Optimization of the extraction conditions of Nypa fruticans Wurmb. using response surface methodology and artificial neural network

Chinchkar A, Singh A, Upadhyay A, Mehari B. Ultrasonic-assisted extraction and UHPLC determination of ascorbic acid, polyphenols, and half-maximum effective concentration in Citrus medica and Ziziphus spina-christi fruits using multivariate experimental design

This study aimed to determine the concentrations of ascorbic acid and polyphenols in fruits and peels of Citrus medica and Ziziphus spina-christi grown in Ethiopia. Conditions of ultrasound-assisted extraction (UAE) and ultra-high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD) were optimized, using a multivariate experimental design. The optimum conditions of UAE were 15 min extraction time at 35 ℃, with 75 % aqueous methanol as solvent, and a fruit powder-to-solvent ratio (m/v) of 1:15. Among the different drying conditions investigated, freeze-drying was found to be appropriate for analyzing ascorbic acid, polyphenols, and antioxidant potential. The overall ranges, across the fruits and peels, of ascorbic acid, total polyphenols, and antioxidant potentials (EC50) obtained were 8.7 ± 1.4-91.2 ± 2.6 mg/100 g, 253.0 ± 6.3-764.1 ± 25.8 mg GAE/100 g and 2.4 ± 0.1-26.1 ± 2.9 mg/mL, respectively. This indicates that the fruits and peels of the studied plants are advantageous as sources of ascorbic acid and polyphenols.

RSM- and ANN-Based Multifrequency Ultrasonic Extraction of Polyphenol-Rich Sargassum horneri Extracts Exerting Antioxidative Activity via the Regulation of MAPK/Nrf2/HO-1 Machinery

Sargassum horneri (SH) is widely consumed as a healthy seaweed food in the Asia-Pacific region. However, the bioactive components contributing to its biological activity remain unknown. Herein, we optimized multifrequency ultrasonic-assisted extraction conditions to achieve higher antioxidant activity using a response surface methodology and an artificial neural network. High-resolution mass spectrometry (HRMS; negative mode) was used to tentatively identify the secondary metabolites in the optimized SH extract, which were further tested against oxidative stress in RAW264.7 cells. Additionally, the identified compounds were analyzed in silico to determine their binding energies with the Keap1 protein (4L7B). We identified 89 compounds using HRMS, among which 19 metabolites (8 polyphenolics, 2 flavonoids, 2 lignans, 2 terpenes, 2 tannins, 2 sulfolipids, and 1 phospholipid) were putatively reported for the first time in SH. The in vitro results revealed that optimized SH extract inhibited oxidative stress via the Nrf2/MAPKs/HO-1 pathway in a dose-dependent manner. This result was validated by performing in silico simulation, indicating that sargaquinoic acid and glycitein-7-O-glucuronide had the highest binding energies (-9.20 and -9.52 Kcal/mol, respectively) toward Keap1 (4L7B). This study offers a unique approach for the scientific community to identify potential bioactive compounds by optimizing the multivariant extraction processing conditions, which could be used to develop functional and nutraceutical foods.

Biological activities, identification, method development, and validation for analysis of polyphenolic compounds in Nymphaea rubra flowers and leaves by UHPLC-Q-cIM-TOF-MS and UHPLC-TQ-MS

INTRODUCTION

Nymphaea rubra belongs to the Nymphaea family and is regarded as a vegetable used in traditional medicine to cure several ailments. These species are rich in phenolic acid, flavonoids, and hydrolysable tannin.

OBJECTIVE

This study aimed to assess the biological activities of Nymphaea rubra flowers (NRF) and leaves (NRL) by identifying and quantifying their polyphenolic compounds using ultra-performance liquid chromatography coupled to quadrupole cyclic ion mobility time-of-flight mass spectrometry (UHPLC-Q-cIM-TOF-MS) and triple quadrupole mass spectrometry (UHPLC-TQ-MS).

METHODOLOGY

NRF and NRL powder was extracted with methanol and fractionated using hexane, ethylacetate, and water. Antioxidant and α-glucosidase, and tyrosinase enzyme inhibitory activities were evaluated. The polyphenolic components of NRF and NRL were identified and quantified using UHPLC-Q-cIM-TOF-MS and UHPLC-TQ-MS. The method was validated using linearity, precision, accuracy, limit of detection (LOD), and lower limit of quantification (LLOQ).

