Optimization of Maceration Conditions for Improving the Extraction of Phenolic Compounds and Antioxidant Effects of Momordica Charantia L. Leaves Through Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs)

From Aloe vera Leaf Waste to the Extracts with Biological Potential: Optimization of the Extractions, Physicochemical Characterization, and Biological Activities

In the study, the optimization of the extraction from Aloe vera leaf waste was performed via varying solid-to-solvent ratio, solvent type, extraction time, and technique (maceration, heat-, ultrasound-, and microwave-assisted extractions-HAE, UAE, and MAE, respectively). The optimal extraction conditions for achieving the highest polyphenol content are a 1:30 ratio, 70% ethanol, and 30 min of HAE. Total flavonoid and protein contents were significantly higher in the extract from MAE, while total condensed tannin content was the highest in HAE. LC-MS analysis quantified 13 anthraquinone and chromone compounds. The variations in the FT-IR spectra of the extracts obtained by different extraction procedures are minor. The influence of extraction conditions on the antioxidant ability of the extracts depended on applied antioxidant assays. The extracts possessed medium inhibition properties against Staphylococcus aureus and weak inhibitory activity against Enterococcus feacalis. The extracts had stimulative effect on HaCaT cell viability. Regarding the extraction yield, there was a significant difference between the used extraction techniques (MAE > HAE > maceration and UAE). The presented study is an initial step in the production of polyphenol-rich extracts from A. vera leaf waste aimed to be used for the potential preparation of pharmaceutical and cosmetic formulations for the skin.

Phenolic mapping and associated antioxidant activities through the annual growth cycle of sugar maple leaves

Concentrations of antioxidant components (analyzed by HPLC-UV) and antioxidant attributes (assayed by radical scavenging and non-radical redox potential methods) of sugar maple leaves (SML) from different harvesting times were investigated. Moreover, measurements of colorimetry, SEM, and FTIR spectroscopy-based characterization of leaves composition, throughout the growth cycle, were performed. Results showed that the antioxidant activities of SML are strongly correlated with phenolic contents and significantly (p < 0.05) varied with harvesting time where minimum amount of total phenolics (105.67 ± 13.16 mg GAE/g DM) and total flavonoids (3.27 ± 0.26 mg CTE/g DM) were found to be concentrated in Fall leaves. The absorption bands obtained from FTIR spectra revealed the presence of functional groups that have great significance towards the antioxidant activity of SML. Principal component analysis revealed that biosynthesis of maximum phenolic compounds in SML mostly occurs during the leaf expansion and growth phases. The obtained data provided a better understanding towards the effect of harvesting time on the phenolic mapping of SML in favor of its valorization into functional food ingredients.

Response surface methodology as a tool to optimize the extraction of bioactive compounds from plant sources

Response surface methodology (RSM) is a widely used mathematical and statistical technique for modeling and optimizing the process for the extraction of bioactive compounds. This review explains the optimization approach through the use of experimental design and empirical models for response prediction and the utilization of the desirability function for multiple response optimization. This paper also reviews recent studies on the application of RSM to optimize bioactive compound extraction processes such as conventional solvent extraction, microwave-assisted extraction, supercritical fluid extraction, and ultrasound-assisted extraction. Finally, the challenges associated with the use of RSM and the efforts made to improve RSM in the extraction process are also highlighted. Overall, this review informs many aspects of RSM that are occasionally ignored or insufficiently discussed with regard to the optimization of bioactive compound extraction processes, and it summarizes significant applications where RSM proved suitable. © 2022 Society of Chemical Industry.

Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds

Spirulina is a microalga and its phenolic compound is affected by growth mediums. In this study, Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference System (ANFIS) and Multilayer perceptron (MLP) models, and Step-Wise-Linear Regression (SWLR) were used to predict total phenolic compounds (TPC) of the spirulina algae. Spirulina productivity (P), extraction yield (EY), total flavonoids (TF), percent of flavonoid (%F) and percent of phenols (%P) are considered as input variables with the corresponding TPC as an output variable. From the result, TPC has a high positive correlation with the input variables with R = 0.99999. Also, the models showed that the ANFIS and SWLR gives superior result in the testing phase and increased its accuracy by 2% compared to MLP model in the prediction of TPC.

An efficient strategy based on two-stage chromatography and in vitro evaluation for rapid screening and isolation of acetylcholinesterase inhibitors from Scutellaria baicalensis Georgi

The extraction of Scutellaria baicalensis Georgi was investigated using the response surface methodology-genetic algorithm mathematical regression model, and the extraction variables were optimized to maximize the flavonoid yield. Furthermore, a simple and efficient ultrafiltration-liquid chromatography-mass spectrometry and molecular docking methods was developed for the rapid screening and identification of acetylcholinesterase inhibitors present in Scutellaria baicalensis Georgi. Subsequently, four major chemical constituents, namely baicalein, norwogonin, wogonin, and oroxylin A, were identified as potent acetylcholinesterase inhibitors. This novel approach, involving the use of ultrafiltration-liquid chromatography-mass spectrometry and molecular docking methods combined with stepwise flow rate counter-current chromatography and semi-preparative high-performance liquid chromatography, could potentially provide a powerful tool for the screening and extraction of acetylcholinesterase inhibitors from complex matrices and be a useful platform for the production of bioactive and nutraceutical ingredients. This article is protected by copyright. All rights reserved.

