Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Imaging Technologies for Nondestructive Measurement of Internal Properties of Agricultural Products: A Review

  • Ahmed, Mohammed Raju (Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University) ;
  • Yasmin, Jannat (Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University) ;
  • Lee, Wang-Hee (Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University) ;
  • Mo, Changyeun (National Academy of Agricultural Science, Rural Development Administration) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University)
  • Received : 2017.08.20
  • Accepted : 2017.08.29
  • Published : 2017.09.01
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Abstract

Purpose: This study reviewed the major nondestructive measurement techniques used to assess internal properties of agricultural materials that significantly influence the quality, safety, and value of the products in markets. Methods: Imaging technologies are powerful nondestructive analytical tools that possess specific advantages in revealing the internal properties of products. Results: This review was exploring the application of various imaging techniques, specifically, hyperspectral imaging (HSI), magnetic resonance imaging (MRI), soft X-ray, X-ray computed tomography (XRI-CT), thermal imaging (TI), and ultrasound imaging (UI), to investigate the internal properties of agricultural commodities. Conclusions: The basic instruments used in these techniques are discussed in the initial part of the review. In the context of an investigation of the internal properties of agricultural products, including crops, fruits, vegetables, poultry, meat, fish, and seeds, various extant studies are examined to understand the potential of these imaging technologies. Future trends for these imaging techniques are also presented.

Keywords

References

  1. Aboras, M., H. Amasha and I. Ibraheem. 2015. Early detection of melanoma using multispectral imaging and artificial intelligence techniques. American Journal of Biomedical and Life Sciences 3(2-3): 29-33. https://doi.org/10.11648/j.ajbls.s.2015030201.15
  2. Aenugu, H. P. R., D. S. Kumar, Srisudharson, N. Parthiban, S. S. Ghosh and D. Banji. 2011. Near infra red spectroscopy- an overview. International Journal Of Chemtech Research 3(2): 825-836.
  3. Ahmed, M. R., J. Yasmin, C. Mo, H. Lee, M. S. Kim, S. J. Hong and B. K. Cho. 2106. Outdoor applications of hyperspectral imaging technology for monitoring agricultural crops: A review. Journal of Biosystems Engineering 41(4): 396-407. https://doi.org/10.5307/JBE.2016.41.4.396
  4. Al-Turki, T. A. and C. C. Baskin. 2017. Determination of seed viability of eight wild Saudi Arabian species by germination and X-ray tests. Saudi Journal of Biological Sciences 24(4): 822-829. https://doi.org/10.1016/j.sjbs.2016.06.009
  5. Arora, N., D. Martins, D. Ruggerio, E. Tousimis, A. J. Swistel and M. P. Osborne. 2008. Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. The American Journal of Surgery 196(4): 523-526. https://doi.org/10.1016/j.amjsurg.2008.06.015
  6. Badhe, V. T., P. Singh and Y. C. Bhatt. 2011. Development and evaluation of mango grader. Journal of Agricultural Engineering 48(2): 43-47.
  7. Baiano A., C. Terracone, G. Peri and R. Romaniello. 2012. Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes. Computers and Electronics in Agriculture 87: 142-151. https://doi.org/10.1016/j.compag.2012.06.002
  8. Baranowski, P., W. Mazurek, B. W. Walczak and C. Slawinski. 2009. Detection of early apple bruises using pulsed-phase thermography. Postharvest Biology and Technology 53(3): 91-100. https://doi.org/10.1016/j.postharvbio.2009.04.006
  9. Barcelon, E. G., S. Tojo and K. Watanabe. 1999. X-ray computed tomography for internal quality evaluation of peaches. Journal of Agricultural Engineering Research 73(4): 323-330. https://doi.org/10.1006/jaer.1999.0409
  10. Barry, P., C. Seqal and S. Carman. 2001. EO-1 Hyperion science Data User's Guide. TRW Space, Defense and Information Systems 1(077):1-65.
  11. Bashford, A. G., D. W. Schindel, D. A. Hutchins and W. M. D. Wright. 1997. Field characterization of an air-coupled micromachined ultrasonic transducer. Journal of Acoustic Society of America 101(1): 315-322. https://doi.org/10.1121/1.418011
  12. Belton, P. S., S. B. Engelsen and H. J. Jakobsen. 2005. Magnetic resonance in food science: The multivariate challenge. Cambridge: Royal Society of Chemistry, UK.
  13. Burg, W. J. V. D., J. W. Aartse, R. A. V. Zwol, H. Jalink and R. J. Bino. 1994. Predicting tomato seedling morphology by x-ray analysis of seeds. Journal of the American Society for Horticultural Science 119(2): 258-263.
  14. Butz, P., C. Hofmann and B. Tauscher. 2005. Recent developments in noninvasive techniques for fresh fruit and vegetable internal quality analysis. Journal of Food Science 70(9): 131-141. https://doi.org/10.1111/j.1365-2621.2005.tb07085.x
  15. Chelladurai, V., D. S. Jayas, N. D. G. White. 2010. Thermal imaging for detecting fungal infection in stored wheat. Journal of Stored Products Research 46(3): 174-179. https://doi.org/10.1016/j.jspr.2010.04.002
  16. Chen, P. and Z. Sun. 1991. A review of non-destructive methods for quality evaluation and sorting of agricultural products. Journal of Agricultural Engineering Research 49: 85-98. https://doi.org/10.1016/0021-8634(91)80030-I
  17. Chen, P., M. J. McCarthy, R. Kauren, Y. Sarig and S. Han. 1993. Maturity evaluation of avocados by NMR methods. Journal of Agricultural Engineering Research 55(3): 177-187. https://doi.org/10.1006/jaer.1993.1042
  18. Chen, Q., C. Zhang, J. Zhao and Q. Ouyang. 2013. Recent advances in emerging imaging techniques for non-destructive detection of food quality and safety. Trends in Analytical Chemistry 52: 261-274. https://doi.org/10.1016/j.trac.2013.09.007
  19. Chengcheng, L., Z. Yufeng and A. Kwabena. 2009. Prediction of IMF percentage of live cattle by using ultrasound technologies with high accuracies. In: WASE International Conference, pp. 474-478, Taiyuan.
