Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef

Journal of Food Engineering

Volumn 110, Issue 1, 2012, Pages 127-140

  Elmasry, Gamal ^a,c   Sun, Da Wen ^a   Allen, Paul ^b  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

^b ASHTOWN FOOD RESEARCH CENTRE   (Ireland)

^c SUEZ CANAL UNIVERSITY   (Egypt)

Author keywords

Beef; Colour; Hyperspectral imaging; Imaging spectroscopy; Meat; pH; Tenderness

Indexed keywords

CONTACT METHODS; CROSS VALIDATION; HIGH DIMENSIONALITY; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGING; HYPERSPECTRAL IMAGING SYSTEMS; IMAGE PROCESSING ALGORITHM; IMAGING SPECTROSCOPY; MEAT PRODUCTS; NEAR INFRARED REGION; NEAR-INFRARED HYPERSPECTRAL IMAGING; NON DESTRUCTIVE; NON-DESTRUCTIVE PREDICTION; NONCONTACT MEASUREMENTS; PARTIAL LEAST SQUARE REGRESSION; POTENTIAL TECHNIQUES; PREDICTING MODELS; QUALITY ATTRIBUTES; QUALITY MEASUREMENTS; ROOT MEAN SQUARE ERRORS; SPECTRAL DATA; SPECTRAL SIGNATURE; SURFACE COLOURS; TENDERNESS; WEIGHTED REGRESSION;

BEEF; COLOR; DIGITAL IMAGE STORAGE; FORECASTING; IMAGE PROCESSING; IMAGING SYSTEMS; INFRARED DEVICES; LEAST SQUARES APPROXIMATIONS; MEAN SQUARE ERROR; PH;

MEATS;

EID: 84855866573     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2011.11.028     Document Type: Article

References (62)

