Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-24T12:52:41.742Z Has data issue: false hasContentIssue false
  • English
  • Français

Non-destructive measurements of the egg quality

Published online by Cambridge University Press:  18 September 2007

B. De Ketelaere ,
F. Bamelis ,
B. Kemps ,
E. Decuypere  and
J. De Baerdemaeker
B. De Ketelaere*
Affiliation:
KU Leuven, Faculty of Agricultural and Applied Biological Sciences, Egg Quality and Incubation Research Group, Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
F. Bamelis
Affiliation:
KU Leuven, Faculty of Agricultural and Applied Biological Sciences, Egg Quality and Incubation Research Group, Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
B. Kemps
Affiliation:
KU Leuven, Faculty of Agricultural and Applied Biological Sciences, Egg Quality and Incubation Research Group, Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
E. Decuypere
Affiliation:
KU Leuven, Faculty of Agricultural and Applied Biological Sciences, Egg Quality and Incubation Research Group, Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
J. De Baerdemaeker
Affiliation:
KU Leuven, Faculty of Agricultural and Applied Biological Sciences, Egg Quality and Incubation Research Group, Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
*
*Corresponding author: e-mail: [email protected]
Get access

Abstract

Due to the increasing throughput of modern egg grading machines, which grade up to 120 000 eggs per hour, the visual inspection of eggs by humans (“candling”), becomes a critical bottleneck in the egg sorting chain. In order to assure a high and consistent egg quality, researchers investigated the use of modern sensor technologies to replace the candling operation. During the last decades, several types of sensors were developed, and it is believed that these sensors will replace human candling in the near future. A first class of sensors is based on mechanical techniques and allows investigation of the physical shell quality, such as the presence of cracks and shell strength. A second class is based upon spectroscopic principles and allows the operator to “see” through the egg shell in order to determine the internal quality of the eggs, such as albumen pH and viscosity and the presence of inclusions such as blood and meat spots. A third class of sensors aims at mimicking the human eye by means of a camera and a software platform (“computer vision”). Besides these types of sensors, some others based on ultrasonic, magnetic resonance and electronic nose principles are investigated and discussed. This paper gives an overview of these modern sensor technologies for egg grading.

Keywords

egg qualitynon-destructive sensors
Type
Reviews
Information
World's Poultry Science Journal , Volume 60 , Issue 3 , September 2004 , pp. 289 - 302
Copyright
Copyright © Cambridge University Press 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This paper was first presented at the Xth European Symposium on the Quality of Eggs and Egg Products, Saint-Brieuc, Ploufragan, France, September 23-26, 2003

