Evaluation of seasonal variation and the optimization of reducing sugar extraction from Ulva prolifera biomass using thermochemical method

Extraction, Characterisation and Evaluation of Antioxidant and Probiotic Growth Potential of Water-Soluble Polysaccharides from Ulva rigida Macroalgae

Ulva rigida green macroalgae contain a variety of polysaccharides. A recent study investigated the optimum concentration and yield of polysaccharide extraction from oven-dried U. rigida biomass using a water-soluble polysaccharide extraction method that adhered to safety standards. This study utilised complete factorial experiments to examine the effects of varying factors on polysaccharide extraction. Results showed a positive correlation between increased levels of all factors and higher polysaccharide extraction yield. This study also found that the main factors and their interaction had a significant impact on the extracted polysaccharides from U. rigida. The highest polysaccharide content and yield were 9.5 mg/mL and 189 mg/g, respectively. Water-soluble polysaccharides demonstrated the presence of reducing sugar (8 mg/g), phenolics (0.69 mg/g) and flavonoids (1.42 mg/g) and exhibited antioxidant properties. Results revealed that freeze-dried polysaccharide powders were primarily composed of the monosaccharide rhamnose. Preliminary results on the effect of these powders on probiotics demonstrated that supplementation of polysaccharides from U. rigida promoted viable Lactobacillus rhamnosus ATCC 53103 growth during cultivation. This discovery has the potential to revolutionise the human food industry and promote the development of functional ingredients for novel and future food products, with numerous applications in the nutraceutical and pharmaceutical industries.

Chemical Composition of Macroalgae Polysaccharides from Galician and Portugal Coasts: Seasonal Variations and Biological Properties

Crude polysaccharides extracted from the Codium sp. and Osmundea sp. macroalgae collected in different seasons (winter, spring and summer) from the Galician and North Portugal coasts were characterised, aiming to support their biomedical application to wound healing. An increase in polysaccharides' sulphate content was registered from winter to summer, and higher values were obtained for Osmundea sp. In turn, the monosaccharide composition constantly changed with a decrease in glucose in Osmundea sp. from spring to winter. For Codium sp., a higher increase was noticed regarding glucose content in the Galician and Portugal coasts. Galactose was the major monosaccharide in all the samples, remaining stable in all seasons and collection sites. These results corroborate the sulphate content and antioxidant activity, since the Osmundea sp.-derived polysaccharides collected in summer exhibited higher scavenging radical ability. The biocompatibility and wound scratch assays revealed that the Osmundea sp. polysaccharide extracted from the Portugal coast in summer possessed more potential for promoting fibroblast migration. This study on seasonal variations of polysaccharides, sulphate content, monosaccharide composition and, consequently, biological properties provides practical guidance for determining the optimal season for algae harvest to standardise preparations of polysaccharides for the biomedical field.

One Step Catalytic Conversion of Polysaccharides in Ulva prolifera to Lactic Acid and Value-Added Chemicals

The production of lactic acid and value-added chemicals (such as hydroxypropanone, glycolic acid, and formic acid) directly from Ulva prolifera via one-step catalytic process was studied. The effect of different amounts of YCl3-derived catalysts on the hydrothermal conversion of carbohydrates in Ulva prolifera was explored, and the reaction conditions were optimized. In this catalytic system, rhamnose could be extracted from Ulva prolifera and converted in situ into lactic acid and hydroxypropanone at 160 °C, while all the glucose, xylose, and rhamnose were fractionated and completely converted to lactic acid at 220 °C or at a higher temperature, via several consecutive and/or parallel catalytic processes. The highest yield of lactic acid obtained was 31.4 wt% under the optimized conditions. The hydrothermal conversion of Ulva prolifera occurred rapidly (within 10 min) and showed promise to valorize Ulva prolifera.

2,4-Dichlorophenoxyacetic acid (2,4-D) adsorptive removal by algal magnetic activated carbon nanocomposite