1. D. Alomar, C. Gallo, M. Castañeda, and R. Fuchslocher Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS) Meat Science 63 2003 441 450
2. S. Andrés, I. Murray, E.A. Navajas, A.V. Fisher, N.R. Lambe, and L. Bünger Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy Meat Science 76 3 2007 509 516 (Pubitemid 46621078)
3. J.R. Andersen, C. Borggaard, A.J. Rasmussen, and L.P. Houmøller Optical measurements of pH in meat Meat Science 53 2 1999 135 141
4. K.E. Belk, J.A. Scanga, A.M. Wyle, D.M. Wulf, J.D. Tatum, J.O. Reagan, and G.C. Smith The use of video image analysis and instrumentation to predict beef palatability Proceeding Reciprocal Meat Conference 53 2000 10 15
5. M.B. Bowling, D.J. Vote, K.E. Belk, J.A. Scanga, J.D. Tatum, and G.C. Smith Using reflectance spectroscopy to predict beef tenderness Meat Science 82 2009 1 5
6. J. Brøndum, L. Munck, P. Henckel, A. Karlsson, E. Tornberg, and S.B. Engelsen Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy Meat Science 55 2 2000 177 185
7. C.E. Byrne, G. Downey, D.J. Troy, and D.J. Buckley Non-destructive prediction of selected quality attributes of beef by near-infrared reflectance spectroscopy between 750 and 1098 nm Meat Science 49 4 1998 399 409 (Pubitemid 128179709)
8. H. Cen, and Y. He Theory and application of near infrared reflectance spectroscopy in determination of food quality Trends in Food Science & Technology 18 2007 72 83 (Pubitemid 46136904)
9. K. Cluff, G.K. Naganathan, J. Subbiah, R. Lu, C.R. Calkins, and A. Samal Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region Sensing and Instrumentation for Food Quality and Safety 2 2008 189 196
10. D. Cozzolino, and I. Murray Identification of animal meat muscles by visible and near infrared reflectance spectroscopy LWT-Food Science and Technology 37 4 2004 447 452 (Pubitemid 38504106)
11. D. Cozzolino, D. De Mattos, and D. Vaz Martins Visible/near infrared reflectance spectroscopy for predicting composition and tracing system of production of beef muscle Animal Science 74 2002 477 484 (Pubitemid 34853672)
12. S. Cubero, N. Aleixos, E. Moltó, J. Gómez-Sanchis, and J. Blasco Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables Food and Bioprocess Technology 4 4 2011 487 504
13. J.L. Damez, and S. Clerjon Meat quality assessment using biophysical methods related to meat structure Meat Science 80 1 2008 132 149
14. E.M. Desmond, T.A. Kenny, P. Ward, and D.-W. Sun Effect of rapid and conventional cooling methods on the quality of cooked ham joints Meat Science 56 3 2000 271 277
15. G. ElMasry, and D.-W. Sun Meat quality assessment using a hyperspectral imaging system Da-Wen Sun, Hyperspectral Imaging for Food Quality Analysis and Control 2010 Academic Press/Elsevier San Diego, California, USA 273 294
16. G. ElMasry, A. Nassar, N. Wang, and C. Vigneault Spectral methods for measuring quality changes of fresh fruits and vegetables Stewart Postharvest Review 4 4 2008 1 13
17. G. ElMasry, D.-W. Sun, and P. Allen Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging Food Research International 44 9 2004 2624 2633
18. G. ElMasry, N. Wang, A. ElSayed, and M. Ngadi Hyperspectral imaging for non-destructive determination of some quality attributes for strawberry Journal of Food Engineering 81 1 2007 98 107 (Pubitemid 46178468)
19. G. ElMasry, N. Wang, and C. Vigneault Detecting chilling injury in red delicious apple using hyperspectral imaging and neural networks Postharvest Biology and Technology 52 1 2009 1 8
20. A.G. Frenich, D. Jouan-Rimbaud, D.L. Massart, S. Kuttatharmmakul, M.M. Galera, and J.L.M. Vidal Wavelength selection method for multicomponent spectrophotometric determinations using partial least squares Analyst 120 1995 2787 2792
21. Y. Garini, I.T. Young, and G. McNamara Spectral imaging, principles and applications Cytometry Part A 69A 2006 735 747
22. G.H. Geesink, F.H. Schreutelkamp, R. Frankhuizen, H.W. Vedder, N.M. Faber, R.W. Kranen, and M.A. Gerritzen Prediction of pork quality attributes from near infrared reflectance spectra Meat Science 65 1 2003 661 668 (Pubitemid 36624685)
23. N. Heigl, C.H. Petter, M. Lieb, G.K. Bonn, and C.W. Huck Near-infrared reflection spectroscopy and partial least squares regression for determining the total carbon coverage of silica packings for liquid chromatography Vibrational Spectroscopy 49 2009 155 161
24. A.N. Herrero Raman spectroscopy a promising technique for quality assessment of meat and fish: a review Food Chemistry 107 4 2008 1642 1651
25. K.I. Hildrum, B.N. Nilsen, M. Mielnik, and T. Næs Prediction of sensory characteristics of beef by near-infrared spectroscopy Meat Science 38 1 1994 67 80
26. D.L. Hopkins, E. Safari, J.M. Thompson, and C.R. Smith Video image analysis in the Australian meat industry - precision and accuracy of predicting lean meat yield in lamb carcasses Meat Science 67 2004 269 274 (Pubitemid 38087901)
27. H. Huang, H. Yu, H. Xu, and Y. Ying Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review Journal of Food Engineering 87 2008 303 313
28. U. Indahl, and T. Næs A variable selection strategy for supervised classification with continuous spectroscopic data Journal of Chemometrics 18 1 2004 53 61
29. M. Keskin, R.B. Dodd, Y.J. Han, and A. Khalilian Assessing nitrogen content of golf course turfgrass clippings using spectral reflectance Applied Engineering in Agriculture 20 6 2004 851 860 (Pubitemid 40307879)
30. G.A. Konstantinos, and M.G. Athanasia Application of nearinfrared reflectance spectroscopy in the determination of major components in taramosalata Journal of Food Engineering 63 2004 199 207
31. B. Leroy, S. Lambotte, O. Dotreppe, H. Lecocq, L. Istasse, and A. Clinquart Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra Meat Science 66 1 2003 45 54