RESULTS

Bioactive substances and antioxidants were highest in the ethylacetate fraction of flowers and leaves. Principal component analysis showed how solvent and plant components affect N. rubra's bioactivity and bioactive compound extraction. A total of 67 compounds were identified, and among them 21 significant polyphenols were quantified. Each calibration curve had R2 > 0.998. The LOD and LLOQ varied from 0.007 to 0.09 μg/mL and from 0.01 to 0.1 μg/mL, respectively. NRF contained a significant amount of gallic acid (10.1 mg/g), while NRL contained abundant pentagalloylglucose (2.8 mg/g).

CONCLUSION

The developed method is simple, rapid, and selective for the identification and quantification of bioactive molecules. These findings provide a scientific basis for N. rubra's well-documented biological effects.

Unveiling antioxidant capacity of standardized chitosan-tripolyphosphate microcapsules containing polyphenol-rich extract of Portulaca oleraceae

The medicinal plant Portulaca oleraceae has a long history of usage in traditional medicine. Plant extracts have several interesting pharmacological effects but have some drawbacks that can be addressed via capsulation with chitosan. This work set out to do just that tally up the antioxidant effects of a polyphenol-rich P. olerace extract and see how capsulation affected them. The reflux extraction and response surface methodology (RSM) were carried out to optimize the phenolic and flavonoid content of P. oleraceae extract. Additionally, high-resolution mass spectrometry was employed to determine the secondary metabolite present in the extract. The microcapsules of extract-loaded chitosan were prepared using the ionic gelation method and characterized in terms of size, encapsulation efficiency (EE), and morphology of microcapsules. Fourier transform infrared (FTIR) was used to observe the successful production of microcapsules with a principal component analysis (PCA) approach. The antioxidant activity of microcapsules was established using the radical scavenging method. According to RSM, the highest amounts of TPC and TFC were obtained at 72.894 % ethanol, 2.031 h, and 57.384 °C. The compounds were employed from the optimized extract of P. oleraceae including phenolics and flavonoids. The microcapsules were secured with a %EE of 43.56 ± 2.31 %. The characteristics of microcapsules were approved for the obtained product's successful synthesis according to the PCA. The microcapsules have antioxidant activity in a concentration-dependent manner (p < 0.0001). The findings of this study underscored the benefits of employing chitosan as a nanocarrier for extract, offering a promising approach to enhance plant-derived therapies.

Exploring Antioxidant and α-Glucosidase Inhibitory Activities in Mulberry Leaves (Morus alba L.) across Growth Stages: A Comprehensive Metabolomic Analysis with Chemometrics

Metabolic product accumulation exhibited variations among mulberry (Morus alba L.) leaves (MLs) at distinct growth stages, and this assessment was conducted using a combination of analytical techniques including high-performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS). Multivariate analysis was applied to the data, and the findings were correlated with antioxidant activity and α-glucosidase inhibitory effects in vitro. Statistical analyses divided the 27 batches of MLs at different growth stages into three distinct groups. In vitro assays for antioxidant activity and α-glucosidase inhibition revealed that IC50 values were highest at the Y23 stage, which corresponds to the 'Frost Descends' solar term. In summary, the results of this study indicate that MLs at different growth stages throughout the year can be categorized into three primary growth stages using traditional Chinese solar terms as reference points, based on the observed variations in metabolite content.

Tyrosinase inhibitory activity of Sargassum fusiforme and characterisation of bioactive compounds

INTRODUCTION

Sargassum fusiforme (Harvey) Setchell, also known as Tot (in Korean) and Hijiki (in Japanese), is widely consumed in Korea, Japan, and China due to its health promoting properties. However, the bioactive component behind the biological activity is still unknown.

OBJECTIVES

We aimed to optimise the extraction conditions for achieving maximum tyrosinase inhibition activity by using two sophisticated statistical tools, that is, response surface methodology (RSM) and artificial neural network (ANN). Moreover, high-resolution mass spectrometry (HRMS) was used to tentatively identify the components, which are then further studied for molecular docking study using 2Y9X protein.

