Parametric analysis and machine learning for enhanced recovery of high-value sugar from date fruits using supercritical CO2 with co-solvents

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 CO2 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 R2 of 0.986 ± 0.010. Compared to conventional hot water extraction method (CHWE), the CO2-SFE process resulted in a 1.4-fold increase in TSC recovery and a 2.1-fold increase in organic acids recovery. CO2-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 CO2-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.