LAPSE:2024.0980
Published Article
LAPSE:2024.0980
Optimizing the Extraction Process of Value-Added Products from Olive Cake Using Neuro-Fuzzy Models
June 7, 2024
Abstract
The use of olive cake, an abundant residue in the olive oil industry, has been studied by developing a biorefinery scheme. The aim was to develop a novel, efficient, and environmentally friendly strategy for the valorization of olive cake, contributing to sustainable agriculture. A special extraction procedure based on a combination of hydrothermal treatments with liquid/liquid extractions was designed to produce value-added products, along with solids that can be used for energy or adsorbent production. The optimal extraction conditions were determined by exploring the influence of the operating variables (temperature, extraction time, solvent type, solvent/extract ratio, extraction stages, and pH) on the extraction yield. The decision about the optimal conditions was made by adjusting the experimental results to a neuro-fuzzy model. Glucose and inositol showed similar response surfaces, allowing simultaneous concentration in a single process. Under optimal extraction conditions, the concentration of inositol increased by up to 70%, while glucose and fructose increased by 70 and 30 times, respectively, compared to the initial feed. The proposed methodology successfully extracted significant amounts of bioactive polyols (mainly inositol) (1126 mg/L), saccharides (15,960 mg/L glucose, 385 mg/L xylose, 5550 mg/L fructose, 165 mg/L lactose, and 248 mg/L sucrose), and polyphenols (4792 mg/L) under mild conditions, i.e., 30 °C and 30 min. Thus, olive cake extracts have a great unexploited potential for application in several industrial sectors, including, but not limited to, food and pharmaceuticals.
Keywords
Extraction, inositol, neuro-fuzzy models, olive cake, polyphenols, sugars
Suggested Citation
Lozano EJ, Blázquez G, Calero M, Martín-Lara MÁ, Pérez-Huertas S, Pérez A. Optimizing the Extraction Process of Value-Added Products from Olive Cake Using Neuro-Fuzzy Models. (2024). LAPSE:2024.0980
Author Affiliations
Lozano EJ: Department of Chemical Engineering, University of Granada, Avenida Fuente Nueva s/n, 18071 Granada, Spain
Blázquez G: Department of Chemical Engineering, University of Granada, Avenida Fuente Nueva s/n, 18071 Granada, Spain [ORCID]
Calero M: Department of Chemical Engineering, University of Granada, Avenida Fuente Nueva s/n, 18071 Granada, Spain
Martín-Lara MÁ: Department of Chemical Engineering, University of Granada, Avenida Fuente Nueva s/n, 18071 Granada, Spain [ORCID]
Pérez-Huertas S: Department of Chemical Engineering, University of Granada, Avenida Fuente Nueva s/n, 18071 Granada, Spain [ORCID]
Pérez A: Department of Chemical Engineering, University of Granada, Avenida Fuente Nueva s/n, 18071 Granada, Spain [ORCID]
Journal Name
Processes
Volume
12
Issue
2
First Page
317
Year
2024
Publication Date
2024-02-02
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr12020317, Publication Type: Journal Article
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LAPSE:2024.0980
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https://doi.org/10.3390/pr12020317
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