Current advances in detection of abnormal egg: a review

Area of Science:

• Agricultural Science
• Food Science
• Engineering

Background:

• Preventing cross-contamination in stored eggs is crucial.
• Human inspection of egg defects is labor-intensive and error-prone.
• Emerging non-destructive technologies offer rapid and efficient alternatives.

Purpose of the Study:

• To review and analyze the performance of emerging abnormal egg detection technologies.
• To investigate discriminant methods and algorithms for classifying egg defects.
• To assess the technical feasibility and industrial applicability of current detection systems.

Main Methods:

• Literature review of recently published resources on abnormal egg detection.
• Analysis of technologies including acoustic response, machine vision, and spectroscopy.
• Investigation of classification algorithms and discriminant methods.

Main Results:

• Commercialized technologies utilize acoustic response, machine vision, and spectroscopy.
• Non-destructive methods enhance egg quality assessment and detection efficiency.
• Laboratory-scale studies demonstrate high technical feasibility and excellent detection accuracy.

Conclusions:

• Selected algorithms are essential for improving abnormal egg classification efficiency.
• Developed detection technologies are technically feasible for identifying internal and external egg defects.
• Achieving industry-required detection rates is necessary for widespread poultry industry adoption.