Comparison of response surface methodology and artificial neural network to enhance the release of reducing sugars from non-edible seed cake by autoclave assisted HCl hydrolysis

Saponins of North Atlantic Sea Cucumber: Chemistry, Health Benefits, and Future Prospectives

Frondosides are the major saponins (triterpene glycosides) of the North Atlantic sea cucumber (Cucumaria frondosa). Frondosides possess amphiphilic characteristics due to the presence of various hydrophilic sugar moieties and hydrophobic genin (sapogenin). Saponins are abundant in holothurians, including in sea cucumbers that are widely distributed across the northern part of the Atlantic Ocean. Over 300 triterpene glycosides have been isolated, identified, and categorized from many species of sea cucumbers. Furthermore, specific saponins from sea cucumbers are broadly classified on the basis of the fron-dosides that have been widely studied. Recent studies have shown that frondoside-containing extracts from C. frondosa exhibit anticancer, anti-obesity, anti-hyperuricemic, anticoagulant, antioxidant, antimicrobial, antiangiogenic, antithrombotic, anti-inflammatory, antitumor, and immunomodulatory activities. However, the exact mechanism(s) of action of biological activities of frondosides is not clearly understood. The function of some frondosides as chemical defense molecules need to be understood. Therefore, this review discusses the different frondosides of C. frondosa and their potential therapeutic activities in relation to the postulated mechanism(s) of action. In addition, recent advances in emerging extraction techniques of frondosides and other saponins and future perspectives are discussed.

Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches

Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quantitative Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia. The most influential descriptors included in the MLR model are RBF, JGI2, nCbH, nRCOOR, nRSR, nPO4 and 'Cl-090', with positive contributions to the dependent variable (negative decimal logarithm of median lethal concentration at 96-h). The Random Forest (RF) regression model was superior amongst the five ML models. We observed higher values of R² (0.812) and lower values of RMSE (0.595) and MAE (0.462) in the cross-validation training set and external validation set. Similarly, this study had a high level of fitness and was internally robust and externally predictive compared to models presented in similar studies. The results suggest that the developed QSTR models are suitable for reliably predicting the aquatic toxicity of structurally diverse pesticides and can be used for screening, prioritising new pesticides, filling data gaps and overcoming the limitations of in vivo and in vitro tests.

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.

Magnetic activated charcoal/Fe2O3 nanocomposite for the adsorptive removal of 2,4-Dichlorophenoxyacetic acid (2,4-D) from aqueous solutions: Synthesis, characterization, optimization, kinetic and isotherm studies

Magnetic activated charcoal/Fe₂O₃ nanocomposite (AC/Fe₂O₃NC) was fabricated using Spondias dulcis leaf extract by a facile method and used for the adsorptive removal of 2,4-Dichlorophenoxyacetic acid (2,4-D) from aqueous solutions for the first time. The nanocomposite was characterized by methods such as FE-SEM, EDS, XRD, FTIR, TGA, VSM, and BET to identify and confirm the surface morphology, elemental composition, crystalline nature, functional groups, thermal stability, magnetic behavior, and surface area respectively. Box-Behnken Design (BBD) - an optimization method, which belongs to the Response surface methodology (RSM) and a modeling tool - Artificial Neural Network (ANN) were employed to design, optimize and predict the relationship between the input parameters (pH, initial concentration of 2,4-D, time and agitation speed) versus the output parameter (adsorption efficiency of 2,4-D). Adsorption efficiency of 98.12% was obtained at optimum conditions (pH: 2.05, initial concentration: 32 ppm, contact time: 100 min, agitation speed: 130 rpm, temperature: 30 °C, and dosage: 0.2 g/L). The predictive ability of the ANN was superior (R² = 0.99) than the quadratic model, given by the RSM (R² = 0.93). The equilibrium data were best-fitted to Langmuir isotherm (R² = 0.9944) and the kinetics obeyed pseudo-second-order model (R² = 0.9993) satisfactorily. Thermodynamic studies revealed the spontaneity and exothermic nature of adsorption. The maximum adsorption capacity, q_m was found to be 255.10 mg/g, substantially larger than the reported values for 2,4-D adsorption by other magnetic nanoadsorbents. Therefore, this nanoadsorbent may be utilized as an excellent alternative for the elimination of 2,4-D from the waterbodies.