  20. Cho, B. K. and J. M. K. Irudayaraj. 2003. Foreign object and internal disorder detection in food materials using noncontact ultrasound imaging. Journal of Food Science 68(3): 967-974. https://doi.org/10.1111/j.1365-2621.2003.tb08272.x
  21. Cogdill, R. P., C. R. Hurburgh, Jr., G. R. Rippke, S. J. Bajic, R. W. Jones, J. F. McClelland, T. C. Jensen and J. Liu. 2004. Single-kernel maize analysis by near-infrared hyperspectral imaging. Transactions of the ASAE 47(1): 311-320. https://doi.org/10.13031/2013.15856
  22. Collewet, G., M. Strzelecki and F. Mariette. 2004. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magnetic Resonance Imaging 22(1): 81-91. https://doi.org/10.1016/j.mri.2003.09.001
  23. Costa, N., C. Stelletta and C. Cannizzo, M. Gianesella, P. L. Fiego, and M. Morgante. 2007. The use of thermography on the slaughter-line for the assessment of pork and raw ham quality. Italian Journal of Animal Science 6(1): 704-706.
  24. Danno, A., M. Miyazato and E. Ishiguro. 1977. Quality evaluation of agricultural products by infrared imaging method: Grading of fruits for bruise and other surface defects. Memoirs of the Faculty of Agriculture, Kagoshima University, 14: 123-138.
  25. Defraeye, T., V. Lehmann, D. Gross, C. Holat, E. Herremans, P. Verboven, B. E. Verlinden and B. M. Nicolai. 2013. Application of MRI for tissue characterisation of 'Braeburn' apple. Postharvest Biology and Technology 75: 96-105. https://doi.org/10.1016/j.postharvbio.2012.08.009
  26. Diels, E., M. V. Dael, J. Keresztes, S. Vanmaercke, P. Verboven, B. Nicolai, W. Saeys, H. Ramon and B. Smeets. 2017. Assessment of bruise volumes in apples using X-ray computed tomography. Postharvest Biology and Technology 128: 24-32. https://doi.org/10.1016/j.postharvbio.2017.01.013
  27. Donis-Gonzalez, I. R., D. E. Guyer, A. Pease and D. W. Fulbright. 2012. Relation of computerized tomography Hounsfield unit measurements and internal components of fresh chestnuts (Castanea spp.). Postharvest Biology and Technology 64(1): 74-82. https://doi.org/10.1016/j.postharvbio.2011.09.018
  28. Donis-Gonzalez, I. R., D. E. Guyer, A. Pease and F. Barthel. 2014. Internal characterisation of fresh agricultural products using traditional and ultrafast electron beam X-ray computed tomography imaging. Biosystems Engineering 117: 104-113. https://doi.org/10.1016/j.biosystemseng.2013.07.002
  29. Du, C. J. and D. W. Sun. 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Science Technology 15(5): 230-249. https://doi.org/10.1016/j.tifs.2003.10.006
  30. ElMasry, G., D. W. Sun and P. Allen. 2012. Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. Journal of Food Engineering 110(1): 127-140. https://doi.org/10.1016/j.jfoodeng.2011.11.028
  31. ElMasry, G., N. Wang, A. ElSayed, M. Ngadi. 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering 81(1): 98-107. https://doi.org/10.1016/j.jfoodeng.2006.10.016
  32. eXtension. 2017. What is the difference between multispectral and hyperspectral imagery?. Geospatial Technology. Available at: http://articles.extension.org/pages/40073/what-is-the-difference-betweenmultispectral-and-hyperspectral-imagery
  33. Farhat, I. A., P. S. Belton and G. A. Webb. 2007. Magnetic resonance in food science: From molecules to man. Cambridge: RSC Publishing, UK.
  34. Faulkner, D. B., D. F. Parrett, F. K. Mckeith and L. L. Berger. 1990. Prediction of fat cover and carcass composition from live and carcass measurements. Journal of Animal Science 68(3): 604-610.
  35. Fernandes, J. S., D. F. D. Silva, H. O. D. Santos and E. V. D. R. V. Pinho. 2016. X-ray test in the evaluation of seed quality of physalis at different stages of development. Revista de Ciencias Agroveterinarias, Lages 15(2): 165-168. (In Portuguese, with English abstract).
