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Egg Quality Prediction Using Dielectric and Visual Properties Based on Artificial Neural Network

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Abstract

Recently, rapid and nondestructive technologies have been developed in the qualification of food products. This research aimed to design and develop an egg qualifying system based on dielectric technology in the range of radio frequency (40 kHz to 20 MHz), machine vision, and artificial neural network (ANN) techniques. Haugh unit, yolk index, yolk/albumen ratio, and yolk weight were studied as quality factors of egg. The designed electronic device was calibrated and evaluated for prediction of the mentioned parameters. The coefficient of determination (R 2) values in the validation of the ANN were 0.998, 0.998, 0.998, and 0.994 for the Haugh unit, yolk index, yolk/albumen, and yolk weight, respectively. In evaluation mode, the mean absolute percent errors (MAPEs) obtained were 5.41, 6.84, 8.79, and 4.24 % for the Haugh unit, yolk index, yolk/albumen, and yolk weight, respectively. Results of the evaluation showed the designed device can be confidently used in the prediction of egg quality indices.

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Acknowledgments

The authors would like to acknowledge the Center of Research and Development of ETKA Organization for providing financial support to this research. The authors would also like to thank the Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran for their contributions to this study.

Conflict of Interest

Mahmoud Soltani, Mahmoud Omid, and Reza Alimardani declare that they have no conflict of interest. This article does not contain any studies with human or animal subjects.

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Authors and Affiliations

  1. Center of Research and Development of ETKA Organization, Tehran, Iran

    Mahmoud Soltani

  2. Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

    Mahmoud Omid & Reza Alimardani

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  1. Mahmoud Soltani
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  2. Mahmoud Omid
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  3. Reza Alimardani
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Correspondence to Mahmoud Soltani.

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Soltani, M., Omid, M. & Alimardani, R. Egg Quality Prediction Using Dielectric and Visual Properties Based on Artificial Neural Network. Food Anal. Methods 8, 710–717 (2015). https://doi.org/10.1007/s12161-014-9948-x

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  • DOI: https://doi.org/10.1007/s12161-014-9948-x

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