Antosiyaninlerin stabilitelerinin arttırılmasında enkapsülasyon teknolojisinin kullanımı
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
İnönü Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Antosiyanin bakımından zengin olan kırmızı pancar ekstraktı tek başına ve kombinasyon halinde maltodekstrin (MD), gam arabik (GA) ve sodyum kazeinat (SK) ile dondurarak kurutma tekniği kullanılarak enkapsüle edilmiştir. Enkapsülasyon etkinliği (%95.36), 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radikal süpürme gücü (%84.43), salınım oranı (%89.44) bakımından en başarılı kabuk materyal formülasyonu MD:GA:SK olarak belirlenmiştir. Kırmızı pancar antosiyaninlerini korumada en başarılı kabuk materyal formülasyonunu belirlemek amacı ile serbest ve mikroenkapsüle edilen pancar ekstraktı farklı pH (2.5, 4.5 ve 6.5) ve sıcaklık (40, 60, 80 ve 100 °C) koşullarına tabi tutulmuştur. İşlem sonunda örneklerdeki antosiyanin azalması kinetik parametreler ile değerlendirilmiştir. Antosiyanin parçalanması en iyi birinci derece kinetik model ile tanımlanmıştır. Mikroenkapsülasyon işlemi ile reaksiyon hız sabiti (k) azalmış, yarı ömür süresinin ise (t1/2) arttığı belirlenmiştir. 100°C ve 2.5 pH koşullarında serbest ve MD:GA:SK kapsülünde k değerinin 0.056 dan 0.013 1/dk' ya düştüğü, t1/2 1/2 1/2 değerinin ise 9.800'den 31.846'ya yükseldiği belirlenmiştir. Antosiyaninlerin sıcaklığa bağlı parçalanması Arrhenius eşitliği ile açıklanmaktadır. Pancar antosiyaninlerinin parçalanma aktivasyon enerjisi (????) pH ve mikrokapsülasyon ile artmaktadır. 6.5 pH da serbest ve MD, GA, MD:GA ve MD:GA:SK mikrokapsüllerinde antosiyanin parçalanması ???? değerleri sırasıyla 23.273, 27.586, 26.053, 57.027 ve 62.045 kj/mol olarak belirlenmiştir. Bu sonuçlar kırmızı pancar ekstraktı antosiyaninlerinin termal kararlılığını arttırmak amacıyla uygulanan mikrokapsülasyon prosesinde en uygun kabuk materyal formülasyonunun MD:GA:SK olduğunu ifade etmektedir. Sonuçlar yapay sinir ağı ile değerlendirildiğinde mikrokapsüle edilen pancar antosiyaninlerinin parçalanma davranışlarının tahmininde oldukça doğru sonuçlar elde edilmiştir. Anahtar Kelimeler: Kırmızı pancar, Antosiyanin, Enkapsülasyon, Termal degredasyon, Yapay sinir ağı
Beetroot extract, rich in anthocyanins, was microencapsulated into alone or combination of maltodextrin (MD) and gum arabic (GA) sodium caseinate (SC) by freeze-drying. The most successful wall formulation was determined as MD:GA:SC in terms of encapsulation efficiency (95.36%), 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging power (88.43%), release rate (89.44%). In order to determine the protective effect of the wall materials on beetroot anthocyanins, free and microencapsulated beetroot extract were subjected to different pH (2.5, 4.5, and 6.5) and temperatures (40, 60, 80, and 100 °C) treatments for 6 hours. The decrease in the amount of anthocyanin at the end of pH and heat treatments was evaluated with kinetic parameters. The degradation of anthocyanins was best described by the first-order kinetic model. It was determined that the reaction rate constant (k) decreased and the half-life (t1/2) increased with microencapsulation. The k and t1/2 values of free and microencapsulated (MD:GA:SC) beetroot anthocyanin at pH 2.5 and 100 °C decreased from 0.056 to 0.013 1/min and increased from 9.800 to 31.846 min, respectively. The temperature-dependent degradation of anthocyanins is described by the Arrhenius equation. The activation energy (?? ?? ) of beetroot anthocyanin degradation increased with pH and the microencapsulation process. The ?? ?? values for anthocyanin degradation in free and MD, GA, MD:GA, and MD:GA:SC microcapsules at pH 6.5 were determined as 23.273, 27.586, 26.053, 57.027, and 62.045 kJ/mol, respectively. These results indicated that the optimum wall material formulation was MD:GA:SC in the microencapsulation process to increase the thermal stability of beetroot anthocyanins. Artificial Neural Network model yielded accurate in prediction of degradation behaviors of free and microencapsulated beetroot anthocyanin. Keywords: Beetroot, Anthocyanin, Encapsulation, Thermal degredation, Artificial neural network
Beetroot extract, rich in anthocyanins, was microencapsulated into alone or combination of maltodextrin (MD) and gum arabic (GA) sodium caseinate (SC) by freeze-drying. The most successful wall formulation was determined as MD:GA:SC in terms of encapsulation efficiency (95.36%), 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging power (88.43%), release rate (89.44%). In order to determine the protective effect of the wall materials on beetroot anthocyanins, free and microencapsulated beetroot extract were subjected to different pH (2.5, 4.5, and 6.5) and temperatures (40, 60, 80, and 100 °C) treatments for 6 hours. The decrease in the amount of anthocyanin at the end of pH and heat treatments was evaluated with kinetic parameters. The degradation of anthocyanins was best described by the first-order kinetic model. It was determined that the reaction rate constant (k) decreased and the half-life (t1/2) increased with microencapsulation. The k and t1/2 values of free and microencapsulated (MD:GA:SC) beetroot anthocyanin at pH 2.5 and 100 °C decreased from 0.056 to 0.013 1/min and increased from 9.800 to 31.846 min, respectively. The temperature-dependent degradation of anthocyanins is described by the Arrhenius equation. The activation energy (?? ?? ) of beetroot anthocyanin degradation increased with pH and the microencapsulation process. The ?? ?? values for anthocyanin degradation in free and MD, GA, MD:GA, and MD:GA:SC microcapsules at pH 6.5 were determined as 23.273, 27.586, 26.053, 57.027, and 62.045 kJ/mol, respectively. These results indicated that the optimum wall material formulation was MD:GA:SC in the microencapsulation process to increase the thermal stability of beetroot anthocyanins. Artificial Neural Network model yielded accurate in prediction of degradation behaviors of free and microencapsulated beetroot anthocyanin. Keywords: Beetroot, Anthocyanin, Encapsulation, Thermal degredation, Artificial neural network
Açıklama
Anahtar Kelimeler
Gıda Mühendisliği, Food Engineering