Supercritical carbon dioxide extraction of plant phytochemicals for biological and environmental applications - A review

Recently, supercritical fluid CO₂ extraction (SFE) has emerged as a promising and pervasive technology over conventional extraction techniques for various applications, especially for bioactive compounds extraction and environmental pollutants removal. In this context, temperature and pressure regulate the solvent density and thereby effects the yield, selectivity, and biological/therapeutic properties of the extracted components. However, the nature of plant matrices primarily determines the extraction mechanism based on either density or vapor pressure. The present review aims to cover the recent research and developments of SFE technique in the extraction of bioactive plant phytochemicals with high antioxidant, antibacterial, antimalarial, and anti-inflammatory activities, influencing parameters, process conditions, the investigations for improving the yield and selectivity. In another portion of this review focuses on the ecotoxicology and toxic metal recovery applications. Nonpolar properties of Sc-CO₂ create strong solvent strength via distinct intermolecular interaction forces with micro-pollutants and toxic metal complexes. This results in efficient removal of these contaminants and makes SFE technology as a superior alternative for conventional solvent-based treatment methods. Moreover, a compelling assessment on the therapeutic, functional, and solvent properties of SFE is rarely focused, and hence this review would add significant value to the SFE based research studies. Furthermore, we mention the limitations and potential of future perspectives related to SFE applications.

Antioxidant Molecules from Plant Waste: Extraction Techniques and Biological Properties

The fruit, vegetable, legume, and cereal industries generate many wastes, representing an environmental pollution problem. However, these wastes are a rich source of antioxidant molecules such as terpenes, phenolic compounds, phytosterols, and bioactive peptides with potential applications mainly in the food and pharmaceutical industries, and they exhibit multiple biological properties including antidiabetic, anti-obesity, antihypertensive, anticancer, and antibacterial properties. The aforementioned has increased studies on the recovery of antioxidant compounds using green technologies to value plant waste, since they represent more efficient and sustainable processes. In this review, the main antioxidant molecules from plants are briefly described and the advantages and disadvantages of the use of conventional and green extraction technologies used for the recovery and optimization of the yield of antioxidant naturals are detailed; finally, recent studies on biological properties of antioxidant molecules extracted from plant waste are presented here.

Potential for Fourier transform infrared (FTIR) spectroscopy toward predicting antioxidant and phenolic contents in powdered plant matrices

Mid-infrared spectroscopy is finding an increasing number of applications; however, many of its potential uses remain unexplored. In this study, mid-infrared spectroscopy is applied to predict total antioxidant capacity and phenolic contents of powdered matrices of 14 diverse plant species. In all instances, the optimum prediction models were found using standard normal variate smoothing as a pre-processing method. The results show high correlation between the FTIR predicted and chemically determined values, namely R² values of 0.962 for total phenolics, 0.829 for cupric reducing antioxidant potential (CUPRAC) and 0.911 for ferric reducing antioxidant potential (FRAP). The relative RMSE found for validation indicated that total phenolic content could be predicted with higher accuracy than CUPRAC or FRAP. This pilot study highlights the promise of this technology for plant breeders and a range of industries where rapid screening of many samples for antioxidant and/or phenolic content is envisaged.

Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation

Pomegranate (Punica granatum L.) peel is a potential source of polyphenols known for their activity against foodborne pathogen bacteria. In this study, the effects of pomegranate peel extraction time (10–60 min), agitation speed (120–180 rpm), and solvent/solid ratio (10–30) on phytochemical content and antibacterial activity were determined. Response surface methodology (RSM) and artificial neural network (ANN) methods were used, respectively, for multiresponse optimization and predictive modelling. Compared with the original conditions, the total phenolic content (TPC), the total flavonoid content (TFC), and the total anthocyanin content (TAC) increased by 56.22, 63.47, and 64.6%, respectively. Defined by minimal inhibitory concentration (MIC), the maximum of antibacterial activity was higher than that from preoptimized conditions. With an extraction time of 11 min, an agitation speed 125 rpm, and a solvent/solid ratio of 12, anti-S. aureus activity remarkably decreased from 1.56 to 0.171 mg/mL. Model comparisons through the coefficient of determination (R²) and mean square error (MSE) showed that ANN models were better than the RSM model in predicting the photochemical content and antibacterial activity. To explore the mode of action of the pomegranate peel extract (PPE) at optimal conditions against S. aureus and S. enterica, Chapman and Xylose Lysine Deoxycholate broth media were artificially contaminated at 10⁴ CFU/mL. By using statistical approach, linear (ANOVA), and general (ANCOVA) models, PPE was demonstrated to control the two dominant foodborne pathogens by suppressing bacterial growth.