So Jun-Hwi, Joe Sung Yong, Hwang Seon Ho, Hong Soon Jung, Lee Seung Hyun
Department of Smart Agriculture Systems, Chungnam National University, Daejeon 34134, Korea.
Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea.
J Anim Sci Technol. 2022 Sep;64(5):813-829. doi: 10.5187/jast.2022.e56. Epub 2022 Sep 30.
Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.
应检测鸡蛋的内部和外部缺陷，以防止储存期间完好鸡蛋被异常鸡蛋交叉污染。介绍了新兴的异常鸡蛋检测技术，作为人工检验的替代方法。先进技术可以快速检测出异常鸡蛋。利用声学响应、机器视觉和光谱学的异常鸡蛋检测技术已在禽蛋行业实现商业化。同时，无损鸡蛋质量评估方法可以保留鸡蛋的价值并提高检测效率。为了提高检测效率，选择合适的算法对异常鸡蛋类型进行分类至关重要。这篇综述探讨了近期发表资料中各类异常鸡蛋检测技术的性能。此外，还研究了已发表文献中报道的异常鸡蛋判别方法和检测算法。尽管大多数研究是在实验室规模上进行的，但已开发的鸡蛋内部和外部缺陷检测技术在技术上可行，能够获得优异的检测精度。要将已开发的检测技术应用于禽蛋行业，有必要达到行业要求的检测率。
