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

Cigeroglu, Zeynep; ARAS, ÖMÜR; Pinto, Carlos; Bayramoglu, Mahmut; Kirbaslar, Sismail; Lorenzo, Jose; Barba, Francisco; Saraiva, Jorge; Sahin, Selin

doi:10.1002/jsfa.8987

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

Copy For Citation

Cigeroglu Z., ARAS Ö., Pinto C. A., Bayramoglu M., Kirbaslar S., Lorenzo J. M., ...More

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, vol.98, no.12, pp.4584-4596, 2018 (SCI-Expanded)

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
Istanbul University Affiliated: Yes

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.