Scopus Eşleşmesi Bulundu
63
Cilt
Scopus Yazarları: Sengul Uysal, Biljana Lončar, Aleksandra Cvetanović Kljakić, Gokhan Zengin
Özet
Olive leaves (Olea europaea L.) are rich source of bioactive compounds but are usually declared as bio-waste. Knowledge from traditional medicine, supported by modern research, has shown the high value of this bio-waste, and the great benefits of its use. The first step in exploiting the biological potential of olive leaves is the rationalization of the extraction process, through which extracts with high biological and market value could be obtained. This study aimed to investigate the influence of different extraction parameters, including solvent concentration, time, temperature, and ratio, on total phenolics, total flavonoids, antioxidant and enzyme-inhibiton activities. The optimal extraction conditions were determined using an Artificial Neural Network (ANN) model. The results revealed that the optimal conditions for the extraction of total bioactive compounds and antioxidant activities from olive leaves were obtained using ethanol concentration of 90%, extraction time 15 min, temperature of 45 °C, and plant:ethanol ratio 1:30. Under these conditions, the extract exhibited high levels of total phenolic content (TPC: 89.05 ± 3.9 mg GAE/g), total flavonoid content (TFC: 19.98 ± 0.38 mg RE/g), and potent antioxidant activity (DPPH: 195.12 ± 6.85 mg TE/g, ABTS: 270.73 ± 1.00 mg TE/g, CUPRAC: 66.14 ± 25.27 mg TE/g, FRAP: 233.58 ± 21.33 mg TE/g). Additionally, the extract demonstrated significant inhibitory effects on α-amylase (0.35 ± 0.00 mmol ACE/g) and tyrosinase (49.23 ± 1.22 mg KAE/g) enzymes. Taken together, the presented results may be valuable for the preparation of health-promoting formulations using olive leaves in the pharmaceutical and cosmeceutical industries.
Anahtar Kelimeler (Scopus)
Enyzme inhibiton
Antioxidant
Olive leaves
Artificial neural network (ANN)
Optimization
Anahtar Kelimeler
Enyzme inhibiton
Antioxidant
Olive leaves
Artificial neural network (ANN)
Optimization
Makale Bilgileri
Dergi
Food Bioscience
ISSN
2212-4292
Yıl
2025
/ 1. ay
Cilt / Sayı
63
Makale Türü
Özgün Makale
Hakemlik
Hakemli
Endeks
SCI
JCR Quartile
Q1
Yayın Dili
Türkçe
Kapsam
Uluslararası
Toplam Yazar
4 kişi
Erişim Türü
Basılı+Elektronik
Erişim Linki
Makaleye Git
Alan
Fen Bilimleri ve Matematik Temel Alanı
Biyoloji
YÖKSİS Yazar Kaydı
Yazar Adı
UYSAL ŞENGÜL,Loncar Biljana,Kljakic Aleksandra Cvetanovic,ZENGİN GÖKHAN
YÖKSİS ID
8468153
Hızlı Erişim
Metrikler
JCR Quartile
Q1
Yazar Sayısı
4