Three dimensional chemometric analyses of hyperspectral images for beef tenderness forecasting

Govindarajan Konda Naganathan, Kim Cluff, Ashok Samal, Chris R. Calkins, David D. Jones, George E. Meyer, Jeyamkondan Subbiah

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

A prototype on-line hyperspectral imaging system (λ = 400-1000 nm) was developed and used to acquire images of exposed ribeye muscle on hanging beef carcasses (n = 274) at 2-day postmortem in a commercial beef packing plant. After image acquisition, a strip steak was cut from each carcass and vacuum packaged. After aging for 14 days, the steaks were cooked and Warner-Bratzler shear force values were collected as a measure of tenderness. Four different principal component analysis-based dimensionality reduction methods were implemented to reduce information redundancy in beef hyperspectral images. Textural features extracted from the 2-day hyperspectral images were modeled using Fisher's linear discriminant (FLD), support vector machines (SVM), and decision tree (DT) models to predict 14-day aged, cooked beef tenderness. Based on a true validation procedure using 101 samples, the FLD model yielded a tender certification accuracy of 86.7%. In addition, wavelengths corresponding to myoglobin and its derivatives (541, 577, and 635 nm), beef aging (541, 577, 635, 756, and 980 nm), protein (910 nm), fat (928 nm), and water (739, 756, and 988 nm) were identified.

Original languageEnglish (US)
Pages (from-to)309-320
Number of pages12
JournalJournal of Food Engineering
Volume169
DOIs
StatePublished - Jan 1 2016

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Keywords

  • Decision tree
  • Fisher's linear discriminant model
  • Instrument grading
  • Partial least squares analysis
  • Principal component analysis
  • Support vector machines

ASJC Scopus subject areas

  • Food Science

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