Skip to main content
SpringerLink
Log in

Non-destructive Quality Assessment of Table Eggs for Online Sorting

  • Chapter
  • First Online:
Informatics in Poultry Production

Abstract

The increase in egg production among agricultural and livestock products over the past 50 years has been very significant. Most external properties are related to the eggshell, including the integrity of the shell, color, thickness, porosity, and strength. Internal quality is related to egg albumin properties. The quality of albumin decreases with increasing storage time due to biochemical reactions, which are managed by complex internal and external factors, such as temperature and relative humidity, as well as the presence of bacteria. In current industrial practices, table egg grading is performed based on size and mass. It is essential to ensure the internal quality and healthiness of the table eggs before packing. Therefore, non-destructive testing methods such as acoustic emission, electronic nose, machine vision, and spectral method will be discussed in this chapter which plays an essential role in the online sorting system based on the internal and external quality of eggs. Acoustic emission and spectral methods are detected by collecting information on the inner contents of eggs. Machine vision is detected from the surface information of eggs, including shape and color. This chapter describes recent research on non-destructive quality evaluation of egg.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdanan Mehdizadeh, S., Minaei, S., Hancock, N. H., & Karimi Torshizi, M. A. (2014). An intelligent system for egg quality classification based on visible infrared transmittance spectroscopy. Information Processing Agriculture, 1(2), 105–114.

    Google Scholar 

  2. Abdanan Mehdizadeh, S., & Nadi, F. (2016). Experimental and Numerical Analysis for Prediction of Mechanical Properties of Eggshell. Journal of Food Engineering, 12(2), 1–7.

    Google Scholar 

  3. Aboonajmi, M., & Faridi, H. (2020). In quality assessment methods and postharvest handling of fresh poultry eggs. Boca Raton, FL, USA: CRC Press.

    Book  Google Scholar 

  4. Aboonajmi, M., & Abbasian Najafabadi, T. (2014). Prediction of poultry egg freshness using Vis-Nir spectroscopy with maximum likelihood method. International Journal of Food Properties, 17(10), 2166–2176.

    Article  Google Scholar 

  5. Aboonajmi, M., Akram, A., Nishizu, T., Kondo, N., Setarehdan, S. K., & Rajabipour, A. (2010). An ultrasound based technique for the determination of poultry egg quality. Research in Agriculture Engineering, 56(10), 26–32.

    Article  Google Scholar 

  6. Aboonajmi, M., Saberi, A., Abbasian Najafabadi, T., & Kondo, N. (2016). Quality assessment of poultry egg based on Vis- NIR Spectroscopy and RBF networks. International Journal of Food Properties, 19(5), 1163–1172.

    Article  CAS  Google Scholar 

  7. Aboonajmi, M., Jahangiri, M., & Hassan-Beygi, S. R. (2015). A review on application of acoustic analysis in quality evaluation of agro-food products. Journal of Food Processing and Preservation, 39, 3175–3188.

    Article  CAS  Google Scholar 

  8. Anton, M., Martinet, V., Dalgalarrondo, M., Beaumal, V., David Briand, E., & Rabesona, H. (2003). Chemical and structural characterization of low-density lipoproteins purified from hen egg yolk. Food Chemistry, 83(2), 175–183.

    Article  CAS  Google Scholar 

  9. Ariana, D. P., Shrestha, B. P., & Guyer, D. E. (2006). Integrating reflectance and fluorescence imaging for apple disorder classification. Computer Electronics Agriculture, 50, 148–161.

    Article  Google Scholar 

  10. Arivazhagan, S., Shebiah, R. N., Sudharsan, H., Kannan, R. R., & Ramesh, R. (2013). External and internal defect detection of egg using machine vision. Journal of Emerging Trends in Computing and Information Science, 4(3), 257–262.

    Google Scholar 

  11. Bamelis, F. (2003). Noninvasive assessment of eggshell conductance and different developmental stages during incubation of eggs. PhD thesis, No. 587, FLTWB, KU Leuven.

    Google Scholar 

  12. Behboodi, S. A. Aboonajmi, & Hassan-Beygi, S. R. (2020). Non-destructive assessment of the egg quality, the 6th Iranian International NDT Conference Feb. 17–18, 2020, Civil Aviation Technology College, Tehran, Iran.

    Google Scholar 

  13. Buckin, V., O’Driscoll, B., Smyth, C., Alting, A. C., & Visschers, R. W. (2003). Ultrasonic spectroscopy for materials analysis: Recent advances. Spectroscopy Europe, 15(1), 20–25.

