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Journals / Mühendislik Bilimleri ve Araştırmaları Dergisi / 2026 / Cilt: 8 Sayı: 1

E-NoseFlavNet: Towards E-Nose based Aroma Flavour Analysis Empowered by Diverse ML Models via Sensor Array Technology

E-NoseFlavNet: Sensör Dizisi Teknolojisi ile Çeşitli Makine Öğrenmesi Modelleriyle Güçlendirilmiş E Burun Tabanlı Aroma Lezzet Analizine Doğru

İlyas Özer(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ)0000-0003-2112-5497
Onursal Çetin(TÜRKSAT AŞ. Uydu Teknolojileri ve Ar-Ge Müdürülüğü)0000-0001-5220-3959
Kutlucan Görür(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ)0000-0003-3578-0150
Feyzullah Temurtaş(OSTİM TEKNİK ÜNİVERSİTESİ)0000-0002-3158-4032
Pages
67–76
Publication Date
2026-04-30
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AI Summary & Key Takeaways

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

Statistical methods showed clear VOC separation, especially in one vs rest analyses.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Ensuring food-supply safety requires reliable aroma/flavour detection, yet conventional tools (test paper, cyclotron, chromatography) often lack accuracy, broad detection range, speed, portability, and low cost.
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Key Findings & Data:Using a literature benchmark of 95.73% accuracy, we evaluated Random Forest, ExtraTrees, XGBoost, CatBoost, and a Stacking ensemble for 5 class prediction and one vs rest tests.
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Conclusion & Impact:Statistical methods showed clear VOC separation, especially in one vs rest analyses.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Aroma Lezzet Analiz · E Burun · Makine Öğrenimi Modelleri

Abstract

Ensuring food-supply safety requires reliable aroma/flavour detection, yet conventional tools (test paper, cyclotron, chromatography) often lack accuracy, broad detection range, speed, portability, and low cost. We propose a sensor array electronic nose (E nose) for aroma VOC analysis and prediction, combining statistical visualization/discrimination (t SNE, MANOVA) with machine learning models. Five classes were studied: chocolate, clove, cinnamon, ginger, and an unflavoured VOC set. Using a literature benchmark of 95.73% accuracy, we evaluated Random Forest, ExtraTrees, XGBoost, CatBoost, and a Stacking ensemble for 5 class prediction and one vs rest tests. All models perfectly identified chocolate in one vs rest prediction. Stacking performed poorly for unflavoured vs others (50%), whereas other aromas reached up to 94%. Statistical methods showed clear VOC separation, especially in one vs rest analyses.

Özet

Gıda güvenliği için güvenilir aroma tespiti gereklidir, ancak geleneksel yöntemler (test kağıdı, kromatografi) doğruluk, hız, taşınabilirlik ve maliyet açısından yetersizdir. Bu çalışmada, aroma VOC analizi için istatistiksel yöntemler (t-SNE, MANOVA) ve makine öğrenmesi modellerini birleştiren sensör dizili bir E-burun sistemi önerilmiştir. Çikolata, karanfil, tarçın, zencefil ve aromasız olmak üzere beş sınıf incelenmiştir. Literatürdeki %95.73 doğruluk referansı ile Random Forest, ExtraTrees, XGBoost, CatBoost ve Stacking ensemble modelleri değerlendirilmiştir. Tüm modeller ikili testlerde çikolatayı mükemmel şekilde tespit etmiştir. Stacking aromasız sınıfında %50, diğer aromalarda %94'e kadar başarı göstermiştir. İstatistiksel yöntemler özellikle ikili testlerde belirgin VOC ayrımı sağlamıştır.

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Cite This Article

Özer, İ., Çetin, O., Görür, K., Temurtaş, F. (2026). E-NoseFlavNet: Towards E-Nose based Aroma Flavour Analysis Empowered by Diverse ML Models via Sensor Array Technology. Mühendislik Bilimleri ve Araştırmaları Dergisi, Cilt: 8 Sayı: 1, 67–76. https://doi.org/10.46387/bjesr.1854260

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