Modeling and Optimization of Ultrasound-Assisted Extraction of Bioactive Compounds from Allium sativum Leaves Using Response Surface Methodology and Artificial Neural Network Coupled with Genetic Algorithm

Abstract

This study explains the effect of ultrasound on the extraction of the bioactive compounds from garlic (Allium sativum L.) leaf powder. The experiment was carried out by varying the ultrasound amplitude (30–60%), treatment time (5–15 min), and ethanol concentration (40–60%) required to obtain the maximum extraction yield of total phenol content (TPC), total flavonoid content (TFC), and antioxidant activity. Rotatable central composite design (RCCD) provided experimental parameter combinations in the ultrasound-assisted extraction (UAE) of garlic leaf powder. The values of extraction yield, TPC, TFC, and antioxidant activity for the optimized condition of RSM were obtained at 53% amplitude, 13 min of treatment time, and 50% ethanol concentration. The values of the target compounds predicted at this optimized condition from RSM were 32.2% extraction yield, 9.9 mg GAE/g TPC, 6.8 mg QE/g TFC, and 58% antioxidant activity. The ANN-GA optimized condition for the leaf extracts was obtained at 60% amplitude, 13 min treatment time, and 53% ethanol concentration. The predicted values of optimized condition obtained by ANN-GA were recorded as 32.1738% extraction yield and 9.8661 mg GAE/g, 6.8398 mg QE/g, and 58.5527% for TPC, TFC, and antioxidant activity, respectively. The matured leaves of garlic, if not harvested during its cultivation, often go waste despite being rich in antioxidants and phenolic compounds. With the increased demand for the production of value-added products, the extraction of the bioactive compounds from garlic leaves can resolve waste management and potential health issues without affecting the crop yield through the process for high-end use in value addition.

Modeling and optimization of microwave-assisted extraction of total phenolics content from mango (Mangifera indica) peel using response surface methodology (RSM) and artificial neural networks (ANN)

“…1 e-f shows that increasing the ethanol content by up to 50% increases the antioxidant activity determined by ABTS, but an additional decrease can be observed ( Fig. 1 d-f), which may be due to the negative effect of the terms ( Shekhar, Prakash, Singha, Prasad, & Singh, 2023 ). In addition, it is observed that the interaction effects do not present significant effects ( ) on the antioxidant activity ( Table 4 ), which indicates that the antioxidant activity was individually affected by the ethanol concentration and not by their interactions for the antioxidant activity determined by ABTS of Avocado seeds ( Weremfo et al, 2020 ).…”

Impact of Sodium Silicate Supplemented, IR-Treated Panax Ginseng on Extraction Optimization for Enhanced Anti-Tyrosinase and Antioxidant Activity: A Response Surface Methodology (RSM) Approach

“…This phenomenon can be attributed to the breakdown of the plant cell wall, facilitated by water's role as a swelling agent, leading to enhanced extraction of phenolic compounds. The solvent ethanol aids in dissolving the polyphenols by disrupting the solid-solvent contact area, further enhancing the extraction process [45]. Ultrasonication has shown similar results when investigated against conventional extraction methods in food processing [46].…”

Valorization of biomass for food protein via deep eutectic solvent extraction: Understanding the extraction mechanism and impact on protein structure and properties

“…Although there are numerous HBA:HBD combinations that can solubilize proteins, it is currently unclear how all of those properties interact to affect protein extraction. In order to maximize the extraction of biomolecules from plant sources, new strategies incorporating computer-aided technology, such as machine learning, are very helpful when analyzing complicated data with various parameters (Ma et al, 2023;Shekhar et al, 2023). Shi et al (2022) successfully employed a machine learning algorithm to predict DES viscosity by considering the basic properties (i.e., molar mass of HBA and HBD, HBA:HBD molar ratio, and temperature), molecular fingerprint of the HBA:HBD composition, and water content as the model input.…”