Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q1
Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks
Food and Bioprocess Technology · Ekim 2025
YÖKSİS Kayıtları
Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks
Food and Bioprocess Technology · 2025 SCI-Expanded
Doç. Dr. OSMAN ÖZBEK →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Limonene Functionalization of Recycled PAN Nanofibers for Chicken Breast Meat Preservation
2026 ISSN: 1935-5130 SCI-Expanded Q1
Öğr. Gör. ALİYE AŞIKKUTLU →
Determination of Rheological Behavior Emulsion Stability Color and Sensory of Sesame Pastes Tahin Blended with Pine Honey
2012 ISSN: 1935-5130 SCI-Expanded 8 atıf
Prof. Dr. MEHMET AKBULUT →
Extraction of Sesquiterpene Lactones from Inula helenium Roots by High-Pressure Homogenization and Effects on Antimicrobial, Antioxidant, and Antiglycation Activities
2024 ISSN: 1935-5130 SCI-Expanded Q1
Doç. Dr. ÖZLEM ÇETİN →
Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks
2025 ISSN: 1935-5130 SCI-Expanded Q1
Doç. Dr. OSMAN ÖZBEK →
Makale Bilgileri
ISSN19355130
Yayın TarihiEkim 2025
Cilt / Sayfa18 · 8453-8467
Scopus ID2-s2.0-105008780662
Erişim🔓 Açık Erişim
Özet
Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices using the k-Nearest Neighbors (KNN), Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms. The results showed an inverse relationship between the storage period and the hardness of the apple slices, with the average hardness values gradually decreasing from 4.33 (day 1) to 3.37 (day 5). Treatment with atmospheric plasma at a pressure of 5 atm and an immersion time of 3 min gave the best results for maintaining the hardness of the slices during the storage period, recording values of 4.85 (first day) and 3.68 (fifth day), outperforming other treatments. The average improvement rate was 23.09% over five consecutive days. Regarding the CNN algorithms, the ANN algorithm achieved the highest classification accuracy of 97%, while the Tree algorithm achieved the lowest accuracy of 88.7%. The KNN and SVM algorithms achieved classification accuracies of 94.7% and 95.1%, respectively. The study demonstrated the possibility of using a CNN to classify apple slices based on the degree of hardness. Furthermore, the application of atmospheric plasma at 5 atmospheres with a 3-min immersion improves the firmness of the apple slices by inhibiting degradative enzymes while preserving the cellular structure and tissue quality.
Yazarlar (6)
1
Mustafa Ahmed Jalal Al-Sammarraie
2
Łukasz Gierz
3
Ghaith H. Jihad
4
Zeki Gökalp
5
Osman Özbek
ORCID: 0000-0003-0034-9387
6
Piotr Markowski
Anahtar Kelimeler
Apple slice
Atmospheric plasma
Convolutional neural network
Hardness
Kurumlar
Erciyes Üniversitesi
Kayseri Turkey
Politechnika Poznanska
Poznan Poland
Selçuk Üniversitesi
Selçuklu Turkey
University of Baghdad
Baghdad Iraq
Uniwersytet Warminsko-Mazurski w Olsztynie
Olsztyn Poland
Scimago Dergi (ISSN Eşleşmesi)
Food and Bioprocess Technology
Q1
SJR Skoru1,071
H-Index136
YayıncıSpringer
ÜlkeUnited States
Food Science (Q1)
Industrial and Manufacturing Engineering (Q1)
Process Chemistry and Technology (Q1)
Safety, Risk, Reliability and Quality (Q1)