Skip to main content

Advertisement

SpringerLink
Log in

X-ray detection of defects and contaminants in the food industry

  • Original Paper
  • Published:
Sensing and Instrumentation for Food Quality and Safety Aims and scope Submit manuscript

Abstract

The ability of X-rays to traverse through matter and reveal hidden contaminants or defects has led to their extensive use in manufacturing industries for quality control inspection. The difficulties inherent in the detection of defects and contaminants in food products have kept the use of X-ray in that industry limited mainly to the packaged foods sector. Nevertheless, the need for non-destructive internal product inspection has motivated a considerable research effort in this field spanning many decades. Improvements in technology, especially more compact and affordable high voltage power sources, high speed computing, and high resolution detector arrays, have made many X-ray detection tasks possible today that were previously unfeasible. These improvements can be expected to continue into the future. The purpose of this article is to give a review of research activity related to the use of X-ray imaging for the detection of defects and contaminants in agricultural commodities and discuss improvements in technology required to improve these detection capabilities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Commission of the European Communities White Paper on Food Safety. http://ec.europa.edu/dgs/health_consumer/library/pub/pub06_en.pdf. (2000). Accessed on 4-2-2007

  2. T.C. Pearson, Detection of pistachio nuts with closed shells using impact acoustics. Appl. Eng. Agric. 17, 1 (2001)

    Google Scholar 

  3. D.F. Bodies, in Food, ed. by M. Edwards (Woodhead Publishing Company, Cambridge, 2007)

    Google Scholar 

  4. P. Butz, C. Hofmann, B. Tauscher, Recent developments in noninvasive techniques for fresh fruit and vegetable internal quality analysis. J Food Sci 70, 131 (2005)

    Google Scholar 

  5. M. Graves, A. Smith, B.G. Batchelor, Approaches to foreign body detection in foods. Trends. Food Sci. Technol. 9, 21 (1998). doi:10.1016/S0924-2244(97)00003-4

    Article  CAS  Google Scholar 

  6. J.A. Abbott, R. Lu, B.L. Upchurch, R.L. Stroshine, Technologies for nondestructive quality evaluation of fruits and vegetables, in Horticultural Reviews Vol. 20, ed. by J. Janick (Wiley, Chichester, U.K., 1997)

  7. J.A. Abbott, Quality measurements of fruits and vegetables. Postharvest. Biol. Technol. 15, 207 (1999). doi:10.1016/S0925-5214(98)00086-6

    Article  Google Scholar 

  8. S.N. Jha, T. Matsuoka, Non-destructive techniques for quality evaluation of intact fruits and vegetables. Food Sci. Technol. Res. 6, 248 (2000). doi:10.3136/fstr.6.248

    Article  CAS  Google Scholar 

  9. M. Graves, B.G. Batchelor, S.C. Palmer, Three-dimensional X-ray inspection of food products. Applications of digital image, in Applications of Digital Image Processing XVII, SPIE Proceedings Vol. 2298, edited by A. G. Tescher (1994), p. 248

  10. G.G. Wagner, Combining X-ray imaging and machine vision, in Optics, illumination, and Image Sensing for Machine Vision II, SPIE Proceedings, ed. by D.J. Svetkoff, vol. 850 (Bellingham, WA, 1987), p. 45

  11. D.S. Jayas, C. Karunakaran, J. Paliwal, Grain quality monitoring using machine vision and soft X-ray for cereal grains, in Proceedings of the International Quality Grains Conference, Indianapolis, 2004

  12. X. Chen, H. Jing, Y. Tao, L. Carr, F. Wheaton, Real-time detection of physical hazards in de-bonded poultry using high resolution X-ray imaging, in Proceedings of the ASAE/CSAE Annual International Meeting, Las Vegas, 2003. Paper #033084

  13. K.S. Lim, M. Barigou, X-ray micro-computed tomography of cellular food products. Food Res. Int. 37, 1001 (2004). doi:10.1016/j.foodres.2004.06.010

