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Predicting beef tenderness using near-infrared spectroscopy.

S R Rust1, D M Price, J Subbiah

  • 1Oklahoma State University, Stillwater 74075, USA.

Journal of Animal Science
|October 4, 2007
PubMed
Summary
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Near-infrared (NIR) spectral reflectance accurately predicts beef tenderness in 14-day aged cooked beef. This technology allows for in-plant carcass sorting for branded beef programs, improving tenderness outcomes.

Area of Science:

  • Meat Science
  • Food Technology
  • Spectroscopy

Background:

  • Beef tenderness is a critical quality attribute influencing consumer satisfaction and purchasing decisions.
  • Accurate prediction of beef tenderness early in the supply chain can facilitate value-added branded beef programs.

Purpose of the Study:

  • To evaluate the accuracy of an on-line near-infrared (NIR) spectral reflectance system for predicting the tenderness of 14-day aged cooked beef.
  • To assess the system's capability to sort beef carcasses into tenderness outcome groups for branded beef programs.

Main Methods:

  • Multiple phases involving 292 (Phase I) and 476 (Phases II & III) beef carcasses (US Select and US Choice grades).
  • Longissimus lumborum (LL) muscle sections were aged for 14 days postmortem.

Related Experiment Videos

  • On-line NIR spectral scans were obtained, followed by slice shear force (SSF) analysis to determine tenderness.

    • Main Results:

    • The NIR system demonstrated accuracy in predicting beef tenderness, with low error rates (3.7% at 90% certification, 2.0% at 80% certification) for tough samples.
    • NIR-certified tender beef was significantly more tender (P < 0.05) than non-certified samples.
    • Sorting tough samples using NIR improved the overall population's average SSF by over 6.5 kg.

    Conclusions:

    • On-line NIR spectral reflectance is a viable tool for in-plant prediction of beef tenderness.
    • The NIR system can effectively sort beef carcasses into tenderness outcome groups, supporting guaranteed-tender branded beef initiatives.