Detection of blood spots in eggs by hyperspectral transmittance imaging
Keywords:
hyperspectral transmittance imaging, non-destructive detection, blood-spot, eggAbstract
Blood spots are one of undesired inclusions in eggs, whose detection success is highly dependent on shell color. This research reports a method for detecting blood spots in light brown-shelled eggs on the basis of hyperspectral transmittance images. The normalized spectra of intact eggs and their shells were acquired. Five feature wavelengths of intact eggs selected by the successive projections algorithm and 3 absorption peak locations of eggshells were regarded as characteristic bands. The k-nearest neighbor (kNN) and support vector machine (SVM) algorithms were adopted to develop detection models. The latter achieved better performance. The overall classification accuracy increased to 100% by merging normalized spectra of intact eggs at 5 feature wavelengths with 3 absorption peaks of eggshells as input variables of SVM-based model. Moreover, a practical SVM-based model with 96.43% overall classification accuracy was established by replacing inputs with normalized spectra of intact eggs at characteristic bands. Keywords: hyperspectral transmittance imaging, non-destructive detection, blood-spot, egg DOI: 10.25165/j.ijabe.20191206.5376 Citation: Feng Z, Ding C Q, Li W H, Cui D. Detection of blood spots in eggs by hyperspectral transmittance imaging. Int J Agric & Biol Eng, 2019; 12(6): 209–214.References
[1] USDA. United States Standards: Grades, and weight classes for shell eggs. in: AMS-56, 2000.
[2] MOFCOM, Grading of shell hen eggs and duck eggs. SB/T 10638-2011, China, 2011.
[3] Brant A W, Norris K H, Chin G. A spectrophotometric method for detecting blood in white-shell eggs. Poultry Science, 1953; 32(2): 357–363.
[4] Patel V C, Mcclendon R W, Goodrum J W. Detection of blood spots and dirt stains in eggs using computer vision and neural networks. Applied Engineering in Agriculture, 1996; 12(2): 253–258.
[5] Patel V C, Mcclendon R W, Goodrum J W. Color computer vision and artificial neural networks for the detection of defects in poultry eggs. in: Panigrahi S, Ting K C (Eds.) Artificial intelligence for biology and agriculture. Springer Netherlands, Dordrecht, 1998; pp. 163–176.
[6] Nakano K, Sasaoka K, Ohtsuka Y. A study on non-destructive detection of abnormal eggs by using image processing. 2nd Asian Conference for Information Technology in Agric, Asian Federation for Information Technology in Agriculture, 2000; pp. 345–352.
[7] Usui Y, Nakano K, Motonaga Y. A study of the development of non-destructive detection system for abnormal eggs. EFITA, Hungary, 2003.
[8] Gielen R M A M, Jong L P D, Kerkvliet H M M. Electrooptical blood-spot detection in intact eggs. IEEE Transactions on Instrumentation & Measurement, 1979; 28(3): 177–183.
[9] Xu H, Xu W, Chen H, Yang Y, Zhang A. Detection of blood spots in brown eggs based on spectroscopic techniques. Transactions of the CSAM, 2014; 45(2): 194–198.
[10] Huang M, Wan X, Zhang M, Zhu Q. Detection of insect-damaged vegetable soybeans using hyperspectral transmittance image. Journal of Food Engineering, 2013; 116(1): 45–49.
[11] Zhang W, Pan L, Tu S, Zhan G, Tu K. Non-destructive internal quality assessment of eggs using a synthesis of hyperspectral imaging and multivariate analysis. Journal of Food Engineering, 2015; 157: 41–48.
[12] Bamelis F R, Ketelaere B D, Kemps B J, Mertens K, Decuypere E M, Baerdemaeker J G D. Non invasive methods for egg quality evaluation. 12th European Poultry Conference, Verona, Italy, 2006.
[13] Nalbandov A V, Card L E. The problem of blood clots and meat spots in chicken eggs. Poultry Science, 1944; 23(3): 170–180.
[14] Chen M, Zhang L, Xu H. On-line detection of blood spot introduced into brown-shell eggs using visible absorbance spectroscopy. Biosystems Engineering, 2015; 131: 95–101.
[15] Xu W. A study on spectral features and real-time detection of blood spots in eggs. Hangzhou: Zhejiang University, 2013.
[16] Qin J, Lu R. Detection of pits in tart cherries by hyperspectral transmission imaging. Proc Spie, 2005; 48(5): 1963–1970.
[17] Araújo M C U, Saldanha T C B, Galvão R K H, Yoneyama T, Chame H C, Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics & Intelligent Laboratory Systems, 2001; 57(2): 65–73.
[18] Ye S, Wang D, Min S. Successive projections algorithm combined with
uninformative variable elimination for spectral variable selection. Chemometrics & Intelligent Laboratory Systems, 2008; 91(2): 194–199.
[19] Altman N S. An introduction to kernel and nearest-neighbor nonparametric regression. American Statistician, 1992; 46(3): 175–185.
[20] Hall P, Park B U, Samworth R J. Choice of neighbor order in nearest-neighbor classification. Annals of Statistics, 2008; 36(5): 2135–2152.