References

Abdallah, A.G., Harms, R.H. and El-Husseiny, O. (1993) Various methods of measuring shell quality in relation to percentage of cracked eggs. Poultry Science 72: 2038–2043.CrossRefGoogle Scholar
Bain, M., Dunn, L.C., Edmond, A., Wilson, P.W., Joseph, N., Waddington, D., Solomon, S., De Ketelaere, B., De Baerdemaeker, J. and Preisinger, R. (2003) Marker assisted selection to improve eggshell quality: establishing the association between egg phenotypic traits and ‘candidate genes’. Proceedings of the WPSA conference,Ploufragan, France:126–131.Google Scholar
Bamelis, F. (1999) Verklaring voor het veranderend trillingsgedrag van broedeieren tijdens de vroege incubatie. Master thesis, Katholieke Universiteit Leuven, Belgium (in Dutch).Google Scholar
Bamelis, F., De Ketelaere, B., Decuypere, E. and De Baerdemaeker, J. (2002) Acoustic resonance analysis of eggs as a technique to measure heat stress on laying hens. Second workshop on smart technologies in livestock–monitoring. Braunschweig, Germany.Google Scholar
Bamelis, F.R., Tona, K., De Baerdemaeker, J.G. and Decuypere, E.M. (2002) Detection of early embryonic development in chicken eggs using visible light transmission. British Poultry Science 43: 922–928.CrossRefGoogle ScholarPubMed
Bliss, G.N. (1973) Crack detector. United States Patent n° 3744299.Google Scholar
Brant, A.W., Norris, K.H. and Chin, G. (1953) A spectrophotometric method for detecting blood in white shelled eggs. Poultry Science 32: 357–363.Google Scholar
Capozzi, F., Cremonini, M.A., Luchinat, C., Placucci, G. and Vignali, C. (1999) A low frequency 1H-NMR external unit for the analysis of large food stuff samples. Journal of Magnetic Resonance 138: 277–280.CrossRefGoogle Scholar
Cho, H.K., Choi, W.K. and Paek, J.H. (2000) Detection of surface cracks in shell eggs by acoustic impulse method. Transaction of the ASAE 43(6): 1921–1926.CrossRefGoogle Scholar
Coucke, P. (1998) Assessment of some physical egg quality parameters based on vibration analysis. PhD thesis, Katholieke Universiteit Leuven, Belgium.Google Scholar
Coucke, P., Dewil, E., Decuypere, E. and De Baerdemaeker, J. (1999) Measuring the mechanical stiffness of an eggshell using resonant frequency analysis. British Poultry Science 40: 227–232.CrossRefGoogle ScholarPubMed
Das, K. and Evans, M.D. (1992a) Detecting fertility of hatching eggs using machine vision II: Histogram characterization method. Transactions of the ASAE 35(4): 1135–1341.Google Scholar
Das, K. and Evans, M.D. (1992b) Detecting fertility of hatching eggs using machine vision II: neural network classifiers. Transactions of the ASAE 35(6): 2035–2041.CrossRefGoogle Scholar
De Ketelaere, B., Coucke, P. and De Baerdemaeker, J. (2000) Eggshell crack detection based on acoustic resonance frequency analysis. Journal of Agricultural Engineering Research 76(2): 157–163.CrossRefGoogle Scholar
De Ketelaere, B. and De Baerdemaeker, J. (2000) PART II: Using high–resolution resonant frequency and damping estimators for the non–destructive, on–line determination of the eggshell strength. Proceedings of the 1st international workshop on noise and vibration in agricultural and biological engineering: 149–154.Google Scholar
De Ketelaere, B. (2002) Data analysis for the non-destructive quality assessment of agro-products using vibration measurements. PhD Thesis, Katholieke Universiteit Leuven, Belgium.Google Scholar
Dunn, I.C., Bain, M., Edmond, A., Wilson, P.W., Joseph, N., Solomon, S., De Ketelaere, B., De Baerdemaeker, J., Preisinger, R. and Waddington, D. (2003 a) Heritability and genetic correlation of measurements derived from resonant frequency analysis; a novel method of determining egg shell quality in domestic hens. Proceedings of the 3rd European Poultry Genetics Symposium,Wageningen, The Netherlands,.Google Scholar
Dunn, I.C., Bain, M., Edmond, A., Wilson, P.W., Joseph, N., Solomon, S., De Ketelaere, B., De Baerdemaeker, J., Preisinger, R. and Waddington, D. (2003 b) Heritability and genetic correlation of measurements derived from acoustic resonance frequency analysis; a novel method of determining egg shell quality in domestic hens. British Poultry Science, submitted.Google Scholar
Dutta, R., Hines, E.L., Gardner, J.W., Udrea, D.D. and Boilot, P. (2003) Non-destructive egg freshness determination: an electronic nose based approach. Measurement Science and Technology 14: 190–198.CrossRefGoogle Scholar
Elster, R.T. and Goodrum, J.W. (1991) Detection of Cracks in Eggs Using Machine Vision. Transactions of the ASAE 30(1): 307–312.CrossRefGoogle Scholar
Feyaerts, F., Vanroose, P., Fransens, R. and Van Gool, L. (2001) Using shape to correct for observed non-uniform color in automated egg-grading. Proceedings of the SPIE Electronic Imaging Conference, machine vision applications in industrial inspection IX,San Jose (CA):93–101.Google Scholar
Garcia-Alegre, M.C., Enciso, J., Ribeiro, A. and Guinea, D. (1997) Towards an automatic visual inspection of eggshell defects. Proceedings of the International Workshop on Robotics and Automated Machinery for Bio-Productions,Gandia, Spain:51–56.Google Scholar
Giangiacomo, R. and Dull, G.G. (1986) NIR spectrophotometric determination of individual sugars in aqueous mixtures. Journal of Food Science 51: 679–683.CrossRefGoogle Scholar
