Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: A review

Journal of Food Engineering

Volumn 132, Issue , 2014, Pages 1-13

  Xiong, Zhenjie ^a   Sun, Da Wen ^a,b   Zeng, Xin An ^a   Xie, Anguo ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

Beef; Hardware; Hyperspectral imaging; Lamb; Pork; Quality attributes; Software

Indexed keywords

HYPERSPECTRAL IMAGING; HYPERSPECTRAL IMAGING SYSTEMS; LAMB; MEAT-PROCESSING INDUSTRY; PORK; QUALITY ATTRIBUTES; TECHNOLOGICAL ATTRIBUTES; WATER HOLDING CAPACITY;

BEEF; COLOR; COMPUTER HARDWARE; COMPUTER SOFTWARE; FOOD PROCESSING; IMAGING SYSTEMS; MOISTURE CONTROL; PROTEINS; SPECTROSCOPY; SPOILAGE;

MEATS;

EID: 84897707658     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2014.02.004     Document Type: Review

References (121)

1. N. Abdel-Nour, M. Ngadi, S. Prasher, and Y. Karimi Prediction of egg freshness and albumen quality using visible/near infrared spectroscopy Food Bioprocess Technol. 4 5 2011 731 736
2. E. Agullo, M. Centurion, V. Ramos, and M. Bianchi Determination of total pigments in red meats J. Food Sci. 55 1 1990 250 251
3. S. Aiqun, H. Yuanqing, and S. Jinchai Applied Technique and Development Trend of CCD Image Sensor Electronic Measurement and Instruments, 8th international conference on 2007 2007 IEEE pp. 2-840-2-843
4. S. Andrés, A. Silva, A. Soares-Pereira, C. Martins, A. Bruno-Soares, and I. Murray The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes Meat Sci. 78 3 2008 217 224
5. D. Barbin, G. Elmasry, D.-W. Sun, and P. Allen Near-infrared hyperspectral imaging for grading and classification of pork Meat Sci. 90 1 2012 259 268
6. D.F. Barbin, G. ElMasry, D.-W. Sun, and P. Allen Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging Food Chem. 2012 10 20
7. D.F. Barbin, G. ElMasry, D.-W. Sun, and P. Allen Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging Anal. Chim. Acta 719 2012 30 42
8. S. Barbut, A. Sosnicki, S. Lonergan, T. Knapp, D. Ciobanu, L. Gatcliffe, E. Huff-Lonergan, and E. Wilson Progress in reducing the pale, soft and exudative (PSE) problem in pork and poultry meat Meat Sci. 79 1 2008 46 63
9. M. Bigas, E. Cabruja, J. Forest, and J. Salvi Review of CMOS image sensors Microelectron. J. 37 5 2006 433 451
10. J. Brooks, J. Belew, D. Griffin, B. Gwartney, D. Hale, W. Henning, D. Johnson, J. Morgan, F. Parrish, and J. Reagan National Beef Tenderness Survey-1998 J. Anim. Sci. 78 7 2000 1852 1860
11. L.M. Bruce, C.H. Koger, and J. Li Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction Geosci. Remote Sens. IEEE Trans. 40 10 2002 2331 2338
12. J.B. Buschmann, K.R. Davies, and J.E. Oetken Tungsten Halogen Lamp Google Patents 2005
13. K. Chen, and C. Qin Segmentation of beef marbling based on vision threshold Comput. Electron. Agric. 62 2 2008 223 230
14. R. Choy, and A. Edelman Parallel MATLAB: doing it right Proc. IEEE 93 2 2005 331 341
15. O. Ciziri, and D. Dogan Fiber optic sensors Int. J. Acad. Res. Appl. Sci. 2 8 2013 7 12
16. 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 Sens. Instrum. Food Qual. Safety 2 3 2008 189 196
17. C. Cortes, and V. Vapnik Support vector machine Mach. Learn. 20 3 1995 273 297
18. Z.W. Cui, S.Y. Xu, and D.-W. Sun Effect of microwave-vacuum drying on the carotenoids retention of carrot slices and chlorophyll retention of chinese chive leaves Dry. Technol. 22 3 2004 563 575
19. A.E. Delgado, L.Y. Zheng, and D.-W. Sun Influence of ultrasound on freezing rate of immersion-frozen apples Food Bioproc. Tech. 2 3 2009 263 270
