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

Influence of ultrasound-assisted extraction of bioactives from garlic (Allium sativum) sprouts using response surface methodology

Garlic sprouts have nutritional and culinary significances due to good source of chemical compounds. The present study aimed to determine the optimized condition for maximum extraction of polyphenols from garlic sprouts through RSM (response surface methodology). However, the proximate, color and microbial analysis were performed on the freeze-dried garlic sprouts. The influences of extraction time (3-9 min), amplitude (50-90 %), ethanol concentration (25-75 %) and solid-liquid ratio (20-40 g/mL) on the extraction yield and total phenolic content (TPC) were investigated. The highest yield was observed on time (9 min), amplitude (70 %), ethanol (25 %) and solid-liquid ratio (30 g/mL) whereas the maximum TPC were measured on time (3 min), amplitude (70 %), ethanol (75 %) and solid-liquid ratio (30 g/mL). The optimal extraction conditions were 5.20 min, 61.1 % ethanol, 71.4 % amplitude and 30.6 g/mL solid-liquid ratio. However, the estimated results of the study are in accordance with the experimental outputs. In addition, chromatographic measurement showed the characterization of polyphenols in garlic sprout extract. Garlic can be fruitful in pharmaceutical and food industries due to a good source of bioactive compounds.

Artificial neural network in optimization of bioactive compound extraction: recent trends and performance comparison with response surface methodology

Plant products and its by-products are rich source of bioactive compounds like antioxidants, flavonoids, phenolics, pigments and phytochemicals. Bioactive compound's health-promoting properties are well studied. However, optimal extraction of bioactive compounds is a complex, labour- and time-intensive process. It is also highly sensitive to experimental variables. Predicting output variables can reduce the experimental work and has positive environmental impact. Various tools such as Response Surface Methodology (RSM), Mathematical modelling have been commonly used for optimization and predictive modelling of the extraction process. Although mathematical modelling and RSM are efficient, recent studies have used Artificial Neural Network (ANN) which is more efficient and accurate and can perform extensive predictions with high accuracy. The manuscript focuses on current trends of ANN application in optimizing the extraction of bioactive compounds. In this study, ANN and RSM have been compared in terms of their performances in optimizing and modelling the extraction of bioactive compounds from herbs, medicinal plants, fruit, vegetables, and their by-products. The findings from the literature indicate that efficiency of ANN was superior to RSM. Future researches can focus on use of ANN in industrial optimization experiments.

Ultrasonic extraction of Inula viscosa: Enhancing antioxidant bioactivity and its application in sunflower oil as an antioxidant

The objective of this study was to optimize the ultrasound-assisted extraction (UAE) of Inula viscosa, focusing on the extraction yield, total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity and to evaluate its antioxidant effect in sunflower oil (SFO) storage. A water-ethanol binary solvent system was applied to extract bioactive components sustainably. Extraction parameters (temperature, time, ethanol concentration, and solvent-to-solid ratio) were optimized using a central composite rotatable design, achieving high accuracy (R2 > 0.974). Optimum conditions were 54 % (v/v) ethanol concentration, 60 °C, 31 min, and a 15 (mL/g) solvent-to-solid ratio resulting in a yield of 24.72 g/g (%), TPC of 489.54 mg gallic acid/g, TFC of 149.81 mg quercetin/g, and IC50 of 18.21 µg/mL. UAE outperformed Soxhlet extraction in yield, bioactive compound composition, and antioxidant capacity. Strong correlations were found between TPC, TFC, and antioxidant capacity, with TFC having a more significant impact. I. viscosa extract was found to be a potent antioxidant and delay the oxidation of SFO during accelerated storage due to peroxide value and oxidative induction time analysis. Microstructural analysis illuminated the structural changes induced by the extraction methods. In conclusion, this study not only optimized UAE of I.viscosa, showing superior efficiency and antioxidant capacity, but also demonstrated the practical application of I.viscosa in enhancing sunflower oil shelf life, thereby providing valuable insights for the field of food engineering and antioxidant research.

