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Journals / Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 9 Sayı: 2

Catalase Enzyme Activity Measurement Methods: Current Approaches, Comparisons, and New Perspectives in the Literature

Katalaz Enzim Aktivite Ölçüm Yöntemleri: Literatürdeki Mevcut Yaklaşımlar, Karşılaştırmalar ve Yeni Perspektifler

Meryem Sena Akkuş(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0000-0003-2550-550X
Ceylan Bal(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0000-0002-1678-1281
Pages
1204–1218
Publication Date
2026-03-16
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AI Summary & Key Takeaways

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

This review comprehensively examines the current state of catalase activity measurement methods and offers new perspectives for future research.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Catalase is a critical antioxidant enzyme that regulates oxidative stress by breaking down hydrogen peroxide into water and oxygen.
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Key Findings & Data:It is commonly found in mammals, plants, and aerobic organisms, with particularly high concentrations in erythrocytes, the liver, and kidneys.
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Conclusion & Impact:This review comprehensively examines the current state of catalase activity measurement methods and offers new perspectives for future research.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Katalaz · Katalaz ölçüm yöntemleri · Yapay zeka

Abstract

Catalase is a critical antioxidant enzyme that regulates oxidative stress by breaking down hydrogen peroxide into water and oxygen. It is commonly found in mammals, plants, and aerobic organisms, with particularly high concentrations in erythrocytes, the liver, and kidneys. The NADPH-bound form of catalase, which has a tetrameric structure, protects the enzyme from oxidative inactivation. This study compared spectrophotometric, colorimetric, electroanalytical, titrimetric, chemiluminescence-based, and gel-based methods for determining catalase activity. Each method's advantages, disadvantages, and application areas were evaluated in detail, and suggestions were presented for developing more sensitive and reliable measurement techniques. In addition, the potential use of artificial intelligence and machine learning-supported analyses in measuring catalase activity was discussed. It was proposed that machine learning-based regression models could utilize large datasets to predict catalase activity and enhance measurement accuracy. This review comprehensively examines the current state of catalase activity measurement methods and offers new perspectives for future research.

Özet

Katalaz enzimi, hidrojen peroksidi su ve oksijene parçalayarak oksidatif stresi düzenleyen kritik bir antioksidan enzimdir. Memeliler, bitkiler ve aerobik organizmalarda yaygın olarak bulunur ve özellikle eritrositler, karaciğer ve böbrekte yüksek konsantrasyonlarda yer alır. Tetramerik bir yapıya sahip olan katalazın NADPH bağlı formu, oksidatif inaktivasyona karşı korunmasını sağlamaktadır. Bu çalışmada, katalaz aktivitesini belirlemeye yönelik spektrofotometrik, kolorimetrik, elektroanalitik, titrimetrik, kemilüminesans ve jel bazlı yöntemler karşılaştırılmıştır. Her yöntemin avantajları, dezavantajları ve kullanım alanları detaylı şekilde değerlendirilmiş, daha hassas ve güvenilir ölçüm teknikleri geliştirmek için öneriler sunulmuştur. Ayrıca, yapay zeka ve makine öğrenmesi destekli analizlerin katalaz aktivitesi ölçümlerinde nasıl kullanılabileceği ele alınmıştır. Makine öğrenmesi tabanlı regresyon modellerinin büyük veri kümelerinden yararlanarak katalaz aktivitesini tahmin edebileceği ve ölçüm hassasiyetini artırabileceği öne sürülmüştür. Bu derleme, katalaz aktivite ölçüm yöntemlerinin mevcut durumunu kapsamlı bir şekilde inceleyerek gelecekteki çalışmalar için yeni perspektifler sunmaktadır.

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Cite This Article

Akkuş, M. S., Bal, C. (2026). Catalase Enzyme Activity Measurement Methods: Current Approaches, Comparisons, and New Perspectives in the Literature. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 9 Sayı: 2, 1204–1218. https://doi.org/10.47495/okufbed.1704381

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