20. S. De Smet Meat, poultry, and fish composition: strategies for optimizing human intake of essential nutrients Anim. Front. 2 4 2012 10 16
21. T. Defraeye, W. Aregawi, S. Saneinejad, P. Vontobel, E. Lehmann, J. Carmeliet, P. Verboven, D. Derome, and B. Nicolaï Novel application of neutron radiography to forced convective drying of fruit tissue Food Bioprocess Technol. 6 12 2013 3353 3367
22. C.J. Du, and D.-W. Sun Comparison of three methods for classification of pizza topping using different colour space transformations J. Food Eng. 68 3 2005 277 287
23. R. El Rammouz, R. Babile, and X. Fernandez Effect of ultimate pH on the physicochemical and biochemical characteristics of turkey breast muscle showing normal rate of postmortem pH fall Poult. Sci. 83 10 2004 1750 1757
24. G. ElMasry, D.-W. Sun, and P. Allen Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging Food Res. Int. 44 9 2011 2624 2633
25. G. ElMasry, D.-W. Sun, and P. Allen Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef J. Food Eng. 110 1 2012 127 140
26. G. Elmasry, D.F. Barbin, D.-W. Sun, and P. Allen Meat quality evaluation by hyperspectral imaging technique: an overview Crit. Rev. Food Sci. Nutr. 52 8 2012 689 711
27. G. Elmasry, M. Kamruzzaman, D.-W. Sun, and P. Allen Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review Crit. Rev. Food Sci. Nutr. 52 11 2012 999 1023
28. G. ElMasry, D.-W. Sun, and P. Allen Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging J. Food Eng. 2013 10 20
29. Y.-Z. Feng, and D.-W. Sun Application of hyperspectral imaging in food safety inspection and control: a review Crit. Rev. Food Sci. Nutr. 52 11 2012 1039 1058
30. X. Fernandez, G. Monin, A. Talmant, J. Mourot, and B. Lebret Influence of intramuscular fat content on the quality of pig meat - 2. Consumer acceptability of M. longissimus lumborum Meat Sci. 53 1 1999 67 72
31. Fife, K., El Gamal, A., Wong, H., 2007. A 0.5 μm pixel frame-transfer CCD image sensor in 110 nm CMOS. In: Electron Devices Meeting 2007. IEDM 2007. IEEE International, 2007; IEEE: 2007, pp. 1003-1006.
32. I.K. Fodor A Survey of Dimension Reduction Techniques Technical Report UCRL-ID-148494 2002 Lawrence Livermore National Laboratory 10 20
33. M. Font-i-Furnols, A. Brun, N. Tous, and M. Gispert Use of Linear Regression and Partial Least Square Regression to Predict Intramuscular Fat of Pig Loin Computed Tomography Images Chemom. Intell. Lab. Sys. 2013
34. A. Fortin, W. Robertson, and A. Tong The eating quality of Canadian pork and its relationship with intramuscular fat Meat Sci. 69 2 2005 297 305
35. G. Geesink, F. Schreutelkamp, R. Frankhuizen, H. Vedder, N. Faber, R. Kranen, and M. Gerritzen Prediction of pork quality attributes from near infrared reflectance spectra Meat Sci. 65 1 2003 661 668
36. Goll, D., 1992. Role of proteinases and protein turnover in muscle growth and meat quality. In: Proceedings - Annual Reciprocal Meat Conference of the American Meat Science Association.
37. J. Gresham, S. McPeake, J. Bernard, and H. Henderson Commercial adaptation of ultrasonography to predict pork carcass composition from live animal and carcass measurements J. Anim. Sci. 70 3 1992 631 639
38. J.A. Hernández, B. Heyd, and G. Trystram On-line assessment of brightness and surface kinetics during coffee roasting J. Food Eng. 87 3 2008 314 322
