Evaluation of the response surface and hybrid artificial neural network-genetic algorithm methodologies to determine extraction yield of Ferulago angulata through supercritical fluid

Carbon dioxide solubility in polyethylene glycol polymer: an accurate intelligent estimation framework

Polyethylene glycol (PEG), a synthetic polymer made up of repeating ethylene oxide units, is widely recognized for its broad utility and adaptable properties. Precise estimation of CO2 solubility in PEG plays a vital role in enhancing processes such as supercritical fluid extraction, carbon capture, and polymer modification, where CO2 serves as a solvent or transport medium. This study focuses on building advanced predictive models using machine-learning approaches, such as random forest (RF), decision tree (DT), adaptive boosting (AdaBoost), k-nearest neighbors (KNN), and ensemble learning (EL) to forecast CO2 solubility in PEG across a wide range of conditions. The data utilized for model development is sourced from previously published literature, and an outlier detection method is applied beforehand to identify any suspicious data points. Additionally, sensitivity analysis is performed to evaluate the relative influence of each input parameter on the output variable. The results proved that DT model is the most performance method for estimating CO2 solubility in PEG since it showed largest R-squared (i.e., 0.801 and 0.991 for test and train, respectively) and lowest error metrics (MSE: 0.0009 and AARE%: 22.58 for test datapoints). In addition, it was found that pressure and PEG molar mass directly affects the solubility in contrast to the temperature variable which has an inverse relationship. The developed DT model can be regarded accurate and robust user-friendly tool for estimating CO2 solubility in PEG without needing experimental workflows which are known to be time-consuming, expensive and tedious.

Preparation of curcumin submicron particles by supercritical antisolvent method with external adjustable annular gap nozzle

The supercritical antisolvent (SAS) method can effectively improve the bioavailability of poorly water-soluble drugs. However, the current supercritical equipment and processes were not fully developed, making industrialization difficult to achieve. Therefore, an externally adjustable annular gap nozzle and its supporting equipment were designed. Curcumin was used as a model drug, ethanol as the solvent, and supercritical carbon dioxide (SC-CO2) as the antisolvent. Building on single-factor experiments, a Box-Behnken Design-Response Surface Methodology (BBD-RSM) was employed to systematically investigate the effects of four process parameters-crystallizer pressure (12-16 MPa), crystallizer temperature (313-323 K), solution concentration (1-2 mg/mL), and CO2/solution flow rate ratio (133-173 g/g)-on the morphology and particle size of curcumin particles. Using scanning electron microscopy (SEM) and dynamic light scattering (DLS) analyses, morphologies and mean diameter ranges were examined. To look into how the SAS process affects TML's chemical and physical characteristics, X-ray diffraction analysis (XRD) and Fourier-transform infrared spectroscopy (FT-IR) were further performed. Experimental results show that, flow ratio of CO2/solution had the greatest effect of particle size, followed by crystallizer temperature and solution concentration, while crystallizer pressure had the least influence. The optimum process conditions are operational conditions were set with a crystallizer pressure of 15 MPa, crystallizer temperature of 320 K, solution concentration of 1.2 mg/mL, and flow ratio of CO2/solution of 134 g/g, resulting in curcumin submicron particles with an average particle size of 808 nm being obtained. This study demonstrated the feasibility of an externally adjustable annular gap nozzle and its associated equipment in the SAS process, showcasing significant potential for reducing particles size and enhancing the bioavailability of poorly water-soluble drugs.

Modeling of CO2 solubility and partial pressure in blended diisopropanolamine and 2-amino-2-methylpropanol solutions via response surface methodology and artificial neural network

In this study, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were developed to estimate the equilibrium solubility and partial pressure of CO2 in blended aqueous solutions of diisopropanolamine (DIPA) and 2-amino-2-methylpropanol (AMP). In this study, several key parameters were analyzed to understand the behavior of the aqueous DIPA/AMP system for CO2 capture. Including DIPA (9-21 wt%), AMP (9-21 wt%), temperature (323.15-358.15 K), pressure (2.140-332 kPa) and CO2 solubility (0.0531-0.8796 mol/mole). The results of the RSM analysis for CO2 solubility indicate that the model demonstrates a strong fit, as evidenced by a Pred-R² of 0.9601, an adjusted R² of 0.9481, and a highly significant F-value of 80.22. The high predicted R² of 0.9601 and 0.9292 values for CO2 solubility and CO2 partial pressure indicate that the predictor variables can explain a substantial amount of the variability in the response variable. The multilayer perceptron (MLP) architecture demonstrated strong correlation capabilities, featuring one hidden layer with 10 and 5 neurons, respectively. Its topology was structured as 4-10-1 for predicting CO2 solubility and 4-5-1 for predicting CO2 partial pressure. The accuracy of the predictions was notably high, with coefficients of determination of 0.99581 for CO2 solubility and 0.99839 for CO2 partial pressure, achieved using the Levenberg-Marquardt algorithm. Upon further analysis, it was concluded that the MLP model exhibited the lowest error rates, with mean square errors of 0.00009085 for CO2 solubility and 0.00316632 for CO2 partial pressure. The findings emphasized that the MLP model not only outperformed the RSM model in accuracy but also demonstrated greater adaptability in handling the intricate variables associated with CO2 solubility and partial pressure in capture technologies.

Production of pazopanib hydrochloride nanoparticles (anti-kidney cancer drug) using a supercritical gas antisolvent (GAS) method

Supercritical fluid-based methods have been receiving increasing popularity in the production of pharmaceutical nanoparticles due to their ability to control the size and distribution of the particles and offer high purity products. The gas anti-solvent method is one of the methods in which a supercritical fluid serves as an anti-solvent. The aim of this work is to develop pazopanib hydrochloride nanoparticles as an anti-cancer agent by the supercritical GAS method. For this purpose, nanoparticles were produced at different temperatures (313, 323 and