METHODOLOGY

RSM central composite design was used to conduct extraction using microwave equipment, which was then compared to ANN. Electrospray ionisation tandem mass spectrometry (ESI-MS/MS) was used to tentatively identify bioactive components, which were then docked to the 2Y9X protein using AutoDock Vina and MolDock software.

RESULTS

Maximum tyrosinase inhibition activity of 79.530% was achieved under optimised conditions of time: 3.27 min, temperature: 128.885°C, ethanol concentration: 42.13%, and microwave intensity: 577.84 W. Furthermore, 48 bioactive compounds were tentatively identified in optimised Sargassum fusiforme (OSF) extract, and among them, seven phenolics, five flavonoids, five lignans, six terpenes, and five sulfolipids and phospholipids were putatively reported for the first time in Sargassum fusiforme. Among 48 bioactive components, trifuhalol-A, diphlorethohydroxycarmalol, glycyrrhizin, and arctigenin exhibited higher binding energies for 2Y9X.

CONCLUSION

Taken together, these findings suggest that OSF extract can be used as an effective skin-whitening source on a commercial level and could be used in topical formulations by replacing conventional drugs.

Profiling of Secondary Metabolites of Optimized Ripe Ajwa Date Pulp (Phoenix dactylifera L.) Using Response Surface Methodology and Artificial Neural Network

The date palm (Phoenix dactylifera L.) is a popular edible fruit consumed all over the world and thought to cure several chronic diseases and afflictions. The profiling of the secondary metabolites of optimized ripe Ajwa date pulp (RADP) extracts is scarce. The aim of this study was to optimize the heat extraction (HE) of ripe Ajwa date pulp using response surface methodology (RSM) and artificial neural network (ANN) modeling to increase its polyphenolic content and antioxidant activity. A central composite design was used to optimize HE to achieve the maximum polyphenolic compounds and antioxidant activity of target responses as a function of ethanol concentration, extraction time, and extraction temperature. From RSM estimates, 75.00% ethanol and 3.7 h (extraction time), and 67 °C (extraction temperature) were the optimum conditions for generating total phenolic content (4.49 ± 1.02 mgGAE/g), total flavonoid content (3.31 ± 0.65 mgCAE/g), 2,2-diphenyl-1-picrylhydrazyl (11.10 ± 0.78 % of inhibition), and cupric-reducing antioxidant capacity (1.43 µM ascorbic acid equivalent). The good performance of the ANN was validated using statistical metrics. Seventy-one secondary metabolites, including thirteen new bioactive chemicals (hebitol II, 1,2-di-(syringoyl)-hexoside, naringin dihydrochalcone, erythron-guaiacylglycerol-β-syringaresinol ether hexoside, erythron-1-(4'-O-hexoside-3,5-dimethoxyphenyl)-2-syrngaresinoxyl-propane-1,3-diol, 2-deoxy-2,3-dehydro-N-acetyl-neuraminic acid, linustatin and 1-deoxynojirimycin galactoside), were detected using high-resolution mass spectroscopy. The results revealed a significant concentration of phytoconstituents, making it an excellent contender for the pharmaceutical and food industries.

Antioxidant, Tyrosinase, α-Glucosidase, and Elastase Enzyme Inhibition Activities of Optimized Unripe Ajwa Date Pulp (Phoenix dactylifera) Extracts by Response Surface Methodology

The Ajwa date (Phoenix dactylifera L., Arecaceae family) is a popular edible fruit consumed all over the world. The profiling of the polyphenolic compounds of optimized unripe Ajwa date pulp (URADP) extracts is scarce. The aim of this study was to extract polyphenols from URADP as effectively as possible by using response surface methodology (RSM). A central composite design (CCD) was used to optimize the extraction conditions with respect to ethanol concentration, extraction time, and temperature and to achieve the maximum amount of polyphenolic compounds. High-resolution mass spectrometry was used to identify the URADP's polyphenolic compounds. The DPPH-, ABTS-radical scavenging, α-glucosidase, elastase and tyrosinase enzyme inhibition of optimized extracts of URADP was also evaluated. According to RSM, the highest amounts of TPC (24.25 ± 1.02 mgGAE/g) and TFC (23.98 ± 0.65 mgCAE/g) were obtained at 52% ethanol, 81 min time, and 63 °C. Seventy (70) secondary metabolites, including phenolic, flavonoids, fatty acids, and sugar, were discovered using high-resolution mass spectrometry. In addition, twelve (12) new phytoconstituents were identified for the first time in this plant. Optimized URADP extract showed inhibition of DPPH-radical (IC₅₀ = 87.56 mg/mL), ABTS-radical (IC₅₀ = 172.36 mg/mL), α-glucosidase (IC₅₀ = 221.59 mg/mL), elastase (IC₅₀ = 372.25 mg/mL) and tyrosinase (IC₅₀ = 59.53 mg/mL) enzymes. The results revealed a significant amount of phytoconstituents, making it an excellent contender for the pharmaceutical and food industries.