39. J.A. Hernández, B. Heyd, and G. Trystram Prediction of brightness and surface area kinetics during coffee roasting J. Food Eng. 89 2 2008 156 163
40. K.O. Honikel, and C. Fischer A rapid method for the detection of PSE and DFD porcine muscles J. Food Sci. 42 6 1977 1633 1636
41. E. Huff-Lonergan Water-holding capacity of fresh meat Fact Sheet 2002 04669
42. P. Jackman, D.-W. Sun, and P. Allen Recent advances in the use of computer vision technology in the quality assessment of fresh meats Trends Food Sci. Technol. 22 4 2011 185 197
43. P. Jackman, D.-W. Sun, C.-J. Du, and P. Allen Prediction of beef eating quality from colour, marbling and wavelet texture features Meat Sci. 80 4 2008 1273 1281
44. T. Jones, and G.J. Rawlins In reverse Hillclimbing genetic algorithms and the busy beaver problem ICGA 1993 1993 70 75
45. A. Jorgensen Clustering Excipient Near Infrared Spectra Using Different Chemometric Methods, Technical Report 2000 Dept. of Pharmacy, University of Helsinki
46. M. Kamruzzaman, G. ElMasry, D.-W. Sun, and P. Allen Application of NIR hyperspectral imaging for discrimination of lamb muscles J. Food Eng. 104 3 2011 332 340
47. M. Kamruzzaman, D. Barbin, G. ElMasry, D.-W. Sun, and P. Allen Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat Innov. Food Sci. Emerg. Technol. 16 2012 316 325
48. M. Kamruzzaman, G. ElMasry, D.-W. Sun, and P. Allen Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression Innovative Food Sci. Emerg. Technol. 16 2012 218 226
49. M. Kamruzzaman, G. ElMasry, D.-W. Sun, and P. Allen Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis Anal. Chim. Acta 714 2012 57 67
50. M. Kamruzzaman, G. ElMasry, D.-W. Sun, and P. Allen Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging Food Chem. 2013
51. C. Kim, and E. Lee Effects of quality grade on the chemical, physical and sensory characteristics of Hanwoo (Korean native cattle) beef Meat Sci. 63 3 2003 397 405
52. K.-I. Kobayashi, Y. Matsui, Y. Maebuchi, T. Toyota, and S. Nakauchi Near infrared spectroscopy and hyperspectral imaging for prediction and visualisation of fat and fatty acid content in intact raw beef cuts J. Near Infrared Spectrosc. 18 5 2010 301 315
53. J. Kongsro, M. Røe, K. Kvaal, A. Aastveit, and B. Egelandsdal Prediction of fat, muscle and value in Norwegian lamb carcasses using EUROP classification, carcass shape and length measurements, visible light reflectance and computer tomography (CT) Meat Sci. 81 1 2009 102 107
54. N. Lambe, D. Ross, E. Navajas, J. Hyslop, N. Prieto, C. Craigie, L. Bünger, G. Simm, and R. Roehe The prediction of carcass composition and tissue distribution in beef cattle using ultrasound scanning at the start and/or end of the finishing period Livestock Sci. 131 2 2010 193 202
55. A. Lavanya, and S. Sanjeevi An improved band selection technique for hyperspectral data using factor analysis J. Indian Soc. Remote Sens. 2013 1 13
56. Li, Y., Zhang, L., Peng, Y., Tang, X., Chao, K., Dhakal, S., 2011a. In: Hyperspectral Imaging Technique for Determination of Pork Freshness Attributes, pp. 80270H-80270H-9.
57. Li, Y., Shan, J., Peng, Y., Gao, X., 2011b. Nondestructive assessment of beef-marbling grade using hyperspectral imaging technology. In: New Technology of Agricultural Engineering (ICAE), 2011 International Conference, pp. 779-783.
58. D. Litwiller CCD vs. CMOS Photon. Spectra 35 1 2001 154 158
59. Y. Liu, X. Sun, and A. Ouyang Non-destructive measurements of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLS and PCA-BPNN LWT - Food Sci. Technol. 43 2010 602 607