  36. Foucat. L., A. Chavagnat and J. P. Renou. 1993. Nuclear magnetic resonance micro-imaging and X-radiography as possible techniques to study seed germination. Scientia Horticulturae 55(3-4): 323-331. (In French, with English abstract). https://doi.org/10.1016/0304-4238(93)90043-P
  37. Gan, T. H., P. Pallav and D. A. Hutchins. 2006. Non-contact ultrasonic quality measurements of food products, Journal of Food Engineering 77 (2): 239-247. https://doi.org/10.1016/j.jfoodeng.2005.06.026
  38. Gaston, E., J. M. Frias, P. J. Cullen, C. P. O'Donnell and A. A. Gowen. 2010. Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. Journal of Agricultural and Food Chemistry 58(10): 6226-6233. https://doi.org/10.1021/jf100501q
  39. Geng, S., H. Wang, X. Wang, X. Ma, S. Xiao, J. Wanga and M. Tan. 2015. A non-invasive NMR and MRI method to analyze the rehydration of dried sea cucumber. Analytical Methods 7(6): 2413-2419. https://doi.org/10.1039/C4AY03007A
  40. Ghaedian, R., E. A. Decker and D. J. McClements. 1997. Use of ultrasound to determine cod fillet composition. Journal of Food Science 62(3): 500-504. https://doi.org/10.1111/j.1365-2621.1997.tb04415.x
  41. Ghaedian, R., J. N. Coupland, E. A. Decker and D. J. McClements. 1998. Ultrasonic determination of fish composition. Journal of Food Engineering 35(3): 323-337. https://doi.org/10.1016/S0260-8774(98)00027-2
  42. GIS Geography. 2017. Multispectral vs hyperspectral imagery explained. Remote Sensing. Available at: http://gisgeography.com/multispectral-vs-hyperspectral-imagery-explained/
  43. Gomes, K. B. P., J. M. M. Matos, I. S. Martins and R. D. C. C. Martins. 2016. X-ray test to evaluate the physiological potencial of Platypodium elegans Vog. Seeds (Fabaceae). Scientia Agropecuaria 7 (3): 305-311. (In Portuguese, with English abstract). https://doi.org/10.17268/sci.agropecu.2016.03.19
  44. Gonzalez, J. J., R. C. Valle, S. Bobroff, W. V. Biasi, E. J. Mitcham and M. J. McCarthy. 2001. Detection and monitoring of internal browning development in 'Fuji' apples using MRI. Postharvest Biology and Technology 22(2):179-188. https://doi.org/10.1016/S0925-5214(00)00183-6
  45. Gowen A. A., C. P. O'Donnell, M. Taghizadeh, P. J. Cullen, J. M. Frias and G. Downey. 2008. Hyperspectral imaging combined with principal component analysis for bruise damage detection on white mushrooms (Agaricus bisporus). Journal of Chemometrics 22(3-4): 259-267. https://doi.org/10.1002/cem.1127
  46. Gowen, A. A., C. P. O'Donnell, P. J. Cullen, G. Downey and J. M. Frias. 2007. Hyperspectral imaging - an emerging process analytical tool for food quality and safety control. Trends in Food Science & Technology 18(12): 590-598. https://doi.org/10.1016/j.tifs.2007.06.001
  47. Gowena, A. A., B. K. Tiwaria, P. J. Cullenb, K. McDonnella and C. P. O'Donnell. 2010. Applications of thermal imaging in food quality and safety assessment. Trends in Food Science & Technology 21(4): 190-200. https://doi.org/10.1016/j.tifs.2009.12.002
  48. Graham, S., T. Kennedy, O. Chevallier, A. Gordon, L. Farmer and C. Elliott. 2010. The application of NMR to study changes in polar metabolite concentrations in beef longissimus dorsi stored for different periods post mortem. Metabolomics 6(3): 395-404. https://doi.org/10.1007/s11306-010-0206-y
  49. Gudjonsdottir, M., P. S. Belton and G. A. Webb. 2009. Magnetic Resonance in Food Science: Challenges in a Changing World. Cambridge: The Royal Society of Chemistry.
  50. Han, Y. J., S. V. Bowers and R. B. Dodd. 1992. Nondestructive detection of split-pit peaches. Transactions of the ASAE 35(6): 2063-2067. https://doi.org/10.13031/2013.28835
  51. Hellebrand, H. J., M. Linke, H. Beuche, B. Herold and M. Geyer. 2000. Horticultural products evaluated by thermography. In AgEng 2000, Paper No. 00-PH-003, University of Warwick, UK.
