Phenolics extraction from sweet potato peels: modelling and optimization by response surface modelling and artificial neural network

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.

Reclaiming Agriceuticals from Sweetpotato (Ipomoea batatas [L.] Lam.) By-Products

Sweetpotato (SP, Ipomoea batatas [L.] Lam.) is a globally significant food crop known for its high nutritional and functional values. Although the contents and compositions of bioactive constituents vary among SP varieties, sweetpotato by-products (SPBs), including aerial parts, storage root peels, and wastes generated from starch processing, are considered as excellent sources of polyphenols (e.g., chlorogenic acid, caffeoylquinic acid, and dicaffeoylquinic acid), lutein, functional carbohydrates (e.g., pectin, polysaccharides, and resin glycosides) or proteins (e.g., polyphenol oxidase, β-amylase, and sporamins). This review summarises the health benefits of these ingredients specifically derived from SPBs in vitro and/or in vivo, such as anti-obesity, anti-cancer, antioxidant, cardioprotective, and anti-diabetic, evidencing their potential to regenerate value-added bio-products in the fields of food and nutraceutical. Accordingly, conventional and novel technologies have been developed and sometimes combined for the pretreatment and extraction processes aimed at optimising the recovery efficiency of bioactive ingredients from SPBs while ensuring sustainability. However, so far, advanced extraction technologies have not been extensively applied for recovering bioactive compounds from SPBs except for SP leaves. Furthermore, the incorporation of reclaimed bioactive ingredients from SPBs into foods or other healthcare products remains limited. This review also briefly discusses current challenges faced by the SPB recycling industry while suggesting that more efforts should be made to facilitate the transition from scientific advances to commercialisation for reutilising and valorising SPBs.

Optimisation of Polyphenols Extraction from Wild Bilberry Leaves-Antimicrobial Properties and Stability Studies

Polyphenolic extracts from natural sources have received great interest due to their beneficial properties for human health. A method to reduce their variability is to use the design of experiments which allows a limited number of experiments to be performed while exploring the experimental space. Firstly, a 23-full factorial model was used to investigate the polyphenols extraction from wild bilberry leaves. Spectrophotometric data (the content of polyphenols, flavonoids, chlorophyll and radical scavenger activity) and extraction yield were used as responses, and six statistical models were determined depending on the two numerical factors (temperature and alcohol % of ethanol-water mixture) being significant (p < 0.05) in all cases. Numerical optimisation performed by Design Expert 13 software correlates well with the chemical profile determined by high-performance liquid chromatography and the amount of the polyphenol. Afterwards, under the optimised conditions, an extract was prepared in three extraction steps for which composition, chemical stability and antimicrobial properties were evaluated. The antimicrobial potential of the extract was compared with that of the standard compounds (rutin and chlorogenic acid), and the results supported a synergistic effect of the extract components.

Application of Artificial Intelligence in Food Industry—a Guideline

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

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.

Optimization of the silica-gel adsorption technique for the extraction of phytosterol glycosides from soybean lecithin powder using response surface methodology and artificial neural network models

Phytosterol glycosides (PGs), comprising both acylated steryl glycosides (ASGs) and steryl glycosides (SGs), are active ingredients with benefits for human use. Here, we aimed to optimize the silica-gel adsorption technique for the extraction of PGs from soybean lecithin powder, which contains 5 to 10% of these glycolipids. Both response surface methodology (RSM) and artificial neural networks (ANNs) were applied to optimize the PG extraction parameters (X1 = silica-gel dosage, X2 = adsorption temperature, and X3 = lecithin concentration) for high-purity phospholipid and PG production, and their prediction and optimization accuracies were compared. Although both models fitted well with the experimental data, the ANN model demonstrated better accuracy for predicting and optimizing the conditions using four interrelated dependent variables (Y1 = phospholipid yield, Y2 = ASG recovery, Y3 = SG recovery, and Y4 = PG purity) and had a higher coefficient of determination and lower root mean square error and absolute average deviation. After digitally setting the percentages of the four dependent variables for phospholipid and PG production, the ANN-optimized phospholipid product (Y1 = 88.07%, Y2 = 98.89%, Y3 = 100%, and Y4 = 49.03%) was acquired at X1 = 3.54 g/g, X2 = 26 °C, and X3 = 43 mg/mL, whereas the PG product (Y1 = 83.83%, Y2 = 97.64%, Y3 = 100%, and Y4 = 59.21%) was obtained at X1 = 2.00 g/g, X2 = 28.38 °C, and X3 = 41 mg/mL. In conclusion, the ANN method was better than RSM for the optimization of the silica-gel adsorption technique for PG extraction from soybean lecithin powder. PRACTICAL APPLICATION: This paper lays a theoretical foundation for the optimization of the industrial production of phytosterol glycosides and the comprehensive utilization of lecithin resources.

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.

Comparative study on antidiabetic, cytotoxicity, antioxidant and antibacterial properties of biosynthesized silver nanoparticles using outer peels of two varieties of Ipomoea batatas (L.) Lam

BACKGROUND

Ipomoea batatas (L.) Lam.(Ib) has high content of various beneficial nutrients which helps in improving and maintaining human health. It is well known as a functional food and also a valuable source of unique natural products. It contains various phenolic and flavonoid bioactive compounds.

METHODS

In this study, using the outer peel of two varieties of Ib : Korean red skin sweet potato and Korean pumpkin sweet potato, silver nanoparticles (AgNPs) were synthesized (termed Ib1-AgNps and Ib2-AgNps), respectively. Characterization of Ib1-AgNPs and Ib2-AgNPs was carried out through scanning electron microscopy, Fourier-transform infrared (FT-IR) spectroscopy, energy-dispersive X-ray analysis, X-ray powder diffraction and UV-Vis spectroscopy. Further, the bio-potential of the synthesized AgNPs was investigated by antidiabetic (α-glucosidase assay), antioxidant (free radical scavenging assays), antibacterial (disc diffusion method) and cytotoxicity assays (cell viability against HepG2 cells).

RESULTS

FT-IR spectroscopy revealed the contribution of bioactive compounds existing in Ib1 and Ib2 extracts, in the biosynthesis and equilibrium of the AgNPs. Although the Ib2-AgNPs had a higher atomic percentage of Ag in comparison with Ib1-AgNPs, in the antidiabetic assay, the inhibition percentage of α-glucosidase was higher for AgNPs of Ib1 than Ib2, at all three concentrations examined. From the cytotoxicity results, HepG2 cancer cells were more sensitive to the Ib1-AgNPs in comparison to the Ib2-AgNPs-treated HepG2 cells. The antioxidant prospective was higher in Ib2-AgNPs than Ib1-AgNPs. Moreover, the Ib2-AgNPs showed inhibitory action against all five tested pathogenic bacteria, producing an inhibition zone of 8.74-11.52 mm while Ib1-AgNPs had an inhibitory effect on four of them, with an 8.67-11.23 (mm) inhibition zone.

CONCLUSIONS

Overall, the results concluded that the Ib2-AgNPs exhibited relatively higher functional activity than Ib1-AgNPs, which might be credited to the greater abundance of bioactive compounds existing in Ib2 extract that acted as reducing as well as capping agents in the synthesis of Ib2-AgNPs. Overall, the current study highlights a novel cost-effective and eco-friendly AgNPs synthesis using food waste peels with biocompatibility and could be potentially utilized in biomedical and pharmaceutical industries.