    CAS  Google Scholar 

  14. Chen, H., Tan, C., & Lin, Z. (2019). Non-destructive identification of native egg by near-infrared spectroscopy and data driven-based class-modeling. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 206, 484–490.

    Google Scholar 

  15. Chung-wei, L., Jui-chun, C., Ching-wei, C., & Li-cheng, H. (2011). A novel non-destructive technology for inspecting eggshell cracks using impulsive response time, 17(1), 1–10.

    Google Scholar 

  16. Dehrouyeh, M. H., Omid, M., Ahmadi, H., Mohtasebi, S. S., & Jamzad, M. (2010). Grading and quality inspection of defected eggs using machine vision. International Journal of Advanced Science Technology, 17(4), 23–31.

    Google Scholar 

  17. Duizer, L. M. (2004). Sound input techniques for measuring texture. In K. David (Ed.), Texture in food (pp. 146–166). Woodhead Publishing Ltd.

    Chapter  Google Scholar 

  18. Giunchi, A., Berardinelli, A., Ragni, L., Fabbri, A., & Silaghi, F. A. (2008). Non-destructive freshness assessment of shell eggs using FT-NIR spectroscopy. Journal of Food Engineering, 89(2), 142–148.

    Article  CAS  Google Scholar 

  19. Gonzalez, R. C., & Richard, E. (2002). Woods, digital image processing. Ed: Prentice Hall Press. ISBN 0-201-18075-8.

    Google Scholar 

  20. Guanjun, B., Mimi, J., Yi, X., Shibo, C., & Qinghua, Y. (2019). Cracked egg recognition based on machine vision. Computers and Electronics in Agriculture, 158, 159–166.

    Article  Google Scholar 

  21. Hester, P. Y. (2017). Egg innovations and strategies for improvements. Elsevier Inc.

    Google Scholar 

  22. Hincke, M. T., Nys, Y., Gautron, J., & Mann, K. (2012). The eggshell: Structure, composition and mineralization. Frontier Bioscience Landmark Edition, 17, 1266–1280.

    Article  CAS  Google Scholar 

  23. Jenshinn Lin, Y., Lin, M., Hsieh, C., & Yang. (2001). An automatic system for eggshell quality monitoring. ASAE Annual Meeting. Paper No. 016032.

    Google Scholar 

  24. Karoui, R., Kemps, B., Bamelis, F., De Ketelaere, B., Mertens, K., Schoonheydt, R., Decuypere, E., & De Baerdemaeker, J. (2006). Development of a rapid method based on front face fluorescence spectroscopy for the monitoring of egg freshness: 2-evolution of yolk. European Food Research and Technology, 223(2), 180–188.

    Article  CAS  Google Scholar 

  25. Kemps, B., Bamelis, F., De Ketelaere, B., Mertens, K., Tona, K., Decuypere, E., & De Baerdemaeker, G. J. (2006). Visible transmission spectroscopy for the assessment of egg freshness. Journal of the Science of Food and Agriculture, 86, 1399–1406.

    Article  CAS  Google Scholar 

  26. Khaliduzzaman, A., Konagaya, K., Suzuki, T., Kashimori, A., Kondo, N., & Ogawa, Y. (2020). A Nondestructive eggshell thickness measurement technique using terahertz waves. Science and Reports, 10, 1052.

    Article  CAS  Google Scholar 

  27. Khaliduzzaman, A., Fujitani, S., Kondo, N., Ogawaa, Y., Fujiura, T., Suzuki, T., Kashimori, A., Syduzzaman, M., & Rahman, A. (2019). Non-invasive characterization of chick embryo body and cardiac movements using near infrared light. Engineering in Agriculture, Environment and Food, 12, 32–39.

    Article  Google Scholar 

  28. Khaliduzzamana, A., Fujitanib, S., Kashimorib, A., Suzukia, T., Ogawaa, Y., & Kondo, N. (2019). A non-invasive diagnosis technique of chick embryonic cardiac arrhythmia using near infrared light. Computers and Electronics in Agriculture, 158, 326–334.

    Article  Google Scholar 

  29. Patel, K. K., Kar, A., Jha, S. N., & Khan, M. A. (2012). Machine vision system: A tool for quality inspection of food and agricultural products. Food Science and Technology, 49(2), 121–141.

    Google Scholar 

  30. Laugier, P., & Hayat, G. (2011). Introduction to the physics of ultrasound. Bone Quantitative Ultrasound Ed. 468pp Springer.

    Google Scholar 

  31. Lesnierowski, G., & Stangierski, J. (2018). What’s new in chicken egg research and technology for human health promotion? A review. Trends in Food Science & Technology, 71, 46–51.

