Hyperspectral imaging for real-time monitoring of water holding capacity in red meat

LWT

Volumn 66, Issue , 2016, Pages 685-691

  Kamruzzaman, Mohammed ^a,b   Makino, Yoshio ^a   Oshita, Seiichi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b BANGLADESH AGRICULTURAL UNIVERSITY   (Bangladesh)

Author keywords

Hyperspectral imaging; Multispectral imaging; Multivariate analysis; Red meat; Water holding capacity

Indexed keywords

LEAST SQUARES APPROXIMATIONS; MEATS; MULTIVARIANT ANALYSIS; PIXELS; SPECTROSCOPY; SUPPORT VECTOR MACHINES; SUPPORT VECTOR REGRESSION;

CONVENTIONAL SPECTROSCOPY; IMAGE PROCESSING ALGORITHM; LEAST SQUARE SUPPORT VECTOR MACHINES; MULTI VARIATE ANALYSIS; MULTISPECTRAL IMAGING; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); RED MEATS; WATER HOLDING CAPACITY;

HYPERSPECTRAL IMAGING;

EID: 84950110640     PISSN: 00236438     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.lwt.2015.11.021     Document Type: Article

References (32)

1. Barbin D., ElMasry G., Sun D.-W., Allen P. Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Analytica Chimica Acta 2012, 719:30-42.
2. Barbin D., ElMasry G., Sun D.-W., Allen P. Non-destructive determination of chemical composition in intact and minced pork by near-infrared hyperspectral imaging. Food Chemistry 2012, 138:1162-1171.
3. Berzaghi P., Riovanto R. Near infrared spectroscopy in animal science production: principles and applications. Italian Journal of Animal Science 2009, 8:39-62.
4. Burger J., Gowen A. Data handling in hyperspectral image analysis. Chemometrics and Intelligent Laboratory Systems 2011, 108:13-22.
5. Cozzolino D., Murray I. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT - Food Science & Technology 2004, 37:447-452.
6. ElMasry G., Kamruzzaman M., Sun D.-W., Allen P. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products, a review. Critical Review in Food Science & Nutrition 2012, 52:999-1023.
7. ElMasry G., Sun D.-W., Allen P. Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Research International. 2011, 44:2624-2633.
8. ElMasry G., Sun D.-W., Allen P. Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. Journal of Food Engineering 2012, 117:235-246.
9. He H.-J., Sun D.-W., Wu D. Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis. Food Research International 2014, 62:476-483.
10. Honikel K.O. Reference methods for the assessment of physical characteristics of meat. Meat Science 1998, 49:447-457.
11. Hoving-Bolink A.H., Vedder H.W., Merks J.W.M., de Klein W.J.H., Reimert H.G.M., Frankhuizen R., et al. Perspective of NIRS measurements early post mortem for prediction of pork quality. Meat Science 2005, 69:417-423.
12. Iqbal A., Sun D.-W., Allen P. Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system. Journal of Food Engineering 2013, 117:42-51.
13. Kamruzzaman M., Barbin D., ElMasry G., Sun D.-W., Allen P. Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat. Innovative Food Science & Emerging Technologies 2012, 16:316-325.
14. Kamruzzaman M., ElMasry G., Sun D.-W., Allen P. Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of Food Engineering 2011, 104:332-340.
15. Kamruzzaman M., ElMasry G., Sun D.-W., Allen P. Prediction of some quality attributes of lamb meat using near infrared hyperspectral imaging and multivariate analysis. Analytica Chimica Acta 2012, 714:57-67.
16. Kamruzzaman M., ElMasry G., Sun D.-W., Allen P. Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovative Food Science & Emerging Technologies 2012, 16:218-226.
17. Kamruzzaman M., ElMasry G., Sun D.-W., Allen P. Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry 2013, 141:389-396.
18. Kamruzzaman M., Makino Y., Oshita S. Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: a review. Analytica Chimica Acta 2015, 853:19-29.
19. Kamruzzaman M., Makino Y., Oshita S. Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. Journal of Food Engineering 2016, 170:8-15. http://dx.doi.org/10.1016/j.jfoodeng.2015.08.02.
20. Kamruzzaman M., Makino Y., Oshita S., et al. Assessment of visible near-infrared hyperspectral imaging as a tool for detection of horsemeat adulteration in minced beef. Food and Bioprocess Technology 2015, 8:1054-1062.
21. Kamruzzaman M., Sun D.-W., ElMasry G., Allen P. Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis. Talanta 2013, 103:130-136.
22. Mamani-Linares L.W., Gallo C., Alomar D. Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Science 2012, 90:378-385.
23. Malley D.F., Yesmin L., Eilers R.G. Rapid analysis of hog manure and manure-amended soils using near infrared spectroscopy. Soil Science Society of America Journal 2002, 66:1677-1686.
24. Park B., Yoon S.-C., Windham W., Lawrence K., Kim M., Chao K. Line-scan hyperspectral imaging for real-time in-line poultry fecal detection. Sensing & Instrumentation in Food Quality & Safety 2011, 5:25-32.
25. Pedersen D.K., Morel S., Andersen H.J., Engelsen S.B. Early prediction of water-holding capacity in meat by multivariate vibrational spectroscopy. Meat Science 2003, 65:581-592.
26. Prevolnik M., Čandek-Potokar M., Škorjanc D. Predicting pork waterholding capacity with NIR spectroscopy in relation to different reference methods. Journal of Food Engineering 2010, 98:347-352.
27. Pu H., Kamruzzaman M., Sun D.-W. Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends in Food Science & Technology 2015, 45:86-104.
28. Qin J., Chao K., Kim M.S., Lu R., Burks T.F. Hyperspectral and multispectral imaging for evaluating food safety and quality-a review. Journal of Food Engineering 2013, 118:157-171.
29. Rossel R.A.V., Taylor H.J., McBratney A.B. Multivariate calibration of hyperspectral gamma-ray energy spectra for proximal soil sensing. European Journal Soil Science 2007, 58:343-353.
30. Suykens J.A., Vandewalle J., De Moor B. Optimal control by least squares support vector machines. Neural Networks 2001, 14:23-35.
31. Tao F., Peng Y., Li Y., Chao K., Dhakal S. Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique. Meat Science 2012, 90:851-857.
32. Wu D., Sun D.-W. Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta 2013, 111:39-46.