Determination of Quality Changes of Hard-Boiled Chicken Eggs Due to Slow and Fast Cooling by Electronic Nose and Machine Learning

Aug 4, 2026

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Freshness levels of boiled chicken eggs were assessed using an electronic nose coupled with machine learning. Eggs were boiled, then stored under refrigeration at 3±1°C from day 0 to day 6. Each day, samples were split into quick-cooled and fast-cooled groups. Electronic nose sensor outputs were collected, and image changes from 110 daily image files were analyzed. Using 85% of image data for training and 15% for testing, classification achieved over 98% accuracy. Results indicated egg white solidified after more than 4 minutes and yolk after 11 minutes; fast-cooled eggs showed significantly lower odor levels, supporting better freshness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Egg quality deterioration during storage

Limitations of conventional analyses

Electronic nose and machine learning for quality assessment

Turkish Journal of Agriculture-Food Science and Technology, 13(4): 934-940, 2025 DOI: [https://doi.org/10.24925/turjaf.v13i4.934-940.7352](https://doi.org/10.24925/turjaf.v13i4.934-940.7352) |
Turkish Journal of Agriculture-Food Science and Technology | | | --- | --- | | Available online, ISSN: 2148-127X │[www.agrifoodscience.com](www.agrifoodscience.com) │ Turkish Science and Technology Publishing (TURSTEP) | | | Determination of Quality Changes of Hard-Boiled Chicken Eggs Due to Slow and Fast Cooling by Electronic Nose and Machine Learning
Metehan Denli1,a, Emre Yavuzer1,b, Hasan Tangüler1,c, Memduh Köse2,d,
Mehmet Kürşat Yalçın3,e, Hasan Macit1,f, Mehmet Yetişen1,g,*
1Department of Food Engineering, Faculty of Engineering, Niğde Ömer Halisdemir University, 50300, Niğde, Türkiye 2Department of Electric and Electronic Engineering, Faculty of Engineering and Architecture, Kırşehir Ahi Evran University, 40100, Kırşehir, Türkiye
3Department of Mechatronics Engineering, Niğde Ömer Halisdemir University, 50300, Niğde, Türkiye
*Corresponding author | | | A R T I C L E I N F O A B S T R A C T | | | Research Article | In this study, the freshness levels of boiled chicken eggs were determined using an electronic nose and machine learning techniques. Eggs were boiled and stored under refrigerator conditions (3±1ºC) from day 0 to day 6. Each storage day, eggs were divided into two groups based on cooling methods: | | Received : 22.11.2024
Accepted : 04.03.2025 | quick-cooled and fast-cooled. Sensor readings were taken using an electronic nose, and image changes from 110 daily image files were processed with a machine learning program. With 85% of the image data used for training and 15% for testing, a classification accuracy of over 98% was | | Keywords: Machine learning Electronic nose Boiled egg Freshness | achieved. The results showed that egg white solidified in more than 4 minutes and yolk solidified in 11 minutes. Fast-cooled eggs exhibited significantly lower odor levels, indicating superior freshness. This study demonstrates the effectiveness of electronic nose and machine learning systems in accurately determining the freshness of boiled eggs. | | Cooling methods | | a [metehandenli16@gmail.com](metehandenli16@gmail.com) c [htanguler@ohu.edu.tr](htanguler@ohu.edu.tr)e [mkyalcin@ohu.edu.tr](mkyalcin@ohu.edu.tr)[ ](mkyalcin@ohu.edu.tr)g [mehmetyetisen@ohu.edu.tr](mehmetyetisen@ohu.edu.tr) [https://orcid.org/0009-0002-8386-8464](https://orcid.org/0009-0002-8386-8464) [https://orcid.org/0000-0001-6425-9896](https://orcid.org/0000-0001-6425-9896) [https://orcid.org/0000-0001-9484-1422](https://orcid.org/0000-0001-9484-1422) [https://orcid.org/0000-0001-8347-4081](https://orcid.org/0000-0001-8347-4081) b [eyavuzer@ohu.edu.tr](eyavuzer@ohu.edu.tr) d [memduh.kose@ahievran.edu.tr](memduh.kose@ahievran.edu.tr) f [hsnmct98@gmail.com](hsnmct98@gmail.com) [https://orcid.org/0000-0002-9192-713X](https://orcid.org/0000-0002-9192-713X) [https://orcid.org/0000-0002-4935-4542](https://orcid.org/0000-0002-4935-4542) [https://orcid.org/0000-0002-8227-7868](https://orcid.org/0000-0002-8227-7868) This work is licensed under Creative Commons Attribution 4.0 International License Introduction Egg, one of the third most important animal protein sources after meat and milk, contains many elements necessary for nutrition. Although different poultry (turkey, goose, quail, etc.) also have eggs, chicken eggs come to mind when "egg" is mentioned in nutrition terminology because they are produced industrially, are easily accessible, and create unique flavors by increasing the consistency of other foods. The important role of chicken eggs in human nutrition is based on the fact that they are considered to be excellent sources of high-quality protein, vitamins, and minerals. (Elkin & Harvatine, 2023; Hossain et al., 2024). However, eggs, considered a complete source of protein essential for opti

FAQs

How were the boiled eggs stored and evaluated in the study?

Boiled chicken eggs were refrigerated at 3±1°C from day 0 to day 6. Freshness was evaluated each day using electronic nose readings and machine-learning analysis of daily images.

What cooling methods were compared?

The study compared quick-cooled versus fast-cooled conditions. Eggs were separated into these two groups at each storage day.

What performance did the machine-learning classification achieve?

Image data were split into 85% training and 15% testing, producing classification accuracy above 98%.

Aug 4, 2026

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