  52. Hernandez-Hierro, J. M., C. Esquerre, J. Valverde, S. Villacreces, K. Reilly, M. Gaffney, M. L. Gonzalez-Miret, F. J. Heredia, C. P. O'Donnell and G. Downey. 2014. Preliminary study on the use of near infrared hyperspectral imaging for quantitation and localisation of total glucosinolates in freeze-dried broccoli. Journal of Food Engineering 126: 107-112. https://doi.org/10.1016/j.jfoodeng.2013.11.005
  53. Herremans, E., P. Verboven, E. Bongaers, P. Estrade, B.E. Verlinden and M. Wevers. 2013. Characterisation of Braeburn browning disorder by means of X-ray micro-CT. Postharvest Biology and Technology 75: 114-124. https://doi.org/10.1016/j.postharvbio.2012.08.008
  54. Herremans, E., P. Verboven, T. Defraeye, S. Rogge, Q.T. Ho and M.L.A.T.M. Hertog. 2014. X-ray CT for quantitative food microstructure engineering: the apple case. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 324: 88-94. https://doi.org/10.1016/j.nimb.2013.07.035
  55. Herrero, A. M., M. I. Cambero, J. A. Ordonez, D. Castejon, M. D. R. D. Avila and L. D. Hoz. 2007. Magnetic resonance imaging, rheological properties and physicochemical characteristics of meat systems with fibrinogen and thrombin. Journal of Agricultural and Food Chemistry 55(23): 9357-9364. https://doi.org/10.1021/jf072132i
  56. Horigane, A. K., H. Takahashi, S. Maruyama, K. Ohtsubo and M. Yoshid. 2006. Water penetration into rice grains during soaking observed by gradient echo magnetic resonance imaging. Journal of Cereal Science 44(3): 307-316. https://doi.org/10.1016/j.jcs.2006.07.014
  57. Horigane, A. K., K. Suzuki and M. Yoshid. 2013. Moisture distribution of soaked rice grains observed by magnetic resonance imaging and physicochemical properties of cooked rice grains. Journal of Cereal Science 57(1): 47-55. https://doi.org/10.1016/j.jcs.2012.09.009
  58. Huang, M., Q. Wang, M. Zhang and Q. Zhu. 2014. Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology. Journal of Food Engineering 128: 24-30. https://doi.org/10.1016/j.jfoodeng.2013.12.008
  59. Hutchins, D. A., D. W. Schindel, A. G. Bashford and W. M. D. Wright. 1998. Advances in ultrasonic electrostatic transduction. Ultrasonics 36(1-5): 1-6. https://doi.org/10.1016/S0041-624X(97)00079-6
  60. Iqbal, A., D. W. Sun and P. Allen. 2013. Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system. Journal of Food Engineering 117(1): 42-51. https://doi.org/10.1016/j.jfoodeng.2013.02.001
  61. Jorge, M. H. A. and D. T. Ray. 2005. Germination characterization of guayule seed by morphology, mass and, X-ray analysis. Industrial Crops and Products 22(1): 59-63. https://doi.org/10.1016/j.indcrop.2004.05.007
  62. Joyce, D. C., P. D. Hockings, R. A. Mazucco and A. J. Shorter. 2002. H-nuclear magnetic resonance imaging of ripening 'Kensington Pride' mango fruit. Plant function and evolutionary biology 29(7): 873-879. https://doi.org/10.1071/PP01150
  63. Jung, Y., J. Lee, J. Kwon, K. S. Lee, D. H. Ryu and G. S. Hwang. 2010. Discrimination of the geographical origin of beef by 1H NMR-based metabolomics. Journal of Agricultural and Food Chemistry 58(19): 10458-10466. https://doi.org/10.1021/jf102194t
  64. Kamruzzaman, M., G. ElMasry, D. W. Sun and P. Allen. 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
  65. Kamruzzaman, M., Y. Makino and S. Oshita. 2016. Parsimonious model development for real-time monitoring of moisture in red meat using hyperspectral imaging. Food Chemistry 196: 1084-1091. https://doi.org/10.1016/j.foodchem.2015.10.051
  66. Katayama, K., K. Komaki and S. Tamiya. 1996. Prediction of starch, moisture and sugar in sweetpotato by near infrared transmittance. HortScience 31(6): 1003-1006.
  67. Khojastehnazhand, M., M. Omid and A. Tabatabaeefar. 2010. Development of a lemon sorting system based on color and size. African Journal of Plant Science 4(4): 122-127.
  68. Kikuchi, K., M. Koizumi, N. Ishida and H. Kano. 2006. Water uptake by dry beans observed by micro-magnetic resonance imaging. Annals of Botany 98 (3): 545-553. https://doi.org/10.1093/aob/mcl145
  69. Kim, S. and T. Schatzki. 2001. Detection of pinholes in almonds through x-ray imaging. Transactions of the ASAE 44(4): 997-1003.
  70. Kim, S. and T. F. Schatzki. 2000. Apple watercore sorting using X-ray imagery: I. Algorithm development. Transactions of the ASAE 43(6): 1695-1702. https://doi.org/10.13031/2013.3070
  71. Kim, S. and T. F. Schatzki. 2001. Detection of pinholes in almonds through X-ray imaging. Transactions of the ASAE, 44(4): 997-1003.
  72. Kotwaliwale, N., A. Kalne and K. Singh. 2011. Radiography, CT and MRI. Springer, Berlin.
  73. Kotwaliwale, N., E. Curtis, S. Othman, G. K. Naganathan and J. Subbiah. 2012. Magnetic resonance imaging and relaxometry to visualize internal freeze damage to pickling cucumber. Postharvest Biology and Technology 68: 22-31. https://doi.org/10.1016/j.postharvbio.2011.12.022
  74. Kotwaliwale. N., K. Singh, A. Kalne, S. N. Jha, N. Seth and A. Kar. 2014. X-ray imaging methods for internal quality evaluation of agricultural produce. Journal of Food Science and Technology 51(1):1-15. https://doi.org/10.1007/s13197-011-0485-y
  75. Lammertyn, J., B. Nicolai, K. Ooms, V. D. Smedt and J. D. Baerdemaeker. 1998. Non-destructive measurement of acidity, soluble solids and firmness of Jonagold apples using NIR-spectroscopy. Transactions of the ASAE 41(4): 1089-1094. https://doi.org/10.13031/2013.17238
  76. Lammertyn, J., T. Dresselaers, P. V. Hecke, P. Jancsok, M. Wevers, B. M. Nicolai. 2003. Analysis of the time course of core breakdown in 'Conference' pears by means of MRI and X-ray CT. Postharvest Biology and Technology 29(1): 19-28. https://doi.org/10.1016/S0925-5214(02)00212-0
  77. Lammertyn, J., T. Dresselaers, P. V. Hecke, P. Jancsók, M. Wevers and B. M. Nicolai. 2003. MRI and X-ray CT study of spatial distribution of core breakdown in conference pears. Magnetic Resonance Imaging 21(7): 805-815. https://doi.org/10.1016/S0730-725X(03)00105-X
  78. Lee, S., S. Lohumi, H. S. Lim, T. Gotoh, B. K. Cho and S. Jung. 2015. Determination of Intramuscular Fat Content in Beef using Magnetic Resonance Imaging. J. Fac. Agr., Kyushu Univ. 60(1): 157-162.
