Online monitoring of red meat color using hyperspectral imaging

Mohammed Kamruzzaman, Yoshio Makino, Seiichi Oshita

Research output: Contribution to journalArticlepeer-review

Abstract

A hyperspectral imaging system in the spectral range of 400-1000 nm was tested to develop an online monitoring system for red meat (beef, lamb, and pork) color in the meat industry. Instead of selecting different sets of important wavelengths for beef, lamb, and pork, a set of feature wavelengths were selected using the successive projection algorithm for red meat colors (L*, a*, b) for convenient industrial application. Only six wavelengths (450, 460, 600, 620, 820, and 980 nm) were further chosen as predictive feature wavelengths for predicting L*, a*, and b* in red meat. Multiple linear regression models were then developed and predicted L*, a*, and b* with coefficients of determination (R2p) of 0.97, 0.84, and 0.82, and root mean square error of prediction of 1.72, 1.73, and 1.35, respectively. Finally, distribution maps of meat surface color were generated. The results indicated that hyperspectral imaging has the potential to be used for rapid assessment of meat color.

Original languageEnglish (US)
Pages (from-to)110-117
Number of pages8
JournalMeat Science
Volume116
DOIs
StatePublished - Jun 1 2016
Externally publishedYes

Keywords

  • Beef
  • Hyperspectral imaging
  • Image processing
  • Lamb
  • Multivariate analysis
  • Pork
  • Successive projections algorithm

ASJC Scopus subject areas

  • Food Science

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