Optimization of Ultrasonic-Assisted Extraction Conditions for Bioactive Components and Antioxidant Activity of Poria cocos (Schw.) Wolf by an RSM-ANN-GA Hybrid Approach

In this study, a response surface methodology and an artificial neural network coupled with a genetic algorithm (RSM-ANN-GA) was used to predict and estimate the optimized ultrasonic-assisted extraction conditions of Poria cocos. The ingredient yield and antioxidant potential were determined with different independent variables of ethanol concentration (X₁; 25-75%), extraction time (X₂; 30-50 min), and extraction solution volume (mL) (X₃; 20-60 mL). The optimal conditions were predicted by the RSM-ANN-GA model to be 55.53% ethanol concentration for 48.64 min in 60.00 mL solvent for four triterpenoid acids, and 40.49% ethanol concentration for 30.25 min in 20.00 mL solvent for antioxidant activity and total polysaccharide and phenolic contents. The evaluation of the two modeling strategies showed that RSM-ANN-GA provided better predictability and greater accuracy than the response surface methodology for ultrasonic-assisted extraction of P. cocos. These findings provided guidance on efficient extraction of P. cocos and a feasible analysis/modeling optimization process for the extraction of natural products.

Comparison of an Artificial Neural Network and a Response Surface Model during the Extraction of Selenium-Containing Protein from Selenium-Enriched Brassica napus L

In this study, the extraction conditions for selenium-enriched rape protein (SEP) were optimized by applying a response surface methodology (RSM) and artificial neural network (ANN) model, and then, the optimal conditions were obtained using a genetic algorithm (GA). Then, the antioxidant power of the SEP was examined by using the DPPH, ABTS, and CCK-8 (Cell Counting Kit-8), and its anticancer activities were explored by conducting a cell migration test. The results showed that compared with the RSM model, the ANN model was more accurate with a higher determination coefficient and fewer errors when it was applied to optimize the extraction method. The data obtained for SEP using a GA were as follows: the extraction temperature was 59.4 °C, the extraction time was 3.0 h, the alkaline concentration was 0.24 mol/L, the liquid-to-material ratio was 65.2 mL/g, and the predicted content of protein was 58.04 mg/g. The protein was extracted under the conditions obtained by the GA; the real content of protein was 57.69 mg/g, and the protein yield was 61.71%. Finally, as the concentration of the selenium-containing protein increased, it showed increased ability in scavenging free radicals and was influential in inhibiting the proliferation and migration of HepG2 cells.

Metabolite Profiling of Microwave-Assisted Sargassum fusiforme Extracts with Improved Antioxidant Activity Using Hybrid Response Surface Methodology and Artificial Neural Networking-Genetic Algorithm

Sargassum fusiforme (SF) is a popular edible brown macroalga found in Korea, Japan, and China and is known for its health-promoting properties. In this study, we used two sophisticated models to obtain optimized conditions for high antioxidant activity and metabolite profiling using high-resolution mass spectrometry. A four-factor central composite design was used to optimize the microwave-assisted extraction and achieve the maximum antioxidant activities of DPPH (Y1: 28.01 % inhibition), ABTS (Y2: 36.07 % inhibition), TPC (Y3: 43.65 mg GAE/g), and TFC (Y4: 17.67 mg CAE/g), which were achieved under the optimized extraction conditions of X1: 47.67 %, X2: 2.96 min, X3: 139.54 °C, and X4: 600.00 W. Moreover, over 79 secondary metabolites were tentatively identified, of which 12 compounds were reported for the first time in SF, including five phenolic (isopropyl 3-(3,4-dihydroxyphenyl)-2-hydroxypropanoate, 3,4-dihydroxyphenylglycol, scopoletin, caffeic acid 4-sulfate, and cinnamoyl glucose), two flavonoids (4',7-dihydroxyisoflavone and naringenin), three phlorotannins (diphlorethohydroxycarmalol, dibenzodioxin-1,3,6,8-tetraol, and fucophlorethol), and two other compounds (dihydroxyphenylalanine and 5-hydroxybenzofuran-2(3H)-one) being identified for the first time in optimized SF extract. These compounds may also be involved in improving the antioxidant potential of the extract. Therefore, optimized models can provide better estimates and predictive capabilities that would assist in finding new bioactive compounds with improved biological activities that can be further applied at a commercial level.