Reactive extraction of muconic acid by hydrophobic phosphonium ionic liquids - Experimental, modelling and optimisation with Artificial Neural Networks

Muconic acid is a six-carbon dicarboxylic acid with conjugated double bonds that finds extensive use in the food (additive), chemical (production of adipic acid, monomer for functional resins and bio-plastics), and pharmaceutical sectors. The biosynthesis of muconic acid has been the subject of recent industrial and scientific attention. However, because of its low concentration in aqueous solutions and high purity requirement, downstream separation presents a significant problem. Artificial Neural Networks and Differential Evolution were used to optimize process parameters for the recovery of muconic acid from aqueous streams in a system with n-heptane as an organic diluent and ionic liquids as extractants. The system using 120 g/L tri-hexyl-tetra-decyl-phosphonium decanoate dissolved in n-heptane, pH of the aqueous phase 3, 20 min contact time, and 45 °C temperature assured a muconic acid extraction efficiency of 99,24 %. Low stripping efficiency compared to extraction efficiency was observed for the optimum conditions on the extraction step (120 g/L ionic liquids dissolved in heptane). However, re-extraction efficiencies obtained for the recycled organic phase in three consecutive stages were close to the first extraction stage. The mechanism analysis proved that the analysed phosphonium ionic liquids (PILSs) extracts only undissociated molecules of muconic acid through H-bonding.

Modeling and Optimization of Ellagic Acid from Chebulae Fructus Using Response Surface Methodology Coupled with Artificial Neural Network

The dried ripe fruit of Terminalia chebula Retz. is a common Chinese materia medica, and ellagic acid (EA), isolated from the plant, is an important bioactive component for medicinal purposes. This study aimed to delineate the optimal extraction parameters for extracting the EA content from Chebulae Fructus (CF), focusing on the variables of ethanol concentration, extraction temperature, liquid-solid ratio, and extraction time. Utilizing a combination of the response surface methodology (RSM) and an artificial neural network (ANN), we systematically investigated these parameters to maximize the EA extraction efficiency. The extraction yields for EA obtained under the predicted optimal conditions validated the efficacy of both the RSM and ANN models. Analysis using the ANN-predicted data showed a higher coefficient of determination (R2) value of 0.9970 and a relative error of 0.79, compared to the RSM's 2.85. The optimal conditions using the ANN are an ethanol concentration of 61.00%, an extraction temperature of 77 °C, a liquid-solid ratio of 26 mL g-1 and an extraction time of 103 min. These findings significantly enhance our understanding of the industrial-scale optimization process for EA extraction from CF.

Ultrasound-Assisted Extraction of Bioactive Compounds from Broccoli By-Products

The objective of this work was to gain insight into the operating conditions that affect the efficiency of ultrasound-assisted extraction (UAE) parameters to achieve the best recovery of bioactive compounds from broccoli leaf and floret byproducts. Therefore, total phenolic content (TPC) and the main sulfur bioactive compounds (sulforaphane (SFN) and glucosinolates (GLSs)) were assayed. Distilled water was used as solvent. For each byproduct type, solid/liquid ratio (1:25 and 2:25 g/mL), temperature (25, 40, and 55 °C), and extraction time (2.5, 5, 7.5, 10, 15, and 20 min) were the studied variables to optimize the UAE process by using a kinetic and a cubic regression model. TPC was 12.5-fold higher in broccoli leaves than in florets, while SFN was from 2.5- to 4.5-fold higher in florets regarding the leaf's extracts obtained from the same plants, their precursors (GLS) being in similar amounts for both plant tissues. The most efficient extraction conditions were at 25 °C, ratio 2:25, and during 15 or 20 min according to the target phytochemical to extract. In conclusion, the type of plant tissue and used ratio significantly influenced the extraction of bioactive compounds, the most efficient UAE parameters being those with lower energy consumption.

Optimization of Ultrasound-Assisted Extraction of Verbascum sinaiticum Leaves: Maximal Phenolic Yield and Antioxidant Capacity

Verbascum sinaiticum (Qetetina or yeahya Joro) is a medicinal plant with secondary metabolites such as phenolics, flavonoids, glycosides, saponins, and alkaloids. This study was designed to optimize the ultrasonic-assisted extraction (UAE) parameters to enhance the phenolic content and characterize the phenolic compounds using ultra-high-performance liquid chromatography, coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry (UHPLC-ESI-QTOF-MS/MS), and antioxidant activities in Verbascum sinaiticum extract. Extraction time, sample-to-solvent ratio, and extraction temperature were considered for UAE optimization. It was found that UAE generated the highest extraction yield (21.6%), total phenolic content (179.8 GAE mg/g), total flavonoid content (64.49 CE mg/g), DPPH (61.85 µg/mL), and ABTS (38.89 µg/mL) when compared to maceration extraction. Metabolite analysis in this study showed the detection of 17 phenolic compounds, confirming antioxidant capacities. The optimization parameters have significant effects on phenolic compounds. Scanning electron microscopy showed the presence of structural changes when UAE was used over the maceration method. The optimized UAE parameters for extraction temperature (41.43 °C), sample-to-solvent ratio (36.32 g/mL), and extraction time (33.22 min) for TPC were obtained. This study shows the potential application for UAE of Verbascum sinaiticum leaves in the development of pharmaceutical and nutraceutical products.