    Article  Google Scholar 

  14. G. Martens, H. Bomsdorf, G. Harding, J. Kanzenbach, R. Linde, Coherent X-ray scatter for foodstuff contamination detection. SPIE 2092, 387 (1993). doi:10.1117/12.171258

    Article  Google Scholar 

  15. C.R. Bull, R. Zwiggelaar, Discrimination between low atomic number materials from their characteristic scattering of X-ray radiation. J. Agric. Eng. Res. 68, 77 (1997). doi:10.1006/jaer.1997.0181

    Article  Google Scholar 

  16. C.R. Bull, R. Zwiggelaar, R.D. Speller, Review of inspection techniques based on the elastic and inelastic scattering of X-rays and their potential in the food and agricultural industry. J. Food Eng. 33, 167 (1997). doi:10.1016/S0260-8774(97)88544-5

    Article  Google Scholar 

  17. Guide to X-ray inspection by safeline. http://www.Safelinexray.com/about-X-ray.html (2007). Accessed on 11-24-2007

  18. R.P. Haff, T.C. Pearson, Separating in-shell pistachio nuts from kernels using impact vibration analysis. Sens. Instrum. Food Qual. 1, 188 (2007). doi:10.1007/s11694-007-9020-2

    Article  Google Scholar 

  19. T.M. Brosnan, W.D. Daley, M.J.T. Smith, Comparison of image analysis techniques for defect detection in food-processing applications, in Optics in Agriculture, Forestry, and Biological Processing II, SPIE Proceedings, 1996, vol. 2907, ed. by G.E. Meyer, J.A. DeShazer, p. 186

  20. D.P. Casasent, M.A. Sipe, T.F. Schatzki, P.M. Keagy, L.C. Le, Neural net classification of X-ray pistachio nut data, in Optics in Agriculture, Forestry, and Biological Processing II, SPIE Proceedings, 1996, vol. 2907, ed. by G.E. Meyer, J.A. DeShazer, p. 217

  21. A. Talukder, D.P. Casasent, H.-W. Lee, P.M. Keagy, T.F. Schatzki, A new feature extraction method for classification of agricultural products from X-ray images, in Precision Agriculture and Biological Quality, SPIE Proceedings, 1998, vol. 3543, ed. by G.E. Meyer, J.A. DeShazer, p. 53

  22. Z. Chen, Y. Tao, H. Jing, J. Walker, Internal inspection of deboned poultry using X-ray imaging and adaptive thresholding. Trans. ASABE 44 (2001)

  23. C. Krger, C.M. Bartle, J.G. West, V.-H. Tran, Digital X-ray imaging and image processing for object detection in closed containers, in Proceedings of the Computer Graphics and Imaging Conference, Kauaii, 2004, ed. by M.H. Hamza. Paper #426-025

  24. M.A. Shahin, E.W. Tollner, S.E. Prussia, Filter design for optimal feature extraction from X-ray images. Trans. ASABE 42, 1879 (1999)

    Google Scholar 

  25. K.R. Castleman, Digital Image Processing (Prentice Hall, Englewood Cliffs, 1979)

    Google Scholar 

  26. R.P. Haff, X-ray inspection of wheat for granary weevils. Ph.D. thesis, University of California, Davis, 2001

  27. P. Horowitz, W. Hill, The Art of Electronics (Cambridge University Press, New York, 1989)

    Google Scholar 

  28. A. Rosenfeld, A.C. Kak, Digital Image Processing (Academic Press, Orlando, 1982)

    Google Scholar 

  29. A.K. Jain, Fundamentals of Digital Image Processing (Prentice Hall, Englewood Cliffs, 1989)

    Google Scholar 

  30. Z. Chen, Y. Tao, X. Chen, Y. Ying, Contrast enhancement of X-ray imaging and postprocessing for food internal inspection, in Proceedings of the ASAE/CSAE Annual International Meeting (Milwaukee, 2000). Paper #033084

