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Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis.
Anal Chim Acta. 2012 Feb 10; 714:57-67.AC

Abstract

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 900-1700 nm. 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.

Authors+Show Affiliations

Food Refrigeration and Computerised Food Technology (FRCFT) Group, School of Biosystems Engineering, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

22244137

Citation

Kamruzzaman, Mohammed, et al. "Prediction of some Quality Attributes of Lamb Meat Using Near-infrared Hyperspectral Imaging and Multivariate Analysis." Analytica Chimica Acta, vol. 714, 2012, pp. 57-67.
Kamruzzaman M, ElMasry G, Sun DW, et al. Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Anal Chim Acta. 2012;714:57-67.
Kamruzzaman, M., ElMasry, G., Sun, D. W., & Allen, P. (2012). Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Analytica Chimica Acta, 714, 57-67. https://doi.org/10.1016/j.aca.2011.11.037
Kamruzzaman M, et al. Prediction of some Quality Attributes of Lamb Meat Using Near-infrared Hyperspectral Imaging and Multivariate Analysis. Anal Chim Acta. 2012 Feb 10;714:57-67. PubMed PMID: 22244137.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. AU - Kamruzzaman,Mohammed, AU - ElMasry,Gamal, AU - Sun,Da-Wen, AU - Allen,Paul, Y1 - 2011/11/25/ PY - 2011/08/04/received PY - 2011/11/15/revised PY - 2011/11/17/accepted PY - 2012/1/17/entrez PY - 2012/1/17/pubmed PY - 2012/5/4/medline SP - 57 EP - 67 JF - Analytica chimica acta JO - Anal Chim Acta VL - 714 N2 - 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 900-1700 nm. 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. SN - 1873-4324 UR - https://www.unboundmedicine.com/medline/citation/22244137/Prediction_of_some_quality_attributes_of_lamb_meat_using_near_infrared_hyperspectral_imaging_and_multivariate_analysis_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0003-2670(11)01580-7 DB - PRIME DP - Unbound Medicine ER -