Autoencoder‐Based Eggshell Crack Detection Using Acoustic Signal

Yabanova İ., Balcı Z., Yumurtacı M., Ünler T.

JOURNAL OF FOOD PROCESS ENGINEERING, cilt.47, sa.11, ss.1-4, 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 47 Sayı: 11
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1111/jfpe.14780
  • Dergi Adı: JOURNAL OF FOOD PROCESS ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Food Science & Technology Abstracts, INSPEC, Veterinary Science Database
  • Sayfa Sayıları: ss.1-4
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

Breaks or cracks in eggshells offer substantial food safety issues. Bacteria and viruses, in particular, are more likely to enter the egg through breaks and cracks, increasing the risk of food poisoning. Furthermore, deformations in the shell may compromise the integrity of the protective shell, exposing the egg to more external variables and causing it to lose freshness and decay faster. To reduce such hazards, this research created an innovative crack detection system based on an autoencoder (AE) that uses acoustic signals from eggshells. A system that creates an acoustic effect by hitting the eggshell without damaging it was designed, and these effects were recorded through a microphone. Acoustic signal data of size 1 × 1000 was fed into k nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM) classifiers. AE was employed to reduce data size in order to accommodate the raw data's unique features. This AE model, which reduces data size, was used with many classifiers and was able to accurately distinguish between intact and cracked eggs. The built AE-based classifier model completed the classification procedure with 100% accuracy, including microcracks that are invisible to the naked eye.