  31. N. Kotwaliwale, J. Subbiah, P. Weckler, G. Brusewitz, G.A. Kranzler, Digital radiography for quality determination of small agricultural products: development and calibration of equipment, in Proceedings of the ASAE/CSAE Annual International Meeting (Las Vegas, 2003). Paper #036231

  32. Z. Chen, Y. Tao, C. Xin, Multiresolution local contrast enhancement of X-ray images for poultry meat inspection. Appl. Opt 40, 1195 (2001). doi:10.1364/AO.40.001195

    Article  CAS  Google Scholar 

  33. R. Zwiggelaar, C.R. Bull, M.J. Mooney, S. Czarnes, The detection of “soft” materials by selective energy X-ray transmission imaging and computer tomography. J. Agric. Eng. Res. 66, 203 (1997). doi:10.1006/jaer.1996.0134

    Article  Google Scholar 

  34. T.F. Schatzki, P.M. Keagy, Effect of image size and contrast on the recognition of insects, in Optics in Agriculture, SPIE Proceedings, 1991, vol. 1379, ed. by J.A. DeShazer G.E. Meyer, p. 182

  35. R. Zwiggelaar, C.R. Bull, M.J. Mooney, X-ray simulations for imaging applications in the agricultural and food industries. J. Agric. Eng. Res. 63, 161 (1996). doi:10.1006/jaer.1996.0018

    Article  Google Scholar 

  36. M. Graves, A. Smith, B.G. Batchelor, S.C. Palmer, Design and analysis of X-ray vision systems for high-speed detection of foreign body contamination in food, in Machine Vision Applications, Architectures, and Systems Integration III, SPIE Proceedings, ed. by B.G. Batchelor, S.S. Solomon, F.M. Waltz, vol. 2347 (1994), p. 80

  37. R.P. Haff, T.F. Schatzki, Image restoration of line-scanned X-ray images. Opt. Eng. 36, 3288 (1997). doi:10.1117/1.601568

    Article  Google Scholar 

  38. T.F. Schatzki, A. Grossman, R. Young, Recognition of agricultural objects by shape. IEEE. T. Pattern Anal. 5, 645 (1983)

    Article  Google Scholar 

  39. T.F. Schatzki, R. Young, R. Duryea, Detection of agricultural items by image processing, in Proceeds of the ASAE National Conference on Agricultural Electronics Applications, (1984), p. 631

  40. T.F. Schatzki, Detection of agricultural contraband in luggage. US. Patent #4,539,648 1985

  41. T.F. Schatzki, R. Young, X-Ray imaging of baggage for agricultural contraband. J. Transp. Eng. 114, 657 (1988)

    Article  Google Scholar 

  42. T.F. Schatzki, Effectiveness of screening methods for contraband recovery. J. Econ. Entomol. 84, 489 (1991)

    Google Scholar 

  43. T.F. Schatzki, R.Y. Wong, Detection of submilligram inclusions of heavy metals in processed foods. Food Technol.-Chicago 43, 72 (1989)

    CAS  Google Scholar 

  44. T.F. Schatzki, R. Young, R.P. Haff, J.G. Eye, G.R. Wright, Visual detection of particulates in X-ray images of processed meat products. Opt. Eng. 35, 2286 (1996). doi:10.1117/1.601010

    Article  Google Scholar 

  45. J.L. Cambier, D.C. Pasiak, On-Line X-ray inspection of packaged foods, in Proceedings of the SME Conference on Vision (Detroit, 1986), p. 1

  46. D.W. Penman, O.J. Olsson, D. Beach, Automatic X-ray inspection of canned products for foreign material, in Machine Vision Applications, Architectures, and Systems, SPIE Proceedings, ed. by B.G. Batchelor, S.S. Solomon, F.M. Waltz, vol. 1823 (1992), p. 342

  47. B.G. Batchelor, E.R. Davies, Using X-rays to detect foreign bodies. in Detecting foreign bodies in food, ed. by M. Edwards (Woodhead Publishing, Boca Raton, 2004), Chap. 13, p. 226

