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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis
Analytica Chimica Acta, Volume 714, Year 2012
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Description
The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900-1700nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R 2) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat. © 2011 Elsevier B.V.
Authors & Co-Authors
Kamruzzaman, M.
Ireland, Dublin
National University of Ireland
ElMasry, Gamal
Ireland, Dublin
National University of Ireland
Sun, Da-Wen
Ireland, Dublin
National University of Ireland
Allen, Paul
Ireland, Carlow
Teagasc - Irish Agriculture and Food Development Authority
Statistics
Citations: 290
Authors: 4
Affiliations: 2
Identifiers
Doi:
10.1016/j.aca.2011.11.037
ISSN:
18734324