  79. Leiva-Valenzuela, G. A., R. Lu and J. M. Aguilera. 2013. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Journal of Food Engineering 115(1): 91-98. https://doi.org/10.1016/j.jfoodeng.2012.10.001
  80. Lu, G. and B. Fei. 2014. Medical hyperspectral imaging: a review. Journal of Biomedical Optics 19(1): 010901-23. https://doi.org/10.1117/1.JBO.19.1.010901
  81. Magwaza, L. S. and U. L. Opara. 2014. Investigating non-destructive quantification and characterization of pomegranate fruit internal structure using X-ray computed tomography. Postharvest Biology and Technology 95: 1-6. https://doi.org/10.1016/j.postharvbio.2014.03.014
  82. Manickavasagan, A., D. S. Jayas, N. D. G. White and J. Paliwal. 2008b. Wheat class identification using thermal imaging. Food and Bioprocess Technology 3(3): 450-460. https://doi.org/10.1007/s11947-008-0110-x
  83. Manickavasagan, A., D. S. Jayas, N. D. G. White and J. Paliwal. 2008a. Wheat class identification using thermal imaging: A potential innovative technique. Transactions of the ASABE 51(2): 649-651. https://doi.org/10.13031/2013.24362
  84. Manickavasagana, A., D. S. Jayasb and N. D. G. White. 2008. Thermal imaging to detect infestation by Cryptolestes ferrugineus inside wheat kernels. Journal of Stored Products Research 44(2): 186-192. https://doi.org/10.1016/j.jspr.2007.10.006
  85. Mariette, F. 2004. Relaxation RMN et IRM: un couplage indispensable pour l'etude des produits alimentaires, Comptes Rendus Chimie 7(3-4): 221-232. (In French, with English abstract). https://doi.org/10.1016/j.crci.2003.11.004
  86. Mariette, F. and C. R. Chimie. 2004. Relaxation RMN et IRM : un couplage indispensable pour l'étude des produits alimentaires. Comptes Rendus Chimie 7(3-4): 221-232. (In French, with English abstract). https://doi.org/10.1016/j.crci.2003.11.004
  87. Marigheto, N., L. Venturi and B. Hills. 2008. Two-dimensional NMR relaxation studies of apple quality. Postharvest Biology and Technology 48(3):331-340. https://doi.org/10.1016/j.postharvbio.2007.11.002
  88. Marshall, Stephen. 2011. Hyperspectral imaging technology. AWE International 1(25): 9-15.
  89. Mathiassen, J. R., E. Misimi, M. Bondo, E. Veliyulin and S. O. Ostvik. 2011. Trends in application of imaging technologies to inspection of fish and fish products. Trends in Food Science & Technology 22(6): 257-275. https://doi.org/10.1016/j.tifs.2011.03.006
  90. McCarthy, M.J., B. Zion, P. Chen, S. Ablett, A. H. Darke and P. J. Lillford. 1995. Diamagnetic susceptibility change in apple tissue after bruising. Journal of the Science of Food and Agriculture 67(1): 13-20. https://doi.org/10.1002/jsfa.2740670103
  91. McClements, D. J. 1997. Ultrasonic Characterization of Foods and Drinks: Principles, Methods, and Applications. Critical Reviews in Food Science and Nutrition 37(1): 1-46. https://doi.org/10.1080/10408399709527766
  92. Melado-Herreros, A., M. A. Munoz-Garcia, A. Blanco, J. Val, M. E. Fernandez-Valle and P. Barreiro. 2013. Assessment of watercore development in apples with MRI: effect of fruit location in the canopy. Postharvest Biology and Technology 86: 125-133. https://doi.org/10.1016/j.postharvbio.2013.06.030
  93. Melado-Herreros, A., M. E. Fernandez-Valle and P. Barreiro. 2015. Non-destructive global and localized 2D T1/T2 NMR relaxometry to resolve microstructure in apples affected by watercore. Food and Bioprocess Technology 8(1): 88-99. https://doi.org/10.1007/s11947-014-1389-4
  94. Mendoza, F., R. Lu, D. Ariana, H. Cen and B. Bailey. 2011. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology 62(2):149-160. https://doi.org/10.1016/j.postharvbio.2011.05.009
  95. Meola, C. and G. M. Carlomagno. 2004. Recent advances in the use of infrared thermography. Measurement Science and Technology 15(9): 27-58. https://doi.org/10.1088/0957-0233/15/9/R01
  96. Mery, D., I. Lillo, H. Loebel, V. Riffo, A. Soto, A. Cipriano and J. M. Aguilera. 2011. Automated fish bone detection using X-ray imaging. Journal of Food Engineering 105(3): 485-492. https://doi.org/10.1016/j.jfoodeng.2011.03.007
  97. Milczarek, R., M. E. Saltveit, T. C. Garvey and M. J. McCarthy. 2009. Assessment of tomato pericarp mechanical damage using multivariate analysis of magnetic resonance images. Postharvest Biology and Technology 52(2): 189-195. https://doi.org/10.1016/j.postharvbio.2009.01.002
  98. Mitchell, A. D., A. M. Scholz, P. C. Wang and H. Song. 2001. Body composition analysis of the pig by magnetic resonance imaging. Journal of Animal Science 79(7): 1800-1813. https://doi.org/10.2527/2001.7971800x