In the present study, ferric oxide nanoparticles impregnated with activated carbon from Ulva prolifera biomass (UPAC-Fe₂O₃) were prepared and employed to remove 2,4-Dichlorophenoxyacetic acid (2,4-D) by adsorption. The UPAC-Fe₂O₃ nanocomposite was characterized for its structural and functional properties by a variety of techniques. The nanocomposite had a jagged, irregular surface with pores due to uneven scattering of Fe₂O₃ nanoparticles, whereas elemental analysis portrayed the incidence of carbon, oxygen, and iron. XRD analysis established the crystalline and amorphous planes corresponding to the iron oxide and carbon phase respectively. FT-IR analyzed the functional groups that confirmed the integration of Fe₂O₃ nanoparticles onto nanocomposite surfaces. VSM and XPS studies uncovered the superparamagnetic nature and presence of carbon and Fe₂O_3, respectively, in the UPAC-Fe₂O₃ nanocomposite. While the surface area was 292.51 m²/g, the size and volume of the pores were at 2.61 nm and 0.1906 cm³/g, respectively, indicating the mesoporous nature and suitability of the nanocomposites that could be used as adsorbents. Adsorptive removal of 2,4-D by nanocomposite for variations in process parameters like pH, dosage, agitation speed, adsorption time, and 2,4-D concentration was studied. The adsorption of 2,4-D by UPAC-Fe₂O₃ nanocomposite was monolayer chemisorption owing to Langmuir isotherm behavior along with a pseudo-second-order kinetic model. The maximum adsorption capacity and second order rate constant values were 60.61 mg/g and 0.0405 g/mg min respectively. Thermodynamic analysis revealed the spontaneous and feasible endothermic adsorption process. These findings confirm the suitability of the synthesized UPAC-Fe₂O₃ nanocomposite to be used as an adsorbent for toxic herbicide waste streams.

Artificial neural network and statistical modelling of biosorptive removal of hexavalent chromium using macroalgal spent biomass

This study focused on the sustainable removal of chromium in its hexavalent form by adsorption using sugar-extracted spent marine macroalgal biomass - Ulva prolifera. The adsorption of Cr (VI) from aqueous solutions utilizing macroalgal biomass was studied under varying conditions of pH, adsorbent amount, agitation speed, and time to assess and optimize the process variables by using a statistical method - response surface methodology (RSM) to enhance the adsorption efficiency. The maximum adsorption efficiency of 99.11 ± 0.23% was obtained using U. prolifera under the optimal conditions: pH: 5.4, adsorbent dosage: 200 mg, agitation speed: 160 rpm, and time: 75 min. Also, a prediction tool - artificial neural network (ANN) model was developed using the RSM experimental data. Eight neurons in the hidden layer yielded the best network topology (4-8-1) with a high correlation coefficient (R_ANN: 0.99219) and low mean squared error (MSE_ANN: 0.99219). Various performance parameters were compared between RSM and ANN models, which confirmed that the ANN model was better in predicting the response with a high coefficient of determination value (R²_ANN: 0.9844, R²_RSM: 0.9721) and low MSE value (MSE_ANN: 3.7002, MSE_RSM: 6.2179). The adsorption data were analyzed by fitting to various equilibrium isotherms. The maximum adsorption capacity was estimated as 6.41 mg/g. Adsorption data was in line with Freundlich isotherm (R² = 0.97) that confirmed the multilayer adsorption process. Therefore, the spent U. prolifera biomass can credibly be applied as a low-cost adsorbent for Cr (VI) removal, and the adsorption process can be modelled and predicted efficiently using ANN.

Modelling of fermentative bioethanol production from indigenous Ulva prolifera biomass by Saccharomyces cerevisiae NFCCI1248 using an integrated ANN-GA approach

Third generation biomass (marine macroalgae) has been projected as a promising alternative energy resource for bioethanol production due to its high carbon and no lignin composition. However, the major challenge in the technologies of production lies in the fermentative bioconversion process. Therefore, in the present study the predictive ability of an integrated artificial neural network with genetic algorithm (ANN-GA) in the modelling of bioethanol production was investigated for an indigenous marine macroalgal biomass (Ulva prolifera) by a novel yeast strain, Saccharomyces cerevisiae NFCCI1248 using six fermentative parameters, viz., substrate concentration, fermentation time, inoculum size, temperature, agitation speed and pH. The experimental model was developed using one-variable-at-a-time (OVAT) method to analyze the effects of the fermentative parameters on bioethanol production and the obtained regression equation was used as a fitness function for the ANN-GA modelling. The ANN-GA model predicted a maximum bioethanol production at 30 g/L substrate, 48 h fermentation time, 10% (v/v) inoculum, 30 °C temperature, 50 rpm agitation speed and pH 6. The maximum experimental bioethanol yield obtained after applying ANN-GA was 0.242 ± 0.002 g/g RS, which was in close proximity with the predicted value (0.239 g/g RS). Hence, the developed ANN-GA model can be applied as an efficient approach for predicting the fermentative bioethanol production from macroalgal biomass.