Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat

Food Chemistry

Volumn 175, Issue , 2015, Pages 417-422

  Xiong, Zhenjie ^a   Sun, Da Wen ^a,b   Xie, Anguo ^a   Han, Zhong ^a   Wang, Lu ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

Chicken meat; Hydroxyproline; Hyperspectral imaging; Multivariate analysis; Non destructive

Indexed keywords

ANIMALS; FORECASTING; MEATS; MULTIVARIANT ANALYSIS; REGRESSION ANALYSIS; SPECTROSCOPY;

CHICKEN MEAT; HYDROXYPROLINE CONTENT; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGING; MULTI VARIATE ANALYSIS; NON DESTRUCTIVE; PARTIAL LEAST SQUARE REGRESSION; REGRESSION COEFFICIENT;

HYDROXYPROLINE;

HYDROXYPROLINE;

ARTICLE; CHICKEN MEAT; IMAGE DISPLAY; IMAGE PROCESSING; MODEL; NONHUMAN; PARTIAL LEAST SQUARE REGRESSION MODEL; REGRESSION ANALYSIS; ROOT MEAN ERROR SQUARE IN PREDICTION MODEL; SPECTROSCOPY; ALGORITHM; ANALYSIS; ANIMAL; CHEMICAL MODEL; CHICKEN; MEAT; MULTIMODAL IMAGING; NEAR INFRARED SPECTROSCOPY; PROCEDURES;

ALGORITHMS; ANIMALS; CHICKENS; HYDROXYPROLINE; MEAT; MODELS, CHEMICAL; MULTIMODAL IMAGING; SPECTROSCOPY, NEAR-INFRARED;

EID: 84917734896     PISSN: 03088146     EISSN: 18737072     Source Type: Journal    
DOI: 10.1016/j.foodchem.2014.11.161     Document Type: Article

References (48)

