Acacia Seyal gum (ASG), also known as gum Arabic, is an antioxidant-rich soluble fiber. ASG has been reported to have many biological activities, including anticancer, antidiabetic, antiulcer, and immunomodulatory activity. Extraction of bioactive compounds from ASG is commonly performed using conventional extraction methods. However, these techniques have certain limitation in terms of extraction time, energy, and solvent requirements. Ultrasound-assisted extraction (UAE) could be used as an alternative technique to extract bioactive compounds in less time, at low temperature, and with less energy and solvent requirements. In this study, the UAE extraction of ASG was optimized using response surface methodology (RSM). A face-centered central composite design (FCCCD) was used to monitor the effect of different independent factors of ultrasound operation (sonication time, temperature, and solvent ratio) on ASG extraction yield. In addition, screening and characterization of phytochemicals in 60% ethanol ASG extract was carried out using Raman microscopy, Fourier transform infrared spectroscopy (FTIR), and gas chromatography time-of-flight mass spectroscopy (GC-TOFMS) analysis. The results indicated that, under optimal conditions (extraction time 45 min, extraction temperature 40 °C, and solid–liquid ratio of 1:25 g/mL), the yield of ASG was 75.87% ± 0.10. This yield was reasonably close to the predicted yield of 75.39% suggested by the design of experiment. The ANOVA revealed that the model was highly significant due to the low probability value (p < 0.0001). Raman spectrum fingerprint detected polysaccharides, such as galactose and glucose, and protein like lysine and proline, while FTIR spectrum revealed the presence of functional groups peaks value of alkanes, aldehydes, aliphatic amines, and phenol. GC-TOFMS spectroscopic detected the presence of strong d-galactopyranose, carotenoid, and lycopene antioxidant compounds. In conclusion, this study demonstrated that the UAE technique is an efficient method to achieve a high yield of ASG extracts. The selected model is adequate to optimize the extraction of several chemical compounds reported in this study.
Correlation between response surface methodology and artificial neural network in the prediction of bioactive compounds of unripe Musa acuminata peel
