Application of NIR hyperspectral imaging for discrimination of lamb muscles

Mohammed Kamruzzaman, Gamal Elmasry, Da Wen Sun, Paul Allen

Research output: Contribution to journalArticlepeer-review


The potential of near-infrared (NIR) hyperspectral imaging system coupled with multivariate analysis was evaluated for discriminating three types of lamb muscles. Samples from semitendinosus (ST), Longissimus dorsi (LD) and Psoas Major (PM) of Charollais breed were imaged by a pushbroom hyperspectral imaging system with a spectral range of 900-1700 nm. Principal component analysis (PCA) was used for dimensionality reduction, wavelength selection and visualizing hyperspectral data. Six optimal wavelengths (934, 974, 1074, 1141, 1211 and 1308 nm) were selected from the eigenvector plot of PCA and then used for discrimination purpose. The results showed that it was possible to discriminate lamb muscles with overall accuracy of 100% using NIR hyperspectral reflectance spectra. An image processing algorithm was also developed for visualizing classification results in a pixel-wise scale with a high overall accuracy.

Original languageEnglish (US)
Pages (from-to)332-340
Number of pages9
JournalJournal of Food Engineering
Issue number3
StatePublished - Jun 2011
Externally publishedYes


  • Charollais
  • Classification
  • Lamb
  • Linear discriminant analysis
  • Longissimus dorsi
  • Near-infrared
  • NIR hyperspectral imaging
  • Principal component analysis
  • Psoas Major
  • Semitendinosus

ASJC Scopus subject areas

  • Food Science


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