[21] Gold C, Sollich P. Model selection for support vector machine classification. Neurocomputing, 2002; 55(1): 221–249.
[22] Mercier G, Lennon M. Support vector machines for hyperspectral image classification with spectral-based kernels. Proceedings of 2003 IEEE International Symposium on Geoscience and Remote Sensing, IGARSS '03. 2003; 281: 288–290.
[23] Kemps B J, Bamelis F R, Ketelaere B D, Mertens K, Tona K, Decuypere E M, et al. Visible transmission spectroscopy for the assessment of egg freshness. Journal of the Science of Food & Agriculture, 2006; 86(9): 1399–1406.
[2] MOFCOM, Grading of shell hen eggs and duck eggs. SB/T 10638-2011, China, 2011.
[3] Brant A W, Norris K H, Chin G. A spectrophotometric method for detecting blood in white-shell eggs. Poultry Science, 1953; 32(2): 357–363.
[4] Patel V C, Mcclendon R W, Goodrum J W. Detection of blood spots and dirt stains in eggs using computer vision and neural networks. Applied Engineering in Agriculture, 1996; 12(2): 253–258.
[5] Patel V C, Mcclendon R W, Goodrum J W. Color computer vision and artificial neural networks for the detection of defects in poultry eggs. in: Panigrahi S, Ting K C (Eds.) Artificial intelligence for biology and agriculture. Springer Netherlands, Dordrecht, 1998; pp. 163–176.
[6] Nakano K, Sasaoka K, Ohtsuka Y. A study on non-destructive detection of abnormal eggs by using image processing. 2nd Asian Conference for Information Technology in Agric, Asian Federation for Information Technology in Agriculture, 2000; pp. 345–352.
[7] Usui Y, Nakano K, Motonaga Y. A study of the development of non-destructive detection system for abnormal eggs. EFITA, Hungary, 2003.
[8] Gielen R M A M, Jong L P D, Kerkvliet H M M. Electrooptical blood-spot detection in intact eggs. IEEE Transactions on Instrumentation & Measurement, 1979; 28(3): 177–183.
[9] Xu H, Xu W, Chen H, Yang Y, Zhang A. Detection of blood spots in brown eggs based on spectroscopic techniques. Transactions of the CSAM, 2014; 45(2): 194–198.
[10] Huang M, Wan X, Zhang M, Zhu Q. Detection of insect-damaged vegetable soybeans using hyperspectral transmittance image. Journal of Food Engineering, 2013; 116(1): 45–49.
[11] Zhang W, Pan L, Tu S, Zhan G, Tu K. Non-destructive internal quality assessment of eggs using a synthesis of hyperspectral imaging and multivariate analysis. Journal of Food Engineering, 2015; 157: 41–48.
[12] Bamelis F R, Ketelaere B D, Kemps B J, Mertens K, Decuypere E M, Baerdemaeker J G D. Non invasive methods for egg quality evaluation. 12th European Poultry Conference, Verona, Italy, 2006.
[13] Nalbandov A V, Card L E. The problem of blood clots and meat spots in chicken eggs. Poultry Science, 1944; 23(3): 170–180.
[14] Chen M, Zhang L, Xu H. On-line detection of blood spot introduced into brown-shell eggs using visible absorbance spectroscopy. Biosystems Engineering, 2015; 131: 95–101.
[15] Xu W. A study on spectral features and real-time detection of blood spots in eggs. Hangzhou: Zhejiang University, 2013.
[16] Qin J, Lu R. Detection of pits in tart cherries by hyperspectral transmission imaging. Proc Spie, 2005; 48(5): 1963–1970.
[17] Araújo M C U, Saldanha T C B, Galvão R K H, Yoneyama T, Chame H C, Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics & Intelligent Laboratory Systems, 2001; 57(2): 65–73.
[18] Ye S, Wang D, Min S. Successive projections algorithm combined with
uninformative variable elimination for spectral variable selection. Chemometrics & Intelligent Laboratory Systems, 2008; 91(2): 194–199.
[19] Altman N S. An introduction to kernel and nearest-neighbor nonparametric regression. American Statistician, 1992; 46(3): 175–185.
[20] Hall P, Park B U, Samworth R J. Choice of neighbor order in nearest-neighbor classification. Annals of Statistics, 2008; 36(5): 2135–2152.
[21] Gold C, Sollich P. Model selection for support vector machine classification. Neurocomputing, 2002; 55(1): 221–249.
[22] Mercier G, Lennon M. Support vector machines for hyperspectral image classification with spectral-based kernels. Proceedings of 2003 IEEE International Symposium on Geoscience and Remote Sensing, IGARSS '03. 2003; 281: 288–290.
[23] Kemps B J, Bamelis F R, Ketelaere B D, Mertens K, Tona K, Decuypere E M, et al. Visible transmission spectroscopy for the assessment of egg freshness. Journal of the Science of Food & Agriculture, 2006; 86(9): 1399–1406.
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Published
2019-12-04
How to Cite
Feng, Z., Ding, C., Li, W., & Cui, D. (2019). Detection of blood spots in eggs by hyperspectral transmittance imaging. International Journal of Agricultural and Biological Engineering, 12(6), 209–214. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5376
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Agro-product and Food Processing Systems
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