1. Aschenbrenner, M., Grammueller, E., Kulozik, U., & Foerst, P. (2014). The contribution of the inherent restricted mobility of glassy sugar matrices to the overall stability of freeze-dried bacteria determined by low-resolution solid-state H-1-NMR. Food and Bioprocess Technology, 7(4), 1012-1024.
2. Barbin, D. F., ElMasry, G., Sun, D.-W., & Allen, P. (2013). Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chemistry, 138(2-3), 1162-1171.
3. Castell-Palou, A., Rossello, C., Femenia, A., & Simal, S. (2013). Simultaneous quantification of fat and water content in cheese by TD-NMR. Food and Bioprocess Technology, 6(10), 2685-2694.
4. Chen, Q. S., Zhang, Y. H., Zhao, J. W., & Hui, Z. (2013). Nondestructive measurement of total volatile basic nitrogen (TVB-N) content in salted pork in jelly using a hyperspectral imaging technique combined with efficient hypercube processing algorithms. Analytical Methods, 5(22), 6382-6388.
5. Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sun, D.-W., & Menesatti, P. (2011). Shape analysis of agricultural products: A review of recent research advances and potential application to computer vision. Food and Bioprocess Technology, 4(5), 673-692.
6. Dai, Q., Cheng, J. H., Sun, D.-W., & Zeng, X. A. (2014). Potential of hyperspectral imaging for non-invasive determination of mechanical properties of prawn (Metapenaeus ensis). Journal of Food Engineering, 136, 64-72.
7. Delgado, A. E., & Sun, D.-W. (2002). Desorption isotherms for cooked and cured beef and pork. Journal of Food Engineering, 51(2), 163-170.
8. Delgado, A. E., Zheng, L., & Sun, D.-W. (2009). Influence of ultrasound on freezing rate of immersion-frozen apples. Food and Bioprocess Technology, 2 (3), 263-270.
9. ElMasry, G., Sun, D.-W., & Allen, P. (2011). Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Research International, 44(9), 2624-2633.
10. Elmasry, G., Barbin, D. F., Sun, D.-W., & Allen, P. (2012). Meat quality evaluation by hyperspectral imaging technique: an overview. Critical Reviews in Food Science and Nutrition, 52(8), 689-711.
11. ElMasry, G., Iqbal, A., Sun, D.-W., & Allen, P. (2011). Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system. Journal of Food Engineering, 103(3), 333-344.
12. ElMasry, G., Sun, D.-W., & Allen, P. (2012). Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food Engineering, 110(1), 127-140.
13. Feng, Y.-Z., & Sun, D.-W. (2013). Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. Talanta, 109, 74-83.
14. González-Martín, M. I., Bermejo, C. F., Hierro, J. M. H., & González, C. I. S. (2009). Determination of hydroxyproline in cured pork sausages and dry cured beef products by NIRS technology employing a fibre-optic probe. Food Control, 20(8), 752-755.
15. Herrero, A. M. (2008). Raman spectroscopy a promising technique for quality assessment of meat and fish: A review. Food Chemistry, 107(4), 1642-1651.
16. Jackman, P., Sun, D.-W., Du, C.-J., & Allen, P. (2008). Prediction of beef eating quality from colour, marbling and wavelet texture features. Meat Science, 80(4), 1273-1281.
17. Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2011). Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of Food Engineering, 104(3), 332-340.
18. Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2012a). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovative Food Science & Emerging Technologies, 16, 218-226.
19. Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2012b). Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Analytica Chimica Acta, 714, 57-67.
20. Kamruzzaman, M., ElMasry, G., Sun, D.-W., & Allen, P. (2013). Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry, 141(1), 389-396.
21. Kobayashi, K., Matsui, Y., Maebuchi, Y., Toyota, T., & Nakauchi, S. (2010). Near infrared spectroscopy and hyperspectral imaging for prediction and visualisation of fat and fatty acid content in intact raw beef cuts. Journal of near Infrared Spectroscopy, 18(5), 301-315.
22. Kolar, K. U. R. T. (1989). Colorimetric determination of hydroxyproline as measure of collagen content in meat and meat products: NMKL collaborative study. Journal-Association of Official Analytical Chemists, 73(1), 54-57.
23. Liu, D., Qu, J., Sun, D.-W., Pu, H., & Zeng, X.-A. (2013). Non-destructive prediction of salt contents and water activity of porcine meat slices by hyperspectral imaging in a salting process. Innovative Food Science & Emerging Technologies, 20, 316-323.
24. Liu, D., Sun, D.-W., Qu, J., Zeng, X.-A., Pu, H., & Ma, J. (2014). Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process. Food Chemistry, 152, 197-204.
25. Liu, D., Sun, D.-W., & Zeng, X.-A. (2014). Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food and Bioprocess Technology, 7(2), 307-323.
26. Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., & Blasco, J. (2013). Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food and Bioprocess Technology, 6(2), 530-541.
27. Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121-1142.
28. Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J., Saeys, W., & Nicolaï, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit - a review. Food and Bioprocess Technology, 5(2), 425-444.
29. Monziols, M., Collewet, G., Bonneau, M., Mariette, F., Davenel, A., & Kouba, M. (2006). Quantification of muscle, subcutaneous fat and intermuscular fat in pig carcasses and cuts by magnetic resonance imaging. Meat Science, 72(1), 146-154.
30. Ottani, V., Raspanti, M., & Ruggeri, A. (2001). Collagen structure and functional implications. Micron, 32(3), 251-260.
31. Prevolnik, M., Candek-Potokar, M., & Skorjanc, D. (2004). Ability of NIR spectroscopy to predict meat chemical composition and quality - A review. Czech Journal of Animal Science-UZPI (Czech Republic).
32. Salinas, Y., Ros-Lis, J. V., Vivancos, J.-L., Martínez-Máñez, R., Marcos, M. D., Aucejo, S., et al. (2012). Monitoring of chicken meat freshness by means of a colorimetric sensor array. Analyst, 137 (16), 3635-3643.
33. Santagapita, P., Laghi, L., Panarese, V., Tylewicz, U., Rocculi, P., & Dalla Rosa, M. (2013). Modification of transverse NMR relaxation times and water diffusion coefficients of kiwifruit pericarp tissue subjected to osmotic dehydration. Food and Bioprocess Technology, 6(6), 1434-1443.
34. Sivertsen, A. H., Heia, K., Hindberg, K., & Godtliebsen, F. (2012). Automatic nematode detection in cod fillets (Gadus morhua L.) by hyperspectral imaging. Journal of Food Engineering, 111(4), 675-681.
35. Sun, D.-W. (1999). Comparison and selection of EMC ERH isotherm equations for rice. Journal of Stored Products Research, 35(3), 249-264.
36. Sun, D.-W. (2004). Computer vision - an objective, rapid and non-contact quality evaluation tool for the food industry. Journal of Food Engineering, 61(1), 1-2.
37. Sun, D.-W., & Byrne, C. (1998). Selection of EMC/ERH isotherm equations for rapeseed. Journal of Agricultural Engineering Research, 69(4), 307-315.
38. Sun, D.-W., & Woods, J. L. (1997). Simulation of the heat and moisture transfer process during drying in deep grain beds. Drying Technology, 15(10), 2479-2508.
39. Valous, N. A., Mendoza, F., Sun, D.-W., & Allen, P. (2009). Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. Meat Science, 81(1), 132-141.
40. Wang, L. J., & Sun, D.-W. (2001). Rapid cooling of porous and moisture foods by using vacuum cooling technology. Trends in Food Science & Technology, 12(5-6), 174-184.
41. Woodcock, T., Fagan, C. C., O'Donnell, C. P., & Downey, G. (2008). Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity. Food and Bioprocess Technology, 1(2), 117-129.
42. Wu, D., & Sun, D.-W. (2013). Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta, 111, 39-46.
43. Wu, D., Chen, X., Cao, F., Sun, D.-W., He, Y., & Jiang, Y. (2014). Comparison of infrared spectroscopy and nuclear magnetic resonance techniques in tandem with multivariable selection for rapid determination of ω-3 polyunsaturated fatty acids in fish oil. Food and Bioprocess Technology, 7(6), 1555-1569.
44. Xiong, Z., Sun, D.-W., Dai, Q., Han, Z., Zeng, X.-A., & Wang, L. (2014). Application of visible hyperspectral imaging for prediction of springiness of fresh chicken meat. Food Analytical Methods, 1-12.
45. Xiong, Z., Sun, D.-W., Zeng, X.-A., & Xie, A. (2014). Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: A review. Journal of Food Engineering, 132, 1-13.
46. Xiong, Z., Xie, A., Sun, D.-W., Zeng, X.-A., & Liu, D. (2014). Applications of hyperspectral imaging in chicken meat safety and quality detection and evaluation: A review. Critical Reviews in Food Science and Nutrition (just-accepted).
47. Xu, S. Y., Chen, X. F., & Sun, D.-W. (2001). Preservation of kiwifruit coated with an edible film at ambient temperature. Journal of Food Engineering, 50(4), 211-216.
48. Zhu, F., Zhang, D., He, Y., Liu, F., & Sun, D.-W. (2013). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen-thawed fish fillets. Food and Bioprocess Technology, 6(10), 2931-2937.