60. D. Liu, D.-W. Sun, and X.-A. Zeng Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry Food Bioprocess Technol. 7 2 2014 307 323
61. D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. García-Navarrete, and J. Blasco Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment Food Bioprocess Technol. 5 4 2012 1121 1142
62. R. Lu Detection of bruises on apples using near-infrared hyperspectral imaging Trans. Am. Soc. Agric. Eng. 46 2 2003 523 530
63. H. Lu, H. Lou, H. Zheng, Y. Hu, and Y. Li Nondestructive evaluation of quality changes and the optimum time for harvesting during jujube (Zizyphus jujuba Mill. cv. Changhong) fruits development Food Bioprocess Technol. 5 6 2012 2586 2595
64. L.S. Magwaza, U.L. Opara, H. Nieuwoudt, P.J. Cronje, W. Saeys, and B. Nicolaï NIR spectroscopy applications for internal and external quality analysis of citrus fruit - a review Food Bioprocess Technol. 5 2 2012 425 444
65. R. Mancini, and M. Hunt Current research in meat color Meat Sci. 71 1 2005 100 121
66. A. Manickavasagan, D.S. Jayas, N.D.G. White, and J. Paliwal Wheat class identification using thermal imaging Food Bioprocess Technol. 3 3 2010 450 460
67. Martellucci, S., Chester, A.N., Mignani, A.G., 2002. Optical Sensors and Microsystems. Springer, US.
68. A.J. McAfee, E.M. McSorley, G.J. Cuskelly, B.W. Moss, J.M. Wallace, M.P. Bonham, and A.M. Fearon Red meat consumption: an overview of the risks and benefits Meat Sci. 84 1 2010 1 13
69. P.M. Mehl, Y.-R. Chen, M.S. Kim, and D.E. Chan Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations J. Food Eng. 61 1 2004 67 81
70. A. Méndez, and A. Csipkes Overview of fiber optic sensors for NDT applications Nondestruct. Test. Mater. Struct. 2013 Springer Netherlands 179 184
71. 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 Comput. Electron. Agric. 64 2 2008 225 233
72. B.M. Nicolai, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K.I. Theron, and J. Lammertyn Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review Postharvest Biol. Technol. 46 2 2007 99 118
73. L. Norgaard, A. Saudland, J. Wagner, J.P. Nielsen, L. Munck, and S.B. Engelsen Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy Appl. Spectrosc. 54 3 2000 413 419
74. A. Orman, G.Ü. ÇalIşkan, S. Dikmen, H. Üstüner, M.M. Ogan, and Ç. ÇalIşkan The assessment of carcass composition of Awassi male lambs by real-time ultrasound at two different live weights Meat Sci. 80 4 2008 1031 1036
75. G. Otto, R. Roehe, H. Looft, L. Thoelking, and E. Kalm Comparison of different methods for determination of drip loss and their relationships to meat quality and carcass characteristics in pigs Meat Sci. 68 3 2004 401 409
76. P.B. Pathare, U.L. Opara, and F.A.J. Al-Said Color measurement and analysis in fresh and processed foods: a review Food Bioprocess Technol. 6 1 2013 36 60
77. B. Park, K.C. Lawrence, W.R. Windham, and D.P. Smith Performance of hyperspectral imaging system for poultry surface fecal contaminant detection J. Food Eng. 75 3 2006 340 348
78. Peng, Y., Wang, W., 2008. Prediction of pork meat total viable bacteria count using hyperspectral imaging system and support vector machines. In: Proceedings of the Food Processing Automation Conference. Providence, RI.
79. Peng, Y., Zhang, J., Wu, J., Hang, H., 2009. Hyperspectral scattering profiles for prediction of the microbial spoilage of beef. In: Proc. of SPIE, 2009. Pp. 73150Q-1.