    Article  CAS  Google Scholar 

  32. Li, J. T., Zhu, S. S., Jiang, S., & Wang, J. (2017). Prediction of egg storage time and yolk index based on electronic nose combined with chemometric methods. LWT - Food Science and Technology, 82, 369–376.

    Article  CAS  Google Scholar 

  33. Li, X. C., Zhao, D. L., Shi, H. L., & Yun, Y. L. (2019). Non-destructive testing method of egg quality based on machine vision. Journal of Food Safety and Quality, 10(2):489–493.

    Google Scholar 

  34. Lin, H., Zhao, J. W., Sun, L., Chen, Q. S., & Zhou, F. (2011). Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis. Innovative Food Science Emerging Technology, 12(2), 182–186.

    Article  Google Scholar 

  35. Lin, J., Lin, Y., Hsieh, M., & Yang, C. (2001). An automatic system for eggshell quality monitoring. Trans of ASABE, 44(3), 1323–1328.

    Google Scholar 

  36. Lin, L., Xu, P. T., Sun, L., Bi, X. K., Zhao, J. W., & Cai, J. R. (2018). Identification of eggshell crack using multiple vibration sensors and correlative information analysis. Food Process Engineering, 9(8).

    Google Scholar 

  37. Liu, M., Pan, L.Q., Tu, K., & Liu, P. (2010). Determination of egg freshness during shelf life with electronic nose Nongye Gongcheng Xuebao. Transaction Chinese Social Agriculture Engineering, 26(4), 317–321.

    Google Scholar 

  38. Liu, Y., Li, Q. W., Huang, X. W., Huo, G. Y., & Zhou, Y. (2015). Egg characteristics extraction from light transmission image and egg freshness model training. Science Technology Engineering, 15(25), 72–77.

    Google Scholar 

  39. Lu, R., & Peng, Y. (2006). Hyperspectral scattering for assessing peach fruit firmness. Biosystems Engineering, 96, 161–171.

    Article  Google Scholar 

  40. Mason, T. J., & Luche, J. L. (1996). Ultrasound as a new tool for synthetic chemists. In R. Van Eldik & C. D. Hubbard (Eds.), Chemistry under extreme or non-classical conditions (pp. 317–380). John Wiley & Sons Inc. and Spektr um Akademischer Verlag.

    Google Scholar 

  41. Mcclements, D. J. (1995). Advances in the application of ultrasound in food analysis and processing. Trends in Food Science & Technology, 6, 293–299.

    Article  CAS  Google Scholar 

  42. Moba. (2019). Forta gt100 machine for grading and packing eggs. Retrieved from https://www.moba.net/page/cn/Products/Detail/forta/1535.

  43. Møller, U., Cooke, D. G., Tanaka, K., & Jepsen, P. U. (2009). Terahertz reflection spectroscopy of Debye relaxation in polar liquids [Invited]. Journal of the Optical Society of America B: Optical Physics, 26, A113.

    Article  Google Scholar 

  44. Moons, E., Sinnaeve, G., & Dardenne, P. (2000). Non-destructive visible and NIR spectroscopy measurement for determination of apple internal quality. Acta Horticulturae, 517, 441–448.

    Article  Google Scholar 

  45. Nakano, K., Sasaoka, K., & Ohtsuka, Y. (1998). A study on non-destructive detection of abnormal eggs by using image processing. South African Statistical Journal, 29(3), 17–23.

    Google Scholar 

  46. Nakano, T., Ikawa, N. I., & Ozimek, L. (2003). Chemical composition of chicken eggshell and shell membranes. Poultry Science, 82, 510–514.

    Article  CAS  PubMed  Google Scholar 

  47. Norris, K. H. (1996). History of NIR. Journal of Near Infrared Spectroscopy., 4, 31–37.

    Article  CAS  Google Scholar 

  48. Omid, M., Soltani, M., Dehrouyeh, M. H., Mohtasebi, S. S., & Ahmadi, H. (2013). An expert egg grading system based on machine vision and artificial intelligence techniques. Journal of Food Engineering, 118, 70–77.

    Article  Google Scholar 

  49. Park, B., Abbott, J. A., Lee, K. J., Choi, C. H., & Choi, K. H. (2003). Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of delicious and Gala apples. Transactions of ASAE, 46, 1721–1731.

    Article  Google Scholar 

  50. Qi, L., Zhao, M.-C., Li, Z., Shen, D.-H., & Lu, J. (2020). Non-destructive testing technology for raw eggs freshness: A review. SN Applied Sciences, 2(6), 1–9.