  48. D. Patel, E.R. Davies, I. Hannah, Towards a breakthrough in the detection of contaminants in food products. Sens. Rev. 15, 27 (1995). doi:10.1108/02602289510085570

    Article  Google Scholar 

  49. R. Dearden, Automatic X-ray inspection for the food industry. Food Sci.Technol. Today 10, 87 (1996)

    Google Scholar 

  50. Y. Ogawa, K. Morita, S. Tanaka, M. Setoguchi, C.N. Thai, Application of X-ray CT for detection of physical foreign materials in foods. Trans. ASAE 41, 157 (1998)

    Google Scholar 

  51. Y. Tao, J.G. Ibarra, Thickness-compensated X-ray imaging detection of bone fragments in deboned poultry-model analysis. Trans. ASABE 43, 453 (2000)

    Google Scholar 

  52. H. Jing, X. Chen, Y. Tao, Analysis of factors influencing the mapping accuracy of X-ray and laser range images in a bone fragment detection system, in Proceedings of the ASAE/CSAE Annual International Meeting (Las Vegas, 2003). Paper #036191

  53. C. Karunakaran, J. Paliwal, D.S. Jayas, N.D.G. White, Comparison of soft X-rays and NIR spectroscopy to detect insect infestations in grain, in Proceedings of the ASAE/CSAE Annual International Meeting (Tampa, 2005). Paper #053139

  54. M.D. Towes, T.C. Pearson, J.F. Campbell, Imaging and automated detection of Sitophilus oryzae (Coleoptera: Curculionidae) pupae in hard red winter wheat. J. Econ. Entomol. 99, 583 (2006)

    Article  Google Scholar 

  55. M.D. Towes, T.C. Pearson, J.F. Campbell, Rapid detection of internal insect infestations in bulk grain, in Proceedings of the Entomological Society of America Annual meeting and Exhibition (Fort Lauderdale, 2005). Paper No.1070

  56. P.M. Keagy, T.F. Schatzki, Effect of image resolution on insect detection in wheat radiographs. Cereal Chem. 68, 339 (1991)

    Google Scholar 

  57. T.F. Schatzki, T.A. Fine, Analysis of radiograms of wheat kernels for quality control. Cereal Chem. 65, 233 (1989)

    Google Scholar 

  58. R.P. Haff, D.C. Slaughter, Real-time X-ray inspection of wheat for infestation by the granary weevil, Sitophilus granarius (L.). Trans. ASAE 47, 531 (2004)

    Google Scholar 

  59. R.P. Haff, D.C. Slaughter, X-ray inspection of wheat for granary weevils. Real-time digital imaging vs. film in Proceedings of the ASAE/CSAE Annual International Meeting (Chicago, 2002). Paper #026093

  60. R.P. Haff, N. Toyofuku, An economical method for X-ray inspection of wheat, in Proceedings of the ASAE/CSAE Annual International Meeting (Milwaukee, 2000). Paper #003069

  61. P.M. Keagy, T.F. Schatzki, Machine recognition of weevil damage in wheat radiograms. Cereal Chem. 70, 696 (1993)

    Google Scholar 

  62. D.S. Jayas, C. Karunakaran, N.D.G. White, Soft X-ray image analysis to detect wheat kernels damaged by Plodia interpunctella (Lepidoptera: Pyralidae). Sci. Aliments 23, 180

  63. C. Karunakaran, D.S. Jayas, N.D.G. White, Soft X-ray inspection of wheat kernels infested by Sitophilus oryzae. Trans. ASAE 46, 739 (2003)

    Google Scholar 

  64. C. Karunakaran, D.S. Jayas, N.D.G. White, X-ray image analysis to detect infestations caused by insects in grain. Cereal Chem. 80, 553 (2003). doi:10.1094/CCHEM.2003.80.5.553

