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Parametric analysis and machine learning for enhanced recovery of high-value sugar from date fruits using supercritical CO2 with co-solvents.
- Source :
- Journal of CO2 Utilization; Jun2023, Vol. 72, pN.PAG-N.PAG, 1p
- Publication Year :
- 2023
-
Abstract
- The extraction of date sugar using supercritical extraction is a process that is still in its formative stages. In this study, a comprehensive parametric analysis of the supercritical fluid extraction (SFE) process using supercritical CO 2 with water/ethanol as co-solvents was performed to achieve maximum recovery of date sugar extract. The results showed that the maximum total sugar content (TSC) was 70.45 ± 0.01 g/100 g of DFP. This was made up of 7.42 g/100 g fructose, 6.49 g/100 g glucose, and 56.54 g/100 g sucrose. This was attained with 15 v/v% water as co-solvent, 50 ℃, and 200 bar. In addition, machine learning with non-linear regression and artificial neural network (ANN) ensembles was used for TSC prediction. The ANN results showed a strong correlation between operating parameters and sugar recovery with a total R<superscript>2</superscript> of 0.986 ± 0.010. Compared to conventional hot water extraction method (CHWE), the CO 2 -SFE process resulted in a 1.4-fold increase in TSC recovery and a 2.1-fold increase in organic acids recovery. CO 2 -SFE demonstrated comparable TSC results with a difference of only 1.2% when compared to the ultrasound-assisted extraction ''USAE' method. The results of the detailed chemical analysis (HPLC and FT-IR) and morphological analysis (SEM) showed that the USAE and CO 2 -SFE were more efficient than CHWE. Supercritical extraction with co-solvents is particularly effective in recovering date sugar from date fruit, making it a desirable ingredient in a variety of food products. [Display omitted] • Supercritical CO 2 with co-solvent yielded 1.4-fold higher extraction than conventional method. • Neural network modeling of sugar extraction yielded an exceptional R<superscript>2</superscript> of 0.995. • Proposed approach also extracted 2.1-fold more essential acids than conventional method. • Surface morphology analysis revealed the residue had a smooth surface without distortions. [ABSTRACT FROM AUTHOR]
- Subjects :
- DATES (Fruit)
MACHINE learning
SUPERCRITICAL fluid extraction
SUGAR
SUGARS
Subjects
Details
- Language :
- English
- ISSN :
- 22129820
- Volume :
- 72
- Database :
- Supplemental Index
- Journal :
- Journal of CO2 Utilization
- Publication Type :
- Academic Journal
- Accession number :
- 164379172
- Full Text :
- https://doi.org/10.1016/j.jcou.2023.102511