  99. Mitsuhashi-Gonzalez, K., M. J. Pitts, J. K. Fellman, E. A. Curry and C. D. Clary. 2010. Bruising profile of fresh apples associated with tissue type and structure. Applied Engineering in Agriculture 26(3): 509-518. https://doi.org/10.13031/2013.29942
  100. Mohammadi, V., M. Ghasemi-Varnamkhasti and L. A. Gonzalez. 2017. Analytical measurements of ultrasound propagation in dairy products: A review. Trends in Food Science & Technology 61: 38-48. https://doi.org/10.1016/j.tifs.2016.12.004
  101. Morita, K., Y. Ogawa, C. N. Thai and F. Tanaka. 2003. Soft X-ray image analysis to detect foreign materials in foods. Food Science and Technology Research 9(2): 137-141. https://doi.org/10.3136/fstr.9.137
  102. Munera, S., C. Besada, N. Aleixos, P. Talens, A. Salvador, D. W. Sun, S. Cubero and J. Blasco. 2017. Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. LWT - Food Science and Technology 77: 241-248. https://doi.org/10.1016/j.lwt.2016.11.063
  103. Musse, M., S. Quellec, M. Cambert, M. F. Devaux, M. Lahaye and F. Mariette, 2009. Monitoring the postharvest ripening of tomato fruit using quantitative MRI and NMR relaxometry. Postharvest Biology and Technology 53(1-2): 22-35. https://doi.org/10.1016/j.postharvbio.2009.02.004
  104. Neethirajan, S., D. S. Jayas and N. D. G. White. 2007. Detection of sprouted wheat kernel using soft X-ray image analysis. Journal of Food Engineering 81(3): 509-513. https://doi.org/10.1016/j.jfoodeng.2006.11.020
  105. Nogales-Bueno, J., J. M. Hernandez-Hierro, F. J. Rodriguez-Pulido and F. J. Heredia. 2014. Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: a preliminary approach. Food Chemistry 152(1): 586-591. https://doi.org/10.1016/j.foodchem.2013.12.030
  106. Noh, H. K. and R. Lu. 2007. Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality. Postharvest Biology and Technology 43(2): 193-201. https://doi.org/10.1016/j.postharvbio.2006.09.006
  107. Novoa-Carballal, R. and E. Fernandez-Megia, C. Jimenez and R. Riguera. 2011. NMR methods for unravelling the spectra of complex mixtures. Natural Product Reports 28: 78-98. https://doi.org/10.1039/C005320C
  108. Offermann, S., D. Bicanic, J. C. Krapez, D., Balageas, E., Gerkema and M. Chirtoc. 1998. Infrared transient thermography for noncontact, non-destructive inspection of whole and dissected apples and of cherry tomatoes at different maturity stages. Instrumentation Science and Technology 26(2-3): 145-155. https://doi.org/10.1080/10739149808002689
  109. Okochi T, Y. Hoshino, H., Fujii and T. Mitsutani. 2007. Nondestructive treering measurements for Japanese oak and Japanese beech using micro-focus X-ray computed tomography. Dendrochronologia 24(2-3): 155-164. https://doi.org/10.1016/j.dendro.2006.10.010
  110. Opara, U. L. and P. B. Pathare. 2014. Bruise damage measurement and analysis of fresh horticultural produce-a review. Postharvest Biology and Technology 91: 9-24. https://doi.org/10.1016/j.postharvbio.2013.12.009
  111. Otero, L. and G. Prestamo. 2009. Effects of pressure processing on strawberry studied by nuclear magnetic resonance. Innovative Food Science & Emerging Technologies 10(4): 434-440. https://doi.org/10.1016/j.ifset.2009.04.004
  112. Park, B., J. A. Abbott, K. J. Lee, C. H. Choi and K. H. Choi. 2003. Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of Delicious and Gala apples. Transactions of the ASAE 46(6): 1721-1731. https://doi.org/10.13031/2013.15628
  113. Patel, K. K., M. A. Khan and A. Kar. 2015. Recent developments in applications of MRI techniques for foods and agricultural produce-an overview. Journal of Food Science and Technology 52(1): 1-26. https://doi.org/10.1007/s13197-012-0917-3
  114. Peiris, K. H. S., C. G. Dull, R. G. Leffler and S. J. Kays. 1999. Spatial variability of soluble solids or dry-matter content within individual fruits, bulbs, or tubers: Implications for the development and use of NIR spectrometric technique. HortScience 34(1): 114-118.
  115. Peirs, A., N. Scheerlinck, K. Touchant and B. M. Nicolai. 2002. Comparison of fourier transform and dispersive near-infrared reflectance spectroscopy for apple quality measurements. Biosystems Engineering 81(3): 305-311. https://doi.org/10.1006/bioe.2001.0040
  116. Perez-Palacios, T., D. Caballero, T. Antequera, M. L. Duran, M. Avila and A. Caro. 2017. Optimization of MRI acquisition and texture analysis to predict physicochemical parameters of loins by data mining. Food Bioprocess Technology 10(4): 750-758. https://doi.org/10.1007/s11947-016-1853-4
  117. Pouliquen. D., D. Gross, V. Lehmann, D. Sylvie, D. Demilly and J. Lechappe. 1997. Study of water and oil bodies in seeds by nuclear magnetic resonance. Comptes Rendus de l'Académie des Sciences - Series III - Sciences de la Vie 320(2): 131-138 (In French, with English abstract).
