A prototype on-line AOTF hyperspectral image acquisition system for tenderness assessment of beef carcasses

Govindarajan Konda Naganathan, Kim Cluff, Ashok Samal, Chris R. Calkins, David D. Jones, Carol L. Lorenzen, Jeyamkondan Subbiah

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

A prototype on-line acousto-optic tunable filter (AOTF)-based hyperspectral image acquisition system (λ = 450-900 nm) was developed for tenderness assessment of beef carcasses. Hyperspectral images of ribeye muscle on stationary hanging beef carcasses (n = 338) at 2-day postmortem were acquired in commercial beef slaughter or packing plants. After image acquisition, a strip steak was cut from each carcass, vacuum packaged, aged for 14 days, cooked, and slice shear force tenderness scores were collected by an independent lab. Beef hyperspectral images were mosaicked together and principal component (PC) analysis was conducted to reduce the spectral dimension. Six different textural feature sets were extracted from the PC images and used in Fisher's linear discriminant model to classify beef samples into two tenderness categories: tender and tough. The pooled feature model performed better than the other models with a tender certification accuracy of 92.9% and 87.8% in cross-validation and third-party true validation, respectively. Two additional metrics namely overall accuracy and a custom defined metric called accuracy index, were used to compare the tenderness prediction models.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalJournal of Food Engineering
Volume154
DOIs
StatePublished - Jun 2015

Keywords

  • Acousto-optic tunable filter
  • Beef grading
  • Discriminant model
  • Feature selection
  • Principal component analysis
  • Textural features

ASJC Scopus subject areas

  • Food Science

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