80. Y. Peng, F. Tao, Y. Li, W. Wang, J. Chen, J. Wu, and S. Dhakal Rapid Detection of Total Viable Count of Chilled Pork Using Hyperspectral Scattering Technique 2010 SPIE Defense Security and Sensing. International Society for Optics and Photonics pp. 76760K-76760K-8
81. M. Petracci, and E. Baeza Harmonization of methodologies for the assessment of poultry meat quality features World's Poult. Sci. J. 67 01 2011 137 151
82. M. Prevolnik, M. Čandek-Potokar, and D. Škorjanc Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods J. Food Eng. 98 3 2010 347 352
83. N. Prieto, E. Navajas, R. Richardson, D. Ross, J. Hyslop, G. Simm, and R. Roehe Predicting beef cuts composition, fatty acids and meat quality characteristics by spiral computed tomography Meat Sci. 86 3 2010 770 779
84. J. Qiao, N. Wang, M. Ngadi, and A. Gunenc In determination of pork quality attributes using hyperspectral imaging technique optics east Int. Soc. Opt. Photon. 2005 pp. 59960M-59960M-8
85. J. Qiao, N. Wang, M. Ngadi, A. Gunenc, M. Monroy, C. Gariépy, and S. Prasher Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique Meat Sci. 76 1 2007 1 8
86. J. Qiao, M.O. Ngadi, N. Wang, C. Gariépy, and S. Prasher Pork quality and marbling level assessment using a hyperspectral imaging system J. Food Eng. 83 1 2007 10 16
87. A. Rinnan, F.V.D. Berg, and S.B. Engelsen Review of the most common pre-processing techniques for near-infrared spectra TrAC, Trends Anal. Chem. 28 10 2009 1201 1222
88. R. Rødbotten, B. Nilsen, and K. Hildrum Prediction of beef quality attributes from early post mortem near infrared reflectance spectra Food Chem. 69 4 2000 427 436
89. K. Rosenvold, and H.J. Andersen Factors of significance for pork quality - a review Meat Sci. 64 3 2003 219 237
90. E. Sahin, M. Yardimci, I. Cetingul, I. Bayram, and E. Sengor The use of ultrasound to predict the carcass composition of live Akkaraman lambs Meat Sci. 79 4 2008 716 721
91. P. Shippert Introduction to hyperspectral image analysis J. Space Commun. 2003 3
92. D.-W. Sun Comparison and selection of EMC ERH isotherm equations for rice J. Stored Prod. Res. 35 3 1999 249 264
93. D.-W. Sun, and T. Brosnan Extension of the vase life of cut daffodil flowers by rapid vacuum cooling Int. J. Refrig. 22 6 1999 472 478
94. D.-W. Sun, and C. Byrne Selection of EMC/ERH isotherm equations for rapeseed J. Agr. Eng. Res. 69 4 1998 307 315
95. D.-W. Sun, and Z.H. Hu CFD simulation of coupled heat and mass transfer through porous foods during vacuum cooling process Int. J. Refrig. 26 1 2003 19 27
96. D.-W. Sun, and J.L. Woods The moisture-content relative-humidity equilibrium relationship of wheat - A review Dry. Technol. 11 7 1993 1523 1551
97. D.-W. Sun, and J.L. Woods Low-temperature Moisture Transfer characteristics of wheat in thin-layers T. ASAE 37 6 1994 1919 1926
98. D.-W. Sun, and J.L. Woods Low-temperature moisture transfer characteristics of barley - thin-layer models and equilibrium isotherms J. Agr. Eng. Res. 59 4 1994 273 283
99. D.-W. Sun, and J.L. Woods Simulation of the heat and moisture transfer process during drying in deep grain beds Dry. Technol. 15 10 1997 2479 2508
100. D.-W. Sun, and L.Y. Zheng Vacuum cooling technology for the agri-food industry: past, present and future J. Food Eng. 77 2 2006 203 214
101. K. Sowoidnich, H. Schmidt, M. Maiwald, B. Sumpf, and H.-D. Kronfeldt Application of diode-laser raman spectroscopy for in situ investigation of meat spoilage Food Bioprocess Technol. 3 6 2010 878 882