  118. Qiao, J., N. Wang, M. O. Ngadi, A. Gunenc, M. Monroy, C. Gariepy and S.O. Prasher. 2007. Prediction of drip-loss, pH and color for pork using a hyperspectral imaging technique. Meat Science 76(1): 1-8. https://doi.org/10.1016/j.meatsci.2006.06.031
  119. Qin J, K. Chao, M. S. Kim. 2011. Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening. Journal of Food Engineering 107(3-4):277-288. https://doi.org/10.1016/j.jfoodeng.2011.07.021
  120. Rahman, A., L. M. Kandpal, S. Lohumi, M. S. Kim, H. Lee, C. Mo and B. K. Cho. 2017. Nondestructive estimation of moisture content, PH and soluble solid contents in intact tomatoes using hyperspectral imaging. Applied Sciences 7(1): 109. https://doi.org/10.3390/app7010109
  121. Rajkumar, P., N. Wang, G. Eimasry, G. S. V. Raghavan and Y. Gariepy. 2012. Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of Food Engineering 108(1): 194-200. https://doi.org/10.1016/j.jfoodeng.2011.05.002
  122. Renou, J. P., L. Foucat and J. M. Bonny. 2003. Magnetic resonance imaging studies of water interactions in meat. Food Chemistry 82(1): 35-39. https://doi.org/10.1016/S0308-8146(02)00582-4
  123. Renou, J. P., P. S. Belton and G. A. Webb. 2011. Magnetic resonance in food science: An exciting future. Cambridge: Royal Society of Chemistry. UK
  124. Ribeiro, F. R. B., L. O. Tedeschi, J. R. Stouffer and G. E. Carstens. 2008. Technical note: A novel technique to assess internal body fat of cattle by using real-time ultrasound. Journal of Animal Science 86(3): 763-767. https://doi.org/10.2527/jas.2007-0560
  125. Riyadi, S., A. A. A. Rahni, M. M. Mustafa and A.Hussain. 2007. Shape characteristics analysis for papaya size classification. In: The 5th Student Conference on Research and Development -SCOReD 2007, pp. 1-5, Malaysia.
  126. Roberts, R. T., 1993. High intensity ultrasonics in food processing. Chemistry and Industry 4: 119-21.
  127. Rodriguez-Pulido, F. J., D. F. Barbin, D. W. Sun, B. Gordillo, M. L. Gonzalez-Miret and F. J. Heredia. 2013. Grape seed characterization by NIR hyperspectral imaging. Postharvest Biology and Technology 76: 74-82. https://doi.org/10.1016/j.postharvbio.2012.09.007
  128. Ruiz-Altisent, M., G. P. Moreda. 2011. Encyclopedia of Agrophysics. In: Fruits, Mechanical Properties and Bruise Susceptibility, eds. J. Glinski, J. Horabik and J. Lipiec, pp. 318-321. Springer Netherlands.
  129. Ruiz-Altisent. M., L. Ruiz-Garcia, G. P. Moreda, R. Lu, N. Hernandez-Sanchez, E. C. Correa, B. Diezma, B. Nicolai and J. Garcia-Ramos. 2010. Sensors for product characterization and quality of specialty crops-A review. Computers and Electronics in Agriculture 74(2): 176-194. https://doi.org/10.1016/j.compag.2010.07.002
  130. Ruiz-Cabrera, M. A., P. Gou, L. Foucat, J. P. Renou and J. D. Daudin. 2004. Water transfer analysis in pork meat supported by NMR imaging. Meat Science 67(1): 169-178. https://doi.org/10.1016/j.meatsci.2003.10.005
  131. Saito, K., T. Miki, S. Hayashi, H. Kajikawa, M. Shimada, Y. Kawate, T. Nishizawa, D. Ikegaya, N. Kimura, K. Takabatake, N. Sugiura and M. Suzuki. 1996. Application of magnetic resonance imaging to non-destructive void detection in watermelon. Cryogenics, 36(12): 1027-1031. https://doi.org/10.1016/S0011-2275(96)00087-2
  132. Schmilovitch, Z., T. Ignat, V. Alchanatis, J. Gatker, V. Ostrovsky and J. Felfoldi. 2014. Hyperspectral imaging of intact bell peppers. Biosystems Engineering 117: 83-93. https://doi.org/10.1016/j.biosystemseng.2013.07.003
  133. Senthilkumar, T., D.S. Jayas, N.D.G. White, P.G. Fields and T. Grafenhan. 2017. Detection of ochratoxin a contamination in stored wheat using near-infrared hyperspectral imaging. Infrared Physics & Technology 81: 228-235. https://doi.org/10.1016/j.infrared.2017.01.015
  134. Shaarani, S. M., K. P. Nott and L. D. Hall. 2006. Combination of NMR and MRI quantitation of structure and structure changes for convection cooking of fresh chicken meat. Meat Science 72(3): 398-403. https://doi.org/10.1016/j.meatsci.2005.07.017
  135. Shahin, M. A., E. W. Tollner, R. D. Gitaitis, D. R. Sumner and B. W. Maw. 2002. Classification of sweet onions based on internal defects using image processing and neural network techniques. Transactions of the American Society of Agricultural Engineers 45(5): 1613-1618.
