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Journals / Mühendislik Bilimleri ve Araştırmaları Dergisi / 2026 / Cilt: 6 Sayı: 1

Classification of Food Flavours using Electronic Nose and Machine Learning Methods

Elektronik Burun ve Makine Öğrenmesi Yöntemleri Kullanılarak Gıda Aromalarının Sınıflandırılması

İlyas Özer(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ)0000-0003-2112-5497
Kutlucan Görür(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ)0000-0003-3578-0150
Onursal Çetin(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ)0000-0001-5220-3959
Feyzullah Temurtaş(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ)0000-0002-3158-4032
Pages
35–41
Publication Date
2024-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this research study, the calculated classification outcomes were estimated above ≥90.00% accuracy with sensitivity (≥90.00) and specificity (≥90.00) scores.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to determine the classes of flavours from chemical odor data obtained from different food flavours using electronic nose and machine learning methods.
📊
Key Findings & Data:The relationship between volatile and non-volatile components in food flavors has been intensively researched.
💡
Conclusion & Impact:In this research study, the calculated classification outcomes were estimated above ≥90.00% accuracy with sensitivity (≥90.00) and specificity (≥90.00) scores.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Elektronik Burun · Gıda Aroması · Koku

Abstract

The aim of this study is to determine the classes of flavours from chemical odor data obtained from different food flavours using electronic nose and machine learning methods. The relationship between volatile and non-volatile components in food flavors has been intensively researched. Normalized analytical protocols are applied to monitor the occurrence of regulated chemicals in processed foods. Although these methods are robust, they involve highly specialized instrumentation and time-consuming and expensive procedures. The electronic nose is a cost-effective and powerful electronic device that can make rapid measurements to distinguish food flavours. Since the molecules responsible for aromatic compounds released during food processing can be detected by the human nose, it may be possible to use electronic nose to detect changes in processed food products. In this research study, the calculated classification outcomes were estimated above ≥90.00% accuracy with sensitivity (≥90.00) and specificity (≥90.00) scores.

Özet

Bu çalışmanın amacı, elektronik burun ve makine öğrenimi yöntemlerini kullanarak farklı gıda aromalarından elde edilen kimyasal koku verilerinden aromalara ait sınıfların belirlenmesidir. Gıda aromaları içinde bulunan uçucu ve uçucu olmayan bileşenler arasındaki ilişki yoğun bir şekilde araştırılmaktadır. İşlenmiş gıdalarda düzenlenmiş kimyasal oluşumunu izlemek için normalleştirilmiş analitik protokoller uygulanır. Bu yöntemler sağlam olmasına karşın, son derece uzmanlaşmış enstrümantasyon, zaman alıcı ve pahalı prosedürler içerir. Elektronik burun, gıda tatlarını ayırt etmek için hızlı ölçümler yapabilen, uygun maliyetli ve güçlü bir elektronik cihazdır. Gıda işleme sırasında açığa çıkan aromatik bileşiklerinden sorumlu moleküller insan burnu tarafından tanınabildiğinden, işlenmiş gıda ürünlerinde meydana gelen değişiklikleri tespit etmek için elektronik burun kullanmak mümkün olabilir. Bu araştırma çalışmasında hesaplanan sınıflandırma sonuçları, duyarlılık (≥90.00%) ve özgüllük (≥90.00%) ile ≥90.00%'in üzerinde doğrulukla tahmin edilmiştir.

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Cite This Article

Özer, İ., Görür, K., Çetin, O., Temurtaş, F. (2024). Classification of Food Flavours using Electronic Nose and Machine Learning Methods. Mühendislik Bilimleri ve Araştırmaları Dergisi, Cilt: 6 Sayı: 1, 35–41. https://doi.org/10.46387/bjesr.1405146

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