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Scopus YÖKSİS DOI Eşleşti SJR Q1

Detection of fish freshness using artificial intelligence methods

European Food Research and Technology · Ağustos 2023

YÖKSİS DOI Eşleşmesi Bulundu

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YÖKSİS Kayıtları
Detection of fish freshness using artificial intelligence methods
European Food Research and Technology · 2023 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
Detection of fish freshness using artificial intelligence methods
EUROPEAN FOOD RESEARCH AND TECHNOLOGY · 2023 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded
Dr. Öğr. Üyesi İRFAN ÖZER →
Antifungal properties of some herb decoctions
2000 ISSN: 1438-2377 SCI
Prof. Dr. NUH BOYRAZ →
The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded 14 atıf
Dr. Öğr. Üyesi İRFAN ÖZER →
The effect of various types of poultry pre and post rigor meats on emulsification capacity water holding capacity and cooking loss
2005 ISSN: 1438-2377 SSCI 15 atıf
Prof. Dr. CEMALETTİN SARIÇOBAN →
The effect of irrigation and harvest time on bioactive properties of olive fruits issued from some olive varieties grown in Mediterranean region
2020 ISSN: 1438-2377 SCI
Doç. Dr. NURHAN USLU →
The effect of irrigation and harvest time on bioactive properties of olive fruits issued from some olive varieties grown in Mediterranean region
2020 ISSN: 1438-2377 SCI-Expanded
Prof. Dr. MEHMET MUSA ÖZCAN →
Volatile profile evolution and sensory evaluation of traditional skinbag Tulum cheeses manufactured in Karaman mountainous region of Turkey during ripening
2021 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. TALHA DEMİRCİ →
Computer Vision Classification of Dry Beans (Phaseolus Vulgaris L.) Based on Deep Transfer Learning Techniques
2022 ISSN: 1438-2377 SCI-Expanded Q2
Arş. Gör. MUSA DOĞAN →
The influence of decoction and infusion methods and times on antioxidant activity, caffeine content and phenolic compounds of coffee brews
2022 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. NURHAN USLU →
A New Hybrid Model for Classification of Corn Using Morphological Properties
2023 ISSN: 1438-2377 SCI-Expanded Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
The effect of nitrogen fertilization on tocopherols in rapeseed genotypes
2008 ISSN: 1438-2377 SCI-Expanded Q2
Dr. Öğr. Üyesi İRFAN ÖZER →
A review: benefit and bioactive properties of olive (Olea europaea L.) leaves
2016 ISSN: 1438-2377 SCI-Expanded
Prof. Dr. MEHMET MUSA ÖZCAN →
Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques
2022 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. İLKER ALİ ÖZKAN →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. ADEM GÖLCÜK →
Benchmarking analysis of CNN models for bread wheat varieties
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. ALİ YAŞAR →
Classification of Deep Image Features of Lentil Varieties with Machine Learning Techniques
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
A New Hybrid Model for Classification of Corn Using Morphological Properties
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Detection of fish freshness using artificial intelligence methods
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. MURAT KÖKLÜ →
Classification of Cicer arietinum varieties using MobileNetV2 and LSTM
2023 ISSN: 1438-2377 SCI-Expanded Q2
Doç. Dr. ALİ YAŞAR →

Makale Bilgileri

Dergi European Food Research and Technology
ISSN14382377
Yayın TarihiAğustos 2023
Cilt / Sayfa249 · 1979-1990
DOI10.1007/s00217-023-04271-4
Scopus ID2-s2.0-85153778212
Özet Fish is commonly acknowledged as a highly nutritious food in many regions worldwide, and humans have been consuming fish for centuries to meet their protein and nutritional requirements. The consumption of fresh fish offers numerous benefits, as they contain essential proteins and materials that may be challenging to obtain from alternative sources. However, the freshness of fish decreases after a few days. Humans can determine the freshness of fish by looking at its eyes, smelling it, and checking its gills. But, can machines do the same? This study proposes a novel approach to evaluate the freshness of fish using deep learning techniques. Despite the long-standing tradition of humans determining fish freshness by sensory analysis, the objective evaluation of fish freshness has been challenging. By employing deep learning algorithms (SqueezeNet and InceptionV3) to classify fish based on their freshness using a dataset of 4476 images of fish bodies categorized as fresh and stale, this study provides a new method to address this challenge. Analyzing the results of the study revealed that the SVM, ANN, and LR models result in an accuracy rate of 100% for each deep learning method. This outcome indicates a greater percentage than the previous research, which was 98.0%. This research's novelty lies in its application of deep learning techniques to determine fish freshness objectively, providing a reliable and cost-effective method to evaluate fish freshness. The significance of this study lies in its potential applications in the food industry, offering a reliable method for quality control and food safety.

Yazarlar (3)

1
Elham Tahsin Yasin
ORCID: 0000-0003-3246-6000
2
Ilker Ali Ozkan
3
Murat Koklu
ORCID: 0000-0002-2737-2360

Anahtar Kelimeler

Classification Deep learning Fish body Fish freshness Machine learning Skin coloration Transfer learning

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey

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Scimago Dergi (ISSN Eşleşmesi)
European Food Research and Technology
Q1
SJR Skoru0,692
H-Index136
YayıncıSpringer Science and Business Media Deutschland GmbH
ÜlkeGermany
Industrial and Manufacturing Engineering (Q1)
Biochemistry (Q2)
Biotechnology (Q2)
Chemistry (miscellaneous) (Q2)
Food Science (Q2)
Dergi sayfasına git

Metrikler

58
Atıf
3
Yazar
7
Anahtar Kelime

Sistemimizdeki Yazarlar

MK
MURAT KÖKLÜ
Doç. Dr.

Hızlı Erişim

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