Journal
JOURNAL OF FOOD ENGINEERING
Volume 104, Issue 3, Pages 332-340Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2010.12.024
Keywords
NIR hyperspectral imaging; Lamb; Charollais; Classification; Semitendinosus; Longissimus dorsi; Psoas Major; Near-infrared; Principal component analysis; Linear discriminant analysis
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Funding
- Irish Government Department of Agriculture, Fisheries and Food under the Food Institutional Research Measure (FIRM)
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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. (C) 2010 Elsevier Ltd. All rights reserved.
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