaging of optical scattering on fresh beef muscle tissue. A hyperspectral imaging system (λ = 922-1739. nm) was used to collect hyperspectral scattering images of the longissimus dorsi muscle (n = 472). A modified Lorentzian function was used to fit optical scattering profiles at each wavelength. After removing highly correlated parameters extracted from the Lorentzian function, principal component analysis was performed. Four principal component scores were used in a linear discriminant model to classify beef tenderness. In a validation data set (n = 118 samples), the model was able to successfully classify tough and tender samples with 83.3% and 75.0% accuracies, respectively. Presence of fat flecks did not have a significant effect on beef tenderness classification accuracy. The results demonstrate that hyperspectral imaging of optical scattering is a viable technology for beef tenderness classification.

Original languageEnglish (US)
Pages (from-to)42-50
Number of pages9
JournalMeat Science
Volume95
Issue number1
DOIs
StatePublished - Sep 2013

Keywords

  • Beef tenderness
  • Computer vision
  • Linear discriminant analysis
  • Lorentzian distribution function
  • Optical scattering
  • Principal component analysis

ASJC Scopus subject areas

  • Food Science

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