    Article  CAS  Google Scholar 

  65. C. Karunakaran, D.S. Jayas, N.D.G. White, Mass determination of wheat kernels from X-ray images, in Proceedings of the ASAE/CSAE Annual International Meeting (Ottawa, 2004). Paper #043120

  66. J.D. Hansen, R.P. Haff, D.W. Schlaman, W.L. Yee, Potential postharvest use of radiography to detect internal pests in deciduous tree fruits. J. Entomol. Sci. 40, 255 (2005)

    Google Scholar 

  67. T.F. Schatzki, R.P. Haff, I. Can, L.C. Le, N. Toyofuku, Defect detection in apples by means of X-ray imaging. Trans. ASAE 40, 1407 (1997)

    Google Scholar 

  68. T.F. Schatzki, R.P. Haff, R. Young, I. Can, L.C. Le, N. Toyofuku, Defect detection in apples by means of X-ray imaging, in Optics in Agriculture, Forestry, and Biological Processing II, SPIE Proceedings, ed. by G.E. Meyer, J.A. DeShazer, vol. 2907 (1996), p. 176

  69. T. Lin, H. Chang, K. Wu, et al, An adaptive image segmentation algorithm for X-ray quarantine inspection of selected fruits, in Proceedings of the ASAE/CSAE Annual International Meeting (Tampa, 2005). Paper #053123

  70. G.C. Marlow, W.H. Loeschner, Watercore. Hortic. Rev. (Am. Soc. Hortic. Sci.) 6, 189 (1984)

    CAS  Google Scholar 

  71. M.A. Shahin, E.W. Tollner, J.L. McClelland, Artificial intelligence classifiers for sorting apples based on watercore. J. Agric. Eng. Res. 79, 265 (2007). doi:10.1006/jaer.2001.0705

    Article  Google Scholar 

  72. S. Kim, T.F. Schatzki, Apple watercore sorting system using X-ray imagery: I. Algorithm development. Trans. ASAE 43, 1695 (2000)

    Google Scholar 

  73. M.A. Shahin, E.W. Tollner, M.D. Evans, H.R. Arabnia, Filter design for optimal feature extraction from X-ray images. Trans. ASAE 42, 1879 (1999)

    Google Scholar 

  74. E.W. Tollner, Y.C. Hung, B.L. Upchurch, S.E. Prussia, Relating X-ray absorption to density and water content in apples. Trans. ASAE 35, 1921 (1992)

    Google Scholar 

  75. P.M. Keagy, B. Parvin, T.F. Schatzki, Machine recognition of naval orange worm damage in X-ray images of pistachio nuts. Lebensm. Wiss. Technol. 29, 140 (1996)

    Article  CAS  Google Scholar 

  76. P.M. Keagy, T.F. Schatzki, L.C. Le, D.P. Casasent, D. Weber, Expanded image database of pistachio X-ray images and classification by conventional methods, in Optics in Agriculture, Forestry, and Biological Processing II, SPIE Proceedings, ed. by G. E. Meyer, J.A. DeShazer, vol. 2907 (1996), p. 196

  77. D.P. Casasent, M.A. Sipe, T.F. Schatzki, P.M. Keagy, L.C. Le, Neural net classification of X-ray pistachio nut data. Lebensm. Wiss. Technol. 31, 122 (1998)

    Article  CAS  Google Scholar 

  78. A. Sim, B. Parvin, P.M. Keagy, Invariant representation and hierarchical network for inspection of nuts from X-ray images, in Proceedings of the IEEE International Conference on Neural Networks, vol. 2 (Perth, 1995), p. 738

  79. D.P. Casasent, A. Talukder, P.M. Keagy, T.F. Schatzki, Detection and segmentation of items in X-ray imagery. Trans. ASAE 44, 337 (2001)