102. Suntharalingam, V., Burke, B., Cooper, M., Yost, D., Gouker, P., Anthony, M., Whittingham, H., Sage, J., Burns, J., Rabe, S., 2000. Monolithic 3.3 V CCD/SOI-CMOS imager technology. In: Electron Devices Meeting 2000. IEDM'00. Technical Digest. International 2000. IEEE: 2000, pp. 697-700.
103. J.A. Suykens, and J. Vandewalle Least squares support vector machine classifiers Neural Process. Lett. 9 3 1999 293 300
104. F. Tao, Y. Peng, Y. Li, K. Chao, and S. Dhakal Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique Meat Sci. 90 3 2012 851 857
105. N.A. Valous, F. Mendoza, and D.-W. Sun Emerging non-contact imaging, spectroscopic and colorimetric technologies for quality evaluation and control of hams: a review Trends Food Sci. Technol. 21 1 2010 26 43
106. N.A. Valous, F. Mendoza, D.-W. Sun, and P. Allen Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams Meat Sci. 81 1 2009 132 141
107. L.J. Wang, and D.-W. Sun Rapid cooling of porous and moisture foods by using vacuum cooling technology Trends Food Sci. Technol. 12 5-6 2001 174 184
108. W. Wang, and L.-L. Zhang A rapid nondestructive measurement method for assessing the total plate count on chilled pork surface Spectrosc. Spect. Anal. 30 12 2010 3405 3409
109. W. Wang, and X.-L. Zhang Study on modeling method of total viable count of fresh pork meat based on hyperspectral imaging system Spectrosc. Spect. Anal. 30 2 2010 411 415
110. W. Wang, Y. Peng, H. Huang, and J. Wu Application of hyper-spectral imaging technique for the detection of total viable bacteria count in pork Sens. Lett. 9 3 2011 1024 1030
111. R. Warner, R. Kauffman, and M. Greaser Muscle protein changes post mortem in relation to pork quality traits Meat Sci. 45 3 1997 339 352
112. E. Wierbicki, and F. Deatherage Water content of meats, determination of water-holding capacity of fresh meats J. Agric. Food Chem. 6 5 1958 387 392
113. J. Wu, Y. Peng, J. Chen, W. Wang, X. Gao, and H. Huang Study of spatially resolved hyperspectral scattering images for assessing beef quality characteristics Spectrosc. Spect. Anal. 30 7 2010 1815 1819
114. J. Wu, Y. Peng, Y. Li, W. Wang, J. Chen, and S. Dhakal Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique J. Food Eng. 109 2 2012 267 273
115. D. Wu, D.-W. Sun, and Y. He Application of long-wave near infrared hyperspectral imaging for measurement of colour distribution in salmon fillet Innovative Food Sci. Emerg. Technol. 2012 10 20
116. S.Y. Xu, X.F. Chen, and D.-W. Sun Preservation of kiwifruit coated with an edible film at ambient temperature J. Food Eng. 50 4 2001 211 216
117. O.A. Young, and J. West Meat color Meat Sci. Appl. 2001 39 69
118. F. Zaïdi, H. Rouissi, S. Dridi, M. Kammoun, J. de Baerdemaeker, and R. Karoui Front-face fluorescence spectroscopy as a rapid and non-destructive tool for differentiating between sicilo-sarde and comisana ewe's milk during lactation period: A preliminary study Food Bioprocess Technol. 1 2 2008 143 151
119. J. Zhang, B. Rivard, and D. Rogge The successive projection algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data Sensors 8 2 2008 1321 1342
120. Q. Zhang, A.S.M. Saleh, and Q. Shen Discrimination of edible vegetable oil adulteration with used frying oil by low field nuclear magnetic resonance Food Bioprocess Technol. 6 9 2013 2562 2570
121. L.Y. Zheng, and D.-W. Sun Innovative applications of power ultrasound during food freezing processes - a review Trends Food Sci. Technol. 17 1 2006 16 23