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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 17

Monitoring the oxidative deterioration process in modified atmosphere packaged ground meat using a machine learning-based model

Modifiye atmosfer paketli kıymada oksidatif bozulma sürecinin makine öğrenmesi tabanlı model ile izlenmesi

Ganime Beyzanur Var(NEVŞEHİR HACI BEKTAŞ VELİ ÜNİVERSİTESİ)0000-0002-1757-7805
Cem Okan Özer(NEVŞEHİR HACI BEKTAŞ VELİ ÜNİVERSİTESİ)0000-0002-2030-1412
Pages
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Publication Date
2026-06-03
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AI Summary & Key Takeaways

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

The results suggested that evaluating oxidative indicators using machine learning can be a reliable tool for monitoring freshness of MAP-packed ground beef.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study investigated the use of a machine learning approach to predict oxidative changes during the storage of modified atmosphere packed (MAP) ground beef.
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Key Findings & Data:Peroxide, TBARS, titratable acidity, and mercaptan levels were used as indicators for oxidative deterioration and integrated into Random Forest model.
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Conclusion & Impact:The results suggested that evaluating oxidative indicators using machine learning can be a reliable tool for monitoring freshness of MAP-packed ground beef.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Oksidatif bozulma · makine öğrenmesi · sığır kıyması

Abstract

This study investigated the use of a machine learning approach to predict oxidative changes during the storage of modified atmosphere packed (MAP) ground beef. Peroxide, TBARS, titratable acidity, and mercaptan levels were used as indicators for oxidative deterioration and integrated into Random Forest model. The obtained model had an R2 value of 0.867, indicating acceptable prediction performance with low prediction errors. The model achieved a classification accuracy of 0.914. Furthermore, it was determined that the peroxide value is the most effective parameter reflecting the early stage of lipid oxidation, while the TBARS value represents the gradually developing secondary oxidation process. The results suggested that evaluating oxidative indicators using machine learning can be a reliable tool for monitoring freshness of MAP-packed ground beef.

Özet

Bu çalışma ile modifiye atmosfer paketli (MAP) kıymanın depolanması sırasında oksidatif değişikliklerin tahmin edilmesi için makine öğrenmesi yaklaşımının kullanımı araştırılmıştır. Peroksit, TBARS, titrasyon asitliği ve merkaptan seviyeleri oksidatif bozulma göstergeleri olarak kullanılmış ve rastgele orman modeline entegre edilmiştir. Elde edilen modelin R2 değeri 0.867 olarak belirlenmiş ve düşük tahmin hataları ile kabul edilebilir bir tahmin performansı göstermiştir. Model 0.914'lük bir sınıflandırma doğruluğu elde edilmiştir. Ayrıca, peroksit değerinin lipid oksidasyonunun erken sürecini yansıtan en etkili parametre olduğu, TBARS değerinin ise kademeli olarak gelişen ikincil oksidasyon sürecini temsil ettiği belirlenmiştir. Sonuçlar, makine öğrenmesi kullanılarak oksidatif göstergelerin değerlendirilmesinin, MAP ambalajında paketlenmiş kıymanın tazeliğini izlemek için güvenilir bir araç olabileceğini göstermiştir.

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Cite This Article

Var, G. B., Özer, C. O. (2026). Monitoring the oxidative deterioration process in modified atmosphere packaged ground meat using a machine learning-based model. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 17. https://doi.org/10.28948/ngumuh.1922495

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