Optimization of ultrasound-assisted extraction of phenolic compounds from grapefruit (Citrus paradisi Macf.) leaves via D-optimal design and artificial neural network design with categorical and quantitative variables
JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, vol.98, no.12, pp.4584-4596, 2018 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 98 Issue: 12
- Publication Date: 2018
- Doi Number: 10.1002/jsfa.8987
- Journal Name: JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.4584-4596
- Keywords: ultrasound-assisted extraction, polyphenols, grapefruit leaves, D-optimal design, artificial neural network, optimization, RESPONSE-SURFACE METHODOLOGY, ISRAELI JAFFA RED, ANTIOXIDANT ACTIVITY, BY-PRODUCTS, FOOD-INDUSTRY, POLYPHENOLS, RECOVERY, YIELD, FRUITS, POWER
- Istanbul University-Cerrahpasa Affiliated: No
Abstract
BACKGROUND: The extraction of phenolic compounds from grapefruit leaves assisted by ultrasound-assisted extraction (UAE) was optimized using response surface methodology (RSM) by means of D-optimal experimental design and artificial neural network (ANN). For this purpose, five numerical factors were selected: ethanol concentration (0-50%), extraction time (15-60 min), extraction temperature (25-50 degrees C), solid:liquid ratio (50 - 100 gL(-1)) and calorimetric energy density of ultrasound (0.25-0.50 kW L-1), whereas ultrasound probe horn diameter (13 or 19 mm) was chosen as categorical factor.