32. Y. Liu, Y.-R. Chen, and Y. Ozaki Two-dimensional visible/near-infrared correlation spectroscopy study of thermal treatment of chicken meats Journal of Agricultural and Food Chemistry 48 2000 901 908 (Pubitemid 30194026)
33. Y. Liu, B.G. Lyon, W.R. Windham, C.E. Realini, T.D.D. Pringle, and S. Duckett Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy: a feasibility study Meat Science 65 2003 1107 1115 (Pubitemid 36880280)
34. D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O.L. García-Navarrete, and J. Blasco Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment Food and Bioprocess Technology 2011 10.1007/s11947-011-0725-1
35. D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, and J. Blasco Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks Food and Bioprocess Technology 1 2011 10.1007/s11947-011-0737-x
36. K. McDonald, and D.-W. Sun Effect of evacuation rate on the vacuum cooling process of a cooked beef product Journal of Food Engineering 48 3 2001 195 202 (Pubitemid 32160693)
37. K. McDonald, D.-W. Sun, and T. Kenny The effect of injection level on the quality of a rapid vacuum cooled cooked beef product Journal of Food Engineering 47 2 2001 139 147 (Pubitemid 32018015)
38. P. Menesatti, A. Zanella, S. D'Andrea, C. Costa, G. Paglia, and F. Pallottino Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples Food and Bioprocess Technology 2 3 2009 308 314
39. T. Næs, and K.I. Hildrum Comparison of multivariate calibration and discriminant analysis in evaluating NIR spectroscopy for determination of meat tenderness Applied Spectroscopy 51 3 1997 350 357 (Pubitemid 127662664)
40. T. Næs, T. Isaksson, T. Fearn, and T. Davies A User-friendly Guide to Multivariate Calibration and Classification 2002 NIR Publications Chichester, UK
41. G.K. Naganathan, L.M. Grimes, J. Subbiah, C.R. Calkins, A. Samal, and G.E. Meyer Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction Sensing and Instrumentation for Food Quality and Safety 2 2008 178 188
42. G.K. Naganathan, L.M. Grimes, J. Subbiah, C.R. Calkins, A. Samal, and G.E. Meyer Visible/near-infrared hyperspectral imaging for beef tenderness prediction Computers and Electronics in Agriculture 64 2008 225 233
43. S. Osborne, R. Jordan, and R. Kunnemeyer Methods of wavelength selection for partial least squares Analyst 122 1997 1531 1537 (Pubitemid 28010633)
44. M. Prevolnik, M. Čandek-Potokar, and D. Škorjanc Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods Journal of Food Engineering 98 3 2010 347 352
45. N. Prieto, S. Andrés, F.J. Giráldez, A.R. Mantecón, and P. Lavín Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples Meat Science 79 2008 692 699
46. N. Prieto, S. Andrés, F.J. Giráldez, A.R. Mantecón, and P. Lavín Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples Meat Science 74 2006 487 496 (Pubitemid 44177699)
47. N. Prieto, S. Andrés, F.J. Giráldez, A.R. Mantecón, and P. Lavín Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples Meat Science 74 3 2006 487 496 (Pubitemid 44177699)
48. N. Prieto, R. Roehe, P. Lavín, G. Batten, and S. Andrés Application of near infrared reflectance spectroscopy to predict meat and meat products quality: a review Meat Science 83 2 2009 175 186
49. J. Qiao, N. Wang, M.O. Ngadi, A. Gunenc, M. Monroy, C. Gariépy, and S.O. Prasher Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique Meat Science 76 1 2007 1 8
50. R. Rødbotten, B.N. Nilsen, and K.I. Hildrum Prediction of beef quality attributes from early post mortem near infrared reflectance spectra Food Chemistry 69 2000 427 436 (Pubitemid 30155765)
51. F. Ruiz de Huidobro, E. Miguel, E. Onega, and B. Blázquez Changes in meat quality characteristics of bovine meat during the first 6 days post-mortem Meat Science 65 2003 1439 1446 (Pubitemid 37032369)
52. S.R. Rust, D.M. Price, J. Subbiah, G. Kranzler, G.G. Hilton, D.L. Vanoverbeke, and J.B. Morgan Predicting beef tenderness using near-infrared spectroscopy Journal of Animal Science 86 2008 211 219
53. W.S. Sepúlveda, M.T. Maza, and L. Pardos Aspects of quality related to the consumption and production of lamb meat consumers versus producers Meat Science 87 2011 366 372
54. S.D. Shackelford, T.L. Wheeler, and M. Koohmaraie On-line classification of US Select beef carcasses for longissimus tenderness using visible and near-infrared reflectance spectroscopy Meat Science 69 2005 409 415 (Pubitemid 40079250)
55. S.D. Shackelford, T.L. Wheeler, M.K. Meade, J.O. Reagan, B.L. Byrnes, and M. Koohmaraie Consumer impressions of tender select beef Journal of Animal Science 79 10 2001 2605 2614 (Pubitemid 33721489)
56. H. Shin, N.H. Reyes, A.L. Barczak, and C.S. Chan Colour object classification using the fusion of visible and near-infrared spectra Trends in Artificial Intelligence Lecture Notes in Computer Science vol. 6230 2010 Springer-Verlag Heidelberg pp. 498-509
57. G. Tøgersen, T. Isaksson, B.N. Nilsen, E.A. Bakker, and K.I. Hildrum On-line NIR analysis of fat, water and protein in industrial scale ground meat batches Meat Science 51 1 1999 97 102 (Pubitemid 129556914)
58. T.N. Tran, R. Wehrens, and L.M.C. Buydens Clustering multispectral images: a tutorial Chemometrics and Intelligent Laboratory Systems 77 1 2005 3 17 (Pubitemid 40720088)
59. D.J. Troy, and J.P. Kerry Consumer perception and the role of science in the meat industry Meat Science 86 2010 214 226
60. D.J. Vote, K.E. Belk, J.D. Tatum, J.A. Scanga, and G.C. Smith Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module Journal of Animal Science 81 2003 457 465 (Pubitemid 38583278)
61. D. Wu, Y. He, S. Feng, and D.-W. Sun Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM Journal of Food Engineering 84 1 2008 124 131 (Pubitemid 47189751)
62. Xing, J., Ngadi, M., Wang, N., De Baerdemaeker, J., 2006. Wavelength selection for surface defects detection on tomatoes by means of a hyperspectral imaging system. In: ASABE Annual International Meeting, Portland, Oregon, USA. Paper No. 063018.