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

Abstract

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.

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

“…During development, the input layer was not triggered using the transfer function, but the output and hidden layers were activated using pure line (purelin) and tangent sigmoid transfer (tansig) functions in MATLAB. In the experiment, the lowest feasible error between training and testing was evaluated, and the minimum number of epochs to prevent model overfitting was considered; the results were consistent with the findings of previous works [ 32 ]. Training the network with the Levenberg–Marquardt technique yielded the best validation performance for all dependent variables: TPC (Y 1 ), TFC (Y 2 ), DPPH (Y 3 ), and CUPRAC (Y 4 ) ( supplementary data Figures S1–S4 ).…”

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

“…The literature showed that phenolic acids lose certain fragments such as methyl (15 Da), hydroxyl (18 Da), and carboxyl (44 Da) during the collision, which helped in determining the compounds [ 3 ]. Compounds 1 – 14 have been tentatively identified in Sargassum fusiforme based on previously reported fragmentation behavior data ( Table 1 ) [ 15 , 16 , 17 , 18 , 19 ]. Interestingly, 5-(3′,5′-Dihydroxyphenyl)-γ-valerolactone 3- O -glucuronide and coumaroylquinic acid were tentatively acknowledged for the first time in optimized MUAE SH extract.…”

“…However, butanediol lignans generate [M–H-48] ions by breaking at the β-position as a combined loss of CHO and H 2 O from the diol structure [ 24 , 25 ]. Based on the monoisotopic mass [M–H]–, fragmentation behavior in mass spectroscopy, and previous studies, compounds 29 – 33 were recognized as lignan molecules and previously reported in Sargassum sp., Ecklonia sp., and sea vegetables [ 15 , 16 , 17 , 19 ]. Among lignans, compounds 32 and 33 were tentatively reported for the first time in the SH.…”

“…After developing the RSM statistical model, the ANN model was constructed using experimental values to check the accuracy of the two models. The ANN model conditions were developed by following the methodology illustrated by Javed et al [ 16 ]. In short, ANNs comprised hidden layers (tan and purplin) with cascade-forward and feed-forward networks.…”

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

“…Their positive effects on human health have thus far undergone substantial study. The study of polyphenolic compounds is gaining popularity, and the first and most crucial stage in extracting and purifying polyphenolic compounds from plant sources is extraction [ 4 ], given that the extraction of polyphenol is influenced by several factors, including the chemical makeup of the sample, the solvent employed, agitation, extraction time, solute/solvent ratio, and temperature [ 5 , 6 ]. Furthermore, phenolic molecules should not be oxidized because they participate in the enzymatic browning reaction and lose their phenol activity and antioxidant capacity [ 7 ].…”