Gielen, R.M.A.M., De Jong, L.P. and Kerkvliet, H.M.M. (1979) Electro-optical blood-spot detection in intact eggs. IEEE Transactions on Instrumentation and Measurements IM-28(3): 177–183.CrossRefGoogle Scholar
Goodrum, J.W. and Elster, R.T. (1992) Machine Vision for Crack Detection in Rotating Eggs. Transactions of the ASAE 35(4): 1323–1328.CrossRefGoogle Scholar
Hamilton, R.M.G. (1982) Methods and factors that affect the measurement of eggshell quality. Poultry Science 61: 2022–2039.CrossRefGoogle Scholar
Han, Y.J. and Feng, Y. (1994) Eggshell inspection using global image analysis. Applied Engineering in Agriculture 10(1): 109–114.CrossRefGoogle Scholar
Haugh, R.R. (1937) The Haugh unit for measuring egg quality. United States Egg. Poultry Magazine 43: 522–555, 572–573.Google Scholar
Hutchison, M.J., Lirette, A., Etches, R.J.Towner, R.A. and Janzen, E.G. (1992) An assessment of egg yolk structure using magnetic resonance imaging. Poultry Science 71: 2117–2121.CrossRefGoogle ScholarPubMed
Jindal, V.K. and Sritham, E. (2003) Detecting Eggshell Cracks by Acoustic Impulse Response and Artificial Neural Networks. ASAE Annual International Meeting, Las Vegas,USA,27–30 July,.Google Scholar
Kemps, B.J., De Ketelaere, B., Bamelis, F.R., Decuypere, E.M. and De Baerdemaeker, J.G. (2003) Vibration analysis on incubation eggs and its relation to embryonic development. Biotechnology Progress 19: 1022–1025.CrossRefGoogle ScholarPubMed
Kemps, B., Bamelis, F., De Ketelaere, B., Mertens, K., Decuypere, E. and De Baerdemaeker, J. (2004) Detection of egg freshness by NIR. (unpublished results).Google Scholar
Kennedy, G.Y. and Vevers, H.G. (1973) Eggshell pigments of the Araucana fowl. Comparative Biochemistry and Physiology 44B: 11–25.Google Scholar
Kuchida, K., Fukaya, M., Miyoshi, S., Uzuki, M. and Tsuruta, S. (1999) Nondestructive Prediction Method for Yolk: Albumen Ratio in Chicken Eggs by Computer Image Analysis. Poultry Science 78: 909–913.CrossRefGoogle ScholarPubMed
Lang, M.R. and Wells, J.W. (1987) A review of eggshell pigmentation. Worlds Poultry Science Journal 43: 238–246.CrossRefGoogle Scholar
Lucisano, M., Hidalgo, A., Comelli, E.M. and Rossi, M. (1996) Evolution of chemical and physical albumen characteristics during the storage of shell eggs. Journal of Agricultural and Food Chemistry 44: 1235–1240.CrossRefGoogle Scholar
Moayeri, H. (1996) Probe, device and method for testing eggs. U.S. Patent 5,728,939.Google Scholar
Narushin, V.G., Van Kempen, T.A., Wineland, M.J. and Christensen, V.L. (2004) Comparing Infrared Spectroscopy and Egg Size Measurements for predicting Eggshell Quality. Biosystems Engineering, in press.CrossRefGoogle Scholar
Norris, K.H. (1996) History of NIR. Journal of Near Infrared Spectroscopy 4: 31–37.Google Scholar
Patel, V.C., Mcclendon, R.W. and Goodrum, J.W. (1994) Color Computer Vision and Artificial Neural Networks for the Detection of Defects in Poultry Eggs. Artificial Intelligence Review 12: 163–176.CrossRefGoogle Scholar
Patel, V.C., Mcclendon, R.W. and Goodrum, J.W. (1998) Crack Detection in Eggs Using Computer Vision and Neural Networks. AI Applications 8(2): 21–31.Google Scholar
Patel, V.C., Mcclendon, R.W. and Goodrum, J.W. (1996) Detection of Blood Spots and Dirt Stains in Eggs Using Computer Vision and Neural Networks. Applied Engineering in Agriculture 12(2): 253–258.CrossRefGoogle Scholar
Pitsilis, J.G., Brooker, D.B., Walton, H.V. and Cotteril, O.J. (1984) Rheological properties of liquid egg white. Transactions of the ASAE 27: 300–304.CrossRefGoogle Scholar
Schmilovitch, Z., Hofmann, A., Egozi, H. and Klein, E. (2002) Determination of egg freshness by NNIRS (Near-Near Infrared Spectroscopy). Proceedings of the AgEng Conference,Budapest.Google Scholar
Schwagele, F., Poser, R. and Krockel, L. (2001) Application of low-resolution NMR spectroscopy of intact eggs - Measurement of quality determining physical characteristics. Fleischwirtschaft 81(10): 103–106.Google Scholar
Schwagele, F., Poser, R. and Krockel, L. (2001) Application of low-resolution NMR spectroscopy for determining internal egg quality – correlations between relaxation time T2(2) and internal egg quality parameters.Google Scholar
Sinha, D.N., Johnston, R.G., Grace, W.K. and Lemanski, C.L. (1992) Acoustic resonances in chicken eggs. Biotechnology Progress 8: 240–243.CrossRefGoogle Scholar
Tung, M.A., Richards, J.F. and Morrison, B.C. (1970) Rheology of fresh, aged and gamma irradiated egg white. Journal of Food Science 35: 872–874.CrossRefGoogle Scholar
Voisey, P.W. and Hunt, J.R. (1967) Relationship between applied force deformation of eggshells and fracture force. Journal of Agricultural Engineering Research 12(1): 1–4.CrossRefGoogle Scholar
Voisey, P.W. and Hamilton, R.M.G. (1976) Ultrasonic measurement of eggshell thickness. Poultry Science 55: 1319–1324.CrossRefGoogle Scholar
Wei, R. and Bitgood, J.J. (1989) A new objective measurement of eggshell color. 1. A test for potential usefulness of two color measuring devices. Poultry Science 69: 1175–1780.Google Scholar
Williams, P.C. and Norris, K.H. (1987) Near infrared technology in the agricultural and food industries. American Association of Cereal Chemists, Inc., St. Paul, MN, USA. pp. 169–180, 201–208.Google Scholar
Worley, J.W. and Goodrum, J.W. (1995) Strobe versus incandescent lighting for egg crack detection using machine vision. Transactions of the ASAE 11(4): 549–554.Google Scholar