Optimization of fermentation parameters for high-activity inulinase production and purification from Rhizopus oryzae by Plackett-Burman and Box-Behnken

The aim of study was to optimize fermentation parameters for inulinase production from Rhizopus oryzae by a statistical approach and to carry out purification of inulinase. Five isolated fungal strains were screen out inulin degradation by using Lugol's iodine solution. R. oryzae exhibited maximum zone of clearance around the colony and was used as an inulinase producer. The effect of carbon sources (inulin, glucose, maltose, sucrose, lactose, onion peel, stevia root, wheat bran) as medium component and fermentation parameters (temperature (25-45 °C), initial pH (4-7), time (3-7 days)) on inulinase production was investigated by Plackett-Burman Design. Wheat Bran (WB), temperature, pH, and incubation time were found to be significant for the production of inulinase (P < 0.05). Furthermore, Box-Behnken Design was employed to optimize fermentation conditions. The maximum experimental results for inulinase activity and specific activity were 348.36 EU/mL and 3621.78 EU/mg, respectively. The results were obtained at 5 days of incubation time, 35 °C of incubation temperature, initial pH of 5.5, and 2% (w/v) WB. Also, inulinase was purified by using ammonium sulfate precipitation, gel filtration chromatography with 2.19-fold and its molecular weight was found as 89.12 kDa. The optimal pH and temperature of the purified enzyme were 4.0 and 60 °C, respectively. Furthermore, the purified enzyme showed excellent stability at 60 °C. In conclusion, the present study offers cost-effective method to produce inulinase from Rhizopus oryzae. Also, it can be suggested that the purified inulinase has strong potential for usage in production of fructose syrup and other industrial areas.

A novel carbon monoxide fed moving bed biofilm reactor for sulfate rich wastewater treatment

In this study, a moving bed biofilm reactor was used for biodesulfuruization using CO as the sole carbon substrate. The effect of hydraulic retention time (HRT), sulfate loading rate and CO loading rate on sulfate and CO removal was examined. At 72, 48 and 24 h HRT, the sulfate removal was 93.5%, 91.9% and 80.1%, respectively. An increase in the sulfate loading reduced the sulfate reduction efficiency, which, however, was improved by increasing the CO flow rate into the MBBR. Best results in terms of sulfate reduction (>80%) were obtained for low inlet sulfate and high CO loading conditions. The CO utilization was very high at 85% throughout the study, except during the last phase of the continuous bioreactor operation it was around 70%. An artificial neural network based model was successfully developed and optimized to accurately predict the bioreactor performance in terms of both sulfate reduction and CO utilization. Overall, this study showed an excellent potential of the moving bed biofilm bioreactor for efficient sulfate reduction even under high loading conditions.

A novel and rapid method for fatty acid preparation by the lipase-catalyzed hydrolysis of Phoenix tree seeds

Fatty acids are the precursors for the production of fuels, oleochemicals and special health care products. In this study, a novel rapid method for fatty acid (FA) preparation by the enzymatic hydrolysis of Phoenix tree seed, an undeveloped woody oil seed, was developed. High-temperature GC with flame ionization detector (FID) and the hydrolysis ratio were used to monitor reaction progress. Enzyme screening and the effect of reaction variables on the hydrolysis of seeds were evaluated and optimized by response surface methodology. The results showed that among the tested enzymes, Lipozyme TLIM showed the greatest amount of hydrolysis of Phoenix tree seed. FAs can be rapidly prepared by one-step hydrolysis of Phoenix tree seeds using Lipozyme TLIM as the biocatalyst. Under the optimized conditions (6% enzyme load, 1:8 mass ratio of seed to water, 47.7 °C and 16 min), the maximum hydrolysis ratio (96.4 ± 1.1%) can be achieved. The effect of reaction variables on the hydrolysis decreased in the following order: reaction time > enzyme load > substrate ratio of seed to water > reaction temperature. This work provides a novel and rapid method for FA preparation from oil seeds.