Untargeted metabolomics study and identification of potential biomarkers in the six sections of the genus Stachys L. (Lamiaceae) using HPLC-MQ-API-MS/MS

INTRODUCTION

The genus Stachys L., belonging to the family Lamiaceae, is one of the largest genera with remarkable medicinal properties. Plants of this genus produce a broad range of secondary metabolites.

OBJECTIVES

Due to the incomplete comprehensive assessment of chemical profiles in Stachys species, we conducted an untargeted metabolomics study and identified potential biomarkers in the six sections of Stachys with chemotaxonomic importance.

MATERIAL AND METHODS

Dried leaves of 17 taxa were utilized for analysis of all the constituents using HPLC-MQ-API-MS. The obtained data were processed and analyzed using multivariate statistical methods, including heatmaps, PLS-DA score plots, functional analysis of metabolic pathways, metabolite set enrichment analysis, and biomarker and network analysis.

RESULTS

Among the 129 metabolites, 111 flavonoids and 18 non-flavonoids were recognized. The most represented flavonoids, including 41 flavones and 20 flavonols, displayed remarkable abundance. In non-flavonoid compounds, a total of six coumarins and six phenolic acids were present at high levels. In terms of approved markers in six sections, 76 chemical compounds, mainly flavonoids, coumarins, quinic acids, and cinnamic acids, were identified as potential biomarkers or chemotaxonomic indicators. Accordingly, the taxonomic complexities of some Stachys species in sections Fragilicaulis, Aucheriana, and Setifolia were properly resolved.

CONCLUSION

An HPLC-MS/MS-based metabolomics approach integrated with multivariate statistical methods was employed to identify (1) valuable markers and analyze metabolic diversity and (2) predict the pharmaceutical properties of Stachys species. The obtained chemical profiles provide a new perspective for investigation of the Stachys genus.

A Hybrid RSM-ANN-GA Approach on Optimization of Ultrasound-Assisted Extraction Conditions for Bioactive Component-Rich Stevia rebaudiana (Bertoni) Leaves Extract

Stevia rebaudiana (Bertoni) leaves consist of dietetically important diterpene steviol glycosides (SGs): stevioside (ST) and rebaudioside-A (Reb-A). ST and Reb-A are key sweetening compounds exhibiting a sweetening potential of 100 to 300 times more intense than that of table sucrose. Ultrasound-assisted extraction (UAE) of SGs was optimized by effective process optimization techniques, such as response surface methodology (RSM) and artificial neural network (ANN) modeling coupled with genetic algorithm (GA) as a function of ethanol concentration (X₁: 0–100%), sonication time (X₂: 10–54 min), and leaf–solvent ratio (X₃: 0.148–0.313 g·mL⁻¹). The maximum target responses were obtained at optimum UAE conditions of 75% (X₁), 43 min (X₂), and 0.28 g·mL⁻¹ (X₃). ANN-GA as a potential alternative indicated superiority to RSM. UAE as a green technology proved superior to conventional maceration extraction (CME) with reduced resource consumption. Moreover, UAE resulted in a higher total extract yield (TEY) and SGs including Reb-A and ST yields as compared to those that were obtained by CME with a marked reduction in resource consumption and CO₂ emission. The findings of the present study evidenced the significance of UAE as an ecofriendly extraction method for extracting SGs, and UAE scale-up could be employed for effectiveness on an industrial scale. These findings evidenced that the UAE is a high-efficiency extraction method with an improved statistical approach.