    Article  Google Scholar 

  51. Sanovo. (2019). Features of Opti grader. Retrieved from https://www.sanovogroup.com/products/egg-gradeng/gradeng/Optigrader/features/.

  52. Schiffman, S. S. & Pearce, T. C. (2003). Introduction to olfaction: perception, anatomy, physiology, and molecular biology. Handbook of Machine Olfaction: Electronic Nose Technology. Wiley Online Library, 1–31.

    Google Scholar 

  53. Schmilovitch, Z., Hoffman, A., Egozi, H., & Klein, E. (2002). Determination of egg freshness by NNIRS (near-near infrared spectroscopy), presented at EurAgEng, Budapest, paper No. 02- AP-023.

    Google Scholar 

  54. Sekeroglu, A., & Altuntas, E. (2009). Effects of egg weight on egg quality characteristics. Journal of the Science of Food and Agriculture, 89(3), 379–383.

    Article  CAS  Google Scholar 

  55. Soltani, M., Omid, M., & Alimardani, R. (2015). Egg quality prediction using dielectric and visual properties based on artificial neural network. Food Analytical Methods, 8(3), 710–717.

    Article  Google Scholar 

  56. Sun, L., Bi, X. K., Lin, H., Zhao, J. W., & Cai, J. R. (2013). On-line detection of eggshell crack based on acoustic resonance analysis. Journal of Food Engineering, 116(1), 240–245.

    Article  Google Scholar 

  57. Sun, L., Yuan, L. M., Cai, J. R., Lin, H., & Zhao, J. W. (2014). Egg freshness on-line estimation using machine vision and dynamic weighing. Food Analytical Methods, 8(4), 922–928.

    Article  Google Scholar 

  58. Todd, E. C. (1996). Risk assessment of use of cracked eggs in Canada. International Journal of Food Microbiology., 30, 125–143.

    Article  CAS  PubMed  Google Scholar 

  59. Trnka, J., Nedomová, Š, Kumbár, V., Šustr, M., & Buchar, J. (2016). A new approach to analyze the dynamic strength of eggs. Journal of Biological Physics, 42(4), 525–537. https://doi.org/10.1007/s10867-016-9420-9

    Article  PubMed  PubMed Central  Google Scholar 

  60. Usui, Y., Nakano, K., & Motonaga, Y. (2003). A study on the development of non-destructive detection system for abnormal eggs. EFITA Conference, Debrecen., Hungary.

    Google Scholar 

  61. Wang, H.-H., & Sun, D.-W. (2002). Melting characteristics of cheese: Analysis of effects of cooking conditions using computer vision techniques. Food Engineering, 52(3), 279–284.

    Article  Google Scholar 

  62. Wang, H., Mao, J., Zhang, J., Jiang, H., & Wang, J. (2016). Acoustic feature extraction and optimization of crack detection for eggshell. Food Engineering, 172, 240–241.

    Article  Google Scholar 

  63. Wang, W., & Paliwal, J. (2007). Near-infrared spectroscopy and imaging in food quality and safety. Sensing Instrumentation of Food Quality, 1, 193–207.

    Article  Google Scholar 

  64. Williams, P., & Norris, K. (2001). Near infrared technology in the agricultural and food industries. Saint Paul, USA: ACCC.

    Google Scholar 

  65. Xu, J., Plaxco, K. W., & Allen, S. J. (2006). Collective dynamics of lysozyme in water: Terahertz absorption spectroscopy and comparison with theory. The Journal of Physical Chemistry B, 110, 24255–24259.

    Article  CAS  PubMed  Google Scholar 

  66. Yongwei, W., Wang, J., Zhou, B., & Lu, Q. (2019). Monitoring storage time and quality attribute of egg based on electronic nose. Analytica Chimica Acta, 650, 183–188.

    Article  Google Scholar 

  67. Zou, X., & Zhao, J. (2015). Non-destructive measurement in food and agro-products.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept of Agrotechnology, College of Abouraihan, University of Tehran, Tehran, Iran

    Mohammad Aboonajmi & Zohre Mostafaei

Authors
  1. Mohammad Aboonajmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zohre Mostafaei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Aboonajmi .

Editor information

Editors and Affiliations

  1. Agricultural Engineering and Technology, Sylhet Agricultural University, Sylhet, Bangladesh

    Alin Khaliduzzaman

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Aboonajmi, M., Mostafaei, Z. (2022). Non-destructive Quality Assessment of Table Eggs for Online Sorting. In: Khaliduzzaman, A. (eds) Informatics in Poultry Production. Springer, Singapore. https://doi.org/10.1007/978-981-19-2556-6_3

Download citation

Publish with us

Policies and ethics

Search

Navigation