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Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region

  • Kim Cluff
  • Govindarajan Konda Naganathan
  • Jeyamkondan SubbiahEmail author
  • Renfu Lu
  • Chris R. Calkins
  • Ashok Samal
Original Paper

Abstract

The objective of this research is to develop a non-destructive method for predicting cooked beef tenderness using optical scattering of light on fresh beef muscle tissue. A hyperspectral imaging system (λ = 496–1,036 nm) that consists of a CCD camera and an imaging spectrograph, was used to acquire beef steak images. The hyperspectral image consisted of 120 bands with spectral intervals of 4.54 nm. Sixty-one fresh beef steaks, including 44 strip loin and 17 tenderloin cuts, were collected. After imaging, the steaks were cooked and Warner-Bratzler shear (WBS) force values were collected as tenderness references. The optical scattering profiles were derived from the hyperspectral images and fitted to the modified Lorentzian function. Parameters, such as the peak height, full scattering width at half maximum (FWHM), and the slope around the FWHM were determined at each wavelength. Stepwise regression was used to identify 7 key wavelengths and parameters. The parameters were then used to predict the WBS scores. The model was able to predict WBS scores with an = 0.67. Optical scattering implemented with hyperspectral imaging shows limited success for predicting current status of tenderness in beef steak.

Keywords

Optical scattering Hyperspectral imaging Beef tenderness Modified Lorentzian function Warner-Bratzler shear force 

Notes

Acknowledgments

We acknowledge the technical support of Mr. Benjamin Bailey, Engineering Technician, and Dr. Matthew E. Doumit, of the Michigan State University Food Science and Human Nutrition, for helping in freezing and vacuum packaging the samples.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Kim Cluff
    • 1
  • Govindarajan Konda Naganathan
    • 1
  • Jeyamkondan Subbiah
    • 1
    Email author
  • Renfu Lu
    • 2
  • Chris R. Calkins
    • 3
  • Ashok Samal
    • 4
  1. 1.Biological Systems EngineeringUniversity of NebraskaLincolnUSA
  2. 2.U.S. Department of Agriculture Agricultural Research ServiceEast LansingUSA
  3. 3.A213 Animal SciencesUniversity of NebraskaLincolnUSA
  4. 4.Computer Science and EngineeringUniversity of NebraskaLincolnUSA

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