The current research status of non-destructive testing technologies for egg quality: internal freshness – a review
1. Traditional methods of assessing egg freshness, such as sensory evaluation and specific gravity testing, are labour-intensive and destructive. However, non-destructive testing (NDT) technologies offer significant advantages in terms of speed, accuracy and non-invasiveness, enabling real-time monitoring of egg quality during production, transportation, storage and sale.2. A review was conducted to evaluate various NDT techniques, including optical methods (such as visible - near infrared spectroscopy and hyperspectral imaging), acoustic methods (such as ultrasonic transmission and reflection analysis), electrical methods (such as dielectric spectroscopy) and emerging technologies (such as low-field nuclear magnetic resonance, electronic nose and infrared thermal imaging).3. These technologies have advanced significantly in accuracy and real-time performance. Among optical methods: visible - near infrared spectroscopy (R<sup>2</sup> = 0.90) and hyperspectral imaging (R<sup>2</sup> = 0.918). Acoustic technologies: ultrasonic phase velocity correlated significantly with Haugh unit (HU)/air cell height (<i>p</i> < 0.05), with a HU model correlation of R<sup>2</sup> = 0.87. Electrical methods: dielectric spectroscopy + algorithms achieved 100% classification accuracy; capacitive sensing + machine vision yielded R<sup>2</sup> > 0.994. Emerging technologies enhanced sensitivity. However, challenges remain in terms of equipment cost, sample variability, lack of unified standards and the integration of multi-source data with artificial intelligence.4. The widespread application of these technologies can be expected to significantly improve the efficiency, safety and sustainability of the egg supply chain.
Authors
- Jie Cai (ORCID: https://orcid.org/0000-0001-9505-6725)
- Binyu Pan (ORCID: https://orcid.org/0000-0001-5078-5588)
- Zhaohong Shi (ORCID: https://orcid.org/0000-0002-4915-5973)
- Feng Zhang (ORCID: https://orcid.org/0000-0001-5893-0604)
- Wenhui Zhu (ORCID: https://orcid.org/0000-0002-4889-781X)
- Q. Li (ORCID: https://orcid.org/0009-0006-6240-0757)
- Yu Qiao (ORCID: https://orcid.org/0000-0002-0896-0044)
- Chaojie Wang (ORCID: https://orcid.org/0000-0003-3518-7680)
- Li Sun (ORCID: https://orcid.org/0000-0002-9140-4275)
- Juan Bai (ORCID: https://orcid.org/0009-0000-4979-3724)
Institutions
- Jiangsu University (CN)
Publication Details
- Journal
- British Poultry Science
- Published
- 2025-10-03
- DOI
- https://doi.org/10.1080/00071668.2025.2552785
- Primary Topic
- Identification and Quantification in Food
- Type
- article
- Field-Weighted Citation Impact
- 0.00