| Yazarlar (7) |
Metehan Denli
|
|
Emre Yavuzer
Niğde Ömer Halisdemir Üniversitesi, Türkiye |
|
Hasan Tangüler
Türkiye |
Dr. Öğr. Üyesi Memduh KÖSE
Kırşehir Ahi Evran Üniversitesi, Türkiye |
|
Mehmet Kürşat Yalçın
Niğde Ömer Halisdemir Üniversitesi, Türkiye |
|
Hasan Macit
|
|
Mehmet Yetişen
Niğde Ömer Halisdemir Üniversitesi, Türkiye |
| Özet |
| 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. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayınlanan tam makale |
| Dergi Adı | Turkish Journal of Agriculture - Food Science and Technology (TURJAF) |
| Dergi ISSN | 2148-127X |
| Dergi Tarandığı Indeksler | TR DİZİN |
| Makale Dili | İngilizce |
| Basım Tarihi | 04-2025 |
| Cilt No | 13 |
| Sayı | 4 |
| Sayfalar | 934 / 940 |
| Doi Numarası | 10.24925/turjaf.v13i4.934-940.7352 |
| Makale Linki | https://agrifoodscience.com/index.php/TURJAF/article/view/7352 |
| Atıf Sayıları |
