Determination of Quality Changes of Hard-Boiled Chicken Eggs Due to Slow and Fast Cooling by Electronic Nose and Machine Learning
DOI:
https://doi.org/10.24925/turjaf.v13i4.934-940.7352Keywords:
Machine learning, electronic nose, boiled egg, Freshness, cooling methodsAbstract
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: 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 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.
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