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

Ginseng has long been widely used for its therapeutic potential. In our current study, we investigated the impact of abiotic stress induced by infrared (IR) radiations and sodium silicate on the upregulation of antioxidant and anti-tyrosinase levels, as well as the total phenolic and total flavonoid contents of the Korean ginseng (Panax ginseng C.A. Meyer) variety Yeonpoong. The RSM-based design was used to optimize ultrasonic-assisted extraction time (1-3 h) and temperature (40-60 °C) for better anti-tyrosinase activity and improved antioxidant potential. The optimal extraction results were obtained with a one-hour extraction time, at a temperature of 40 °C, and with a 1.0 mM sodium silicate treatment. We recorded maximum anti-tyrosinase (53.69%) and antioxidant (40.39%) activities when RSM conditions were kept at 875.2 mg GAE/100 g TPC, and 3219.58 mg catechin/100 g. When 1.0 mM sodium silicate was added to the media and extracted at 40 °C for 1 h, the highest total ginsenoside content (368.09 mg/g) was recorded, with variations in individual ginsenosides. Ginsenosides Rb1, Rd, and F2 were significantly affected by extraction temperature, while Rb2 and Rc were influenced by the sodium silicate concentration. Moreover, ginsenoside F2 increased with the sodium silicate treatment, while the Rg3-S content decreased. Interestingly, higher temperatures favored greater ginsenoside diversity while sodium silicate impacted PPD-type ginsenosides. It was observed that the actual experimental values closely matched the predicted values, and this agreement was statistically significant at a 95% confidence level. Our findings suggest that the application of IR irradiation in hydroponic systems can help to improve the quality of ginseng sprouts when supplemented with sodium silicate in hydroponic media. Optimized extraction conditions using ultrasonication can be helpful in improving antioxidant and anti-tyrosinase activity.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

Design of Experiments for Optimizing Ultrasound-Assisted Extraction of Bioactive Compounds from Plant-Based Sources

Plant-based materials are an important source of bioactive compounds (BC) with interesting industrial applications. Therefore, adequate experimental strategies for maximizing their recovery yield are required. Among all procedures for extracting BC (maceration, Soxhlet, hydro-distillation, pulsed-electric field, enzyme, microwave, high hydrostatic pressure, and supercritical fluids), the ultrasound-assisted extraction (UAE) highlighted as an advanced, cost-efficient, eco-friendly, and sustainable alternative for recovering BC (polyphenols, flavonoids, anthocyanins, and carotenoids) from plant sources with higher yields. However, the UAE efficiency is influenced by several factors, including operational variables and extraction process (frequency, amplitude, ultrasonic power, pulse cycle, type of solvent, extraction time, solvent-to-solid ratio, pH, particle size, and temperature) that exert an impact on the molecular structures of targeted molecules, leading to variations in their biological properties. In this context, a diverse design of experiments (DOEs), including full or fractional factorial, Plackett-Burman, Box-Behnken, Central composite, Taguchi, Mixture, D-optimal, and Doehlert have been investigated alone and in combination to optimize the UAE of BC from plant-based materials, using the response surface methodology and mathematical models in a simple or multi-factorial/multi-response approach. The present review summarizes the advantages and limitations of the most common DOEs investigated to optimize the UAE of bioactive compounds from plant-based materials.

Comparison of Antioxidant and Antiproliferative Effects of Various Forms of Garlic and Ramsons

Garlic is known to be rich in antioxidants, inhibit the proliferation of various cancer cells, and hamper cancer formation and growth, but various forms of garlic can differ greatly in these respects. This study aimed to compare the antioxidant properties of acetone, ethanol, and aqueous extracts of fresh Polish and Spanish garlic, black and granulated garlic, as well as fresh and dried ramsons. Extracts of black and granulated garlic showed the lowest total antioxidant capacity (TAC). The content of phenolic compounds correlated with TAC measured by ABTS• decolorization and FRAP methods, and with the results of FRAP and DPPH• decolorization assays. Garlic extracts inhibited the proliferation of PEO1 and SKOV3 ovarian cancer cells and, usually to a smaller extent, MRC-5 fibroblasts. PBS extracts of fresh Spanish garlic showed the highest potency for inhibition of proliferation of PEO1 cells (IC50 of 0.71 µg extract dry mass/100 µL medium). No significant correlation was found between the potency for inhibition of proliferation and the content of phenolics or flavonoids, confirming that phenolics are the main determinants of TAC but do not contribute significantly to the antiproliferative effects of garlic.