Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q1
Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types
Sensors · Şubat 2023
YÖKSİS Kayıtları
Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types
Sensors · 2023 SCI-Expanded
Doç. Dr. YAVUZ SELİM TAŞPINAR →
YÖKSİS Kayıtları — ISSN Eşleşmesi
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.
2021 ISSN: 1424-8220 SCI-Expanded Q2
Dr. Öğr. Üyesi ÜSAME ALİ USCA →
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
2020 ISSN: 1424-8220 SCI-Expanded Q1
Doç. Dr. MUSTAFA KUNTOĞLU →
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
2020 ISSN: 1424-8220 SCI-Expanded Q1
Doç. Dr. EMİN SALUR →
Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140
2020 ISSN: 1424-8220 SCI-Expanded
Doç. Dr. MUSTAFA KUNTOĞLU →
Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types
2023 ISSN: 1424-8220 SCI-Expanded Q2
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Survey on Blockchain-Based Data Storage Security for Android Mobile Applications
2023 ISSN: 1424-8220 SCI-Expanded Q2
Prof. Dr. ADEM ALPASLAN ALTUN →
Optimizing Autonomous Vehicle Performance Using Improved Proximal Policy Optimization
2025 ISSN: 1424-8220 SCI-Expanded
Dr. Öğr. Üyesi ONUR İNAN →
Comparative Analysis of Machine Learning Methods with Chaotic AdaBoost and Logistic Mapping for Real-Time Sensor Fusion in Autonomous Vehicles: Enhancing Speed and Acceleration Prediction Under Uncertainty
2025 ISSN: 1424-8220 SCI-Expanded
Dr. Öğr. Üyesi ONUR İNAN →
Makale Bilgileri
Dergi
Sensors
ISSN14248220
Yayın TarihiŞubat 2023
Cilt / Sayfa23
Scopus ID2-s2.0-85148863408
Erişim🔓 Açık Erişim
Özet
Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.
Yazarlar (2)
1
Ahmet Feyzioglu
ORCID: 0000-0003-0296-106X
2
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
Anahtar Kelimeler
beef quality
control
data fusion
decision support system
e-nose
Kurumlar
Marmara Üniversitesi
Istanbul Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Sensors
Q1
OA
SJR Skoru0,802
H-Index303
YayıncıMultidisciplinary Digital Publishing Institute (MDPI)
ÜlkeSwitzerland
Analytical Chemistry (Q1)
Electrical and Electronic Engineering (Q1)
Instrumentation (Q1)
Atomic and Molecular Physics, and Optics (Q2)
Biochemistry (Q2)
Information Systems (Q2)
Medicine (miscellaneous) (Q2)
Metrikler
44
Atıf
2
Yazar
5
Anahtar Kelime