Rapid detection of sea bass quality level with machine learning and electronic nose
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In the present study, the odour changes of sea bass was measured and recorded with Arduino-based six gas (MQ) sensors during 7 days of storage. In addition, the changes in biogenic amines (BAs) and total viable count (TVC) occurring in fish meat during storage were determined daily and compared with electronic nose data. For image processing, 500 fish data were taught to Teachable Machine (TM) daily, a web-based machine learning platform. In the study, it was determined that if the level of putrescine (PUT) exceeds 1 mg kg(-1) and spermidine (SPMD) exceeds the level of 2 mg kg(-1), the MQ4, MQ5, MQ8 and MQ9 sensors exceed the value of 500. In the study, it was determined that combined machine learning and electronic nose could be used as a rapid quality determination tool in a perishable food such as fish.
Açıklama
Anahtar Kelimeler
e Nose, machine learning, sea bass, teachable machine
Kaynak
International Journal of Food Science and Technology
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
58
Sayı
5