  136. Silva, S. R., J. J. Afonso, V. A. Santos, A. Monteiro, C. M. Guedes, J. M. T. Azevedo and A. Dias-da-Silva. 2006. In vivo estimation of sheep carcass composition using real-time ultrasound with two probes of 5 and 7.5 MHz and image analysis. Journal of Animal Science 84(12): 3433-3439. https://doi.org/10.2527/jas.2006-154
  137. Silva, S. R., M. J. Gomes, A. Dias-da-Silva, L. F. Gil and J. M. T. Azevedo, 2005. Estimation in vivo of the body and carcass chemical composition of growing lambs by real-time ultrasonography. Journal of Animal Science 83(2): 350-357. https://doi.org/10.2527/2005.832350x
  138. Sood, S., S. Mahajan, A. Doegar and A. Das. 2016. Internal crack detection in kidney bean seeds using X-ray imaging technique. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2258-2261, Jaipur.
  139. Suchanek, M., M. Kordulskab, Z. Olejniczakc, H. Figielb and K. Turekb. 2017. Application of low-field MRI for quality assessment of 'Conference' pears stored under controlled atmosphere conditions. Postharvest Biology and Technology 124:100-106. https://doi.org/10.1016/j.postharvbio.2016.10.010
  140. Suslick, K. S. 1988. Ultrasound: Its Chemical, Physical and Biological Effects. New York: VCH Publishers.
  141. Taglienti, A., R. Massantini, R. Botondi, F. Mencarelli and M. Valentini, 2009. Postharvest structural changes of Hayward kiwifruit by means of magnetic resonance imaging spectroscopy. Food Chemistry 114(4): 1583-1589. https://doi.org/10.1016/j.foodchem.2008.11.066
  142. Tao, Y. and J. Ibarra. 2000. Thickness-compensated X-ray imaging detection of bone fragments in deboned poultry-model analysis. Transactions of the ASAE 43(2): 453-459. https://doi.org/10.13031/2013.2725
  143. Tao, Y., Z. Chen, H. Jing and J. Walker. 2001. Internal inspection of deboned poultry using X-ray imaging and adaptive thresholding. Transactions of the ASAE 44(4):1005-1010.
  144. Theriault, M., C. Pomar and CF. W. astonguay. 2009. Accuracy of real-time ultrasound measurements of total tissue, fat and muscle depths at different measuring sites in lamb. Journal of Animal Science 87(5): 1801-1813. https://doi.org/10.2527/jas.2008-1002
  145. Thybo, A.K., S.N. Jespersen, P.E. Laerke and H.J. Stodkilde-Jorgensen, 2004. Nondestructive detection of internal bruise and spraing disease symptoms in potatoes using magnetic resonance imaging. Magnetic Resonance Imaging 22(9):1311-1317. https://doi.org/10.1016/j.mri.2004.08.022
  146. Tollner, E. W., M. A. Shahin, B. W. Maw, R. D. Gitaitis and D. R. Summer. 1999. Classification of onions based on internal defects using imaging processing and neural network techniques. ASAE Paper No. 993165. S t. Joseph, MI: ASAE.
  147. Vadivambal, R. and D. S. Jayas. 2011. Applications of Thermal Imaging in Agriculture and Food Industry-A Review. Food and Bioprocess Technology 4(2): 186-199. https://doi.org/10.1007/s11947-010-0333-5
  148. Vereycken, R., C. Bravo, H. Ramon, J. D. Baerdemaeker and V. V. Linden. 2003. Detection technique for tomato bruise damage by thermal imaging, Acta Horticulturae 599: 389-394.
  149. Wang, S. X., R. F. Hu, K. Gao, F. Wali, G. B. Zan, D. J. Wang, Z. Y. Pan and S. Q. Wei. 2017. Non-destructive study of fruits using grating-based X-ray imaging. Nuclear Science and Techniques 28: 24. https://doi.org/10.1007/s41365-016-0169-4
  150. Weschenfelder, A. V., L. Saucier, X. Maldague, L. M. Rocha, A. L. Schaefer and L. Faucitano. 2013. Use of infrared ocular thermography to assess physiological conditions of pigs prior to slaughter and predict pork quality variation. Meat Science 95(3): 616-620. https://doi.org/10.1016/j.meatsci.2013.06.003
  151. Williams, A. R. 2002. Ultrasound applications in beef cattle carcass research and management. Journal of Animal Science 80(2): 183-188. https://doi.org/10.2527/animalsci2002.80E-Suppl_2E183x
  152. Xia, T., S. Shi and X. Wan. 2006. Impact of ultrasonic-assisted extraction on the chemical and sensory quality of tea infusion. Journal of Food Engineering 74(4): 557-560. https://doi.org/10.1016/j.jfoodeng.2005.03.043
  153. Xiong, Z., D. W. Sun, H. Pu, W. Gao and Q. Dai. 2017. Applications of Emerging Imaging Techniques for Meat Quality and Safety Detection and Evaluation: A Review. Critical Reviews in Food Science and Nutrition 57(4): 755-768. https://doi.org/10.1080/10408398.2014.954282
  154. Yang, X., J. Fu, Z. Lou, L. Wang, G. Li, W. Freeman. 2006. Tea classification based on artificial olfaction using bionic olfactory neural network. Lecture Notes in Computer Science 3972: 343-348.
  155. Zhao, X., Y. Gao, X. Wang, C. Li, S. Wang and Q. Feng. 2016. Research on tomato seed vigor based on X-ray digital image. In: International Society for Optics and Photonics Asia, pp. 100200J-100200J.
  156. Zhu, Q., M. Huang, X. Zhao and S. Wang. 2013. Wavelength selection of hyperspectral scattering image using new semi-supervised affinity propagation for prediction of firmness and soluble solid content in apples. Food Analytical Methods 6(1):334-342. https://doi.org/10.1007/s12161-012-9442-2