    Google Scholar 

  80. S. Kim, T.F. Schatzki, Detection of pinholes in almonds by X-ray imaging. Trans. ASAE 44, 997 (2001)

    Google Scholar 

  81. R.D. Harrison, W.A. Gardner, E.W. Tollner, D.J. Kinard, X-ray computed tomography studies of the burrowing behavior of the fourth-instar pecan weevil(coleoptera:Curculionidae). J. Econ. Entomol. 86, 1714 (2007)

    Google Scholar 

  82. J.K. Brecht, R.L. Shewfelt, J.C. Garner, E.W. Tollner, Using X-ray computed tomography (X-ray CT) to nondestructively determine maturity of green tomatoes. HortScience 26, 45 (1991)

    Google Scholar 

  83. E.G. Barcelon, S. Tojo, K. Watanabe, X-rat CT imaging and quality detection of peach at different physiological maturity. Trans. ASABE 42, 435 (2007)

    Google Scholar 

  84. J. Lammertyn, B. Nicolai, P. Jancsok, J. De Baerdemaeker, T. Dresselaers, P. Van Hecke, X-ray CT and magnetic resonance imaging to study the development of core breakdown in ‘Conference’ pears, in Proceedings of the ASAE/CSAE Annual International Meeting (Sacramento, 2001). Paper #016037

  85. L. Sonego, R. Ben-Arie, J. Raynal, J.C. Pech, Biochemical and physical evaluation of textural characteristics of nectarines exhibiting woolly breakdown: NMR imaging, X-ray computed tomography. Postharvest Biol. Technol. 5, 187 (1995). doi:10.1016/0925-5214(94)00026-O

    Article  Google Scholar 

  86. E.W. Tollner, R.D. Gitaitis, K.W. Seebold, B.W. Maw, Experiences with a food product X-ray inspection system for classifying onions. Appl. Eng. Agric. 21, 907 (2005)

    Google Scholar 

  87. M.A. Shahin, E.W. Tollner, R.D. Gitaitis, D.R. Sumner, B.W. Maw, Classification of sweet onions based on internal defects using image processing and neural network techniques. Trans. ASAE 21, 907 (2002)

    Google Scholar 

  88. E.W. Tollner, S.G. Pandalai, Efficacy and Economics of Placing X-ray machines in an onion packinghouse. Recent Res. Dev. Crop Sci. 1, 55 (2003)

    Google Scholar 

  89. P. Thomas, A. Kannan, V.H. Degwekar, M.S. Ramamurthy, Non-destructive detection of seed weevil-infested mango fruits by X-ray imaging. Postharvest Biol. Technol. 5, 161 (1995). doi:10.1016/0925-5214(94)00019-O

    Article  Google Scholar 

  90. R.P. Haff, D.C. Slaughter, Y. Sarig, A. Kader, X-ray assessment of translucency in pineapple. J. Food Process. Preserv. 30, 527 (2006). doi:10.1111/j.1745-4549.2006.00086.x

    Article  Google Scholar 

  91. R.E. Nylund, I.M. Lutz, Separation of hollow heart potato tubers by mean of size grading, specific gravity and X-ray examination. Am. Potato J. 27, 214 (1950). doi:10.1007/BF02850188

    Article  Google Scholar 

  92. E.S. Jackson, R.P. Haff, X-ray detection of and sorting of olives damaged by fruit fly, in Proceedings of the ASAE/CSAE Annual International Meeting (Portland, 2006). Paper #066062

  93. R.P. Haff, Real-time correction of distortion in X-ray images of cylindrical or spherical objects and its application to agricultural commodities. Trans. ASABE 51, 341 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. USDA-ARS-WRRC, 800 Buchanan St., Albany, CA, 94710, USA

    Ronald P. Haff & Natsuko Toyofuku

Authors
  1. Ronald P. Haff
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natsuko Toyofuku
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ronald P. Haff.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Haff, R.P., Toyofuku, N. X-ray detection of defects and contaminants in the food industry. Sens. & Instrumen. Food Qual. 2, 262–273 (2008). https://doi.org/10.1007/s11694-008-9059-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11694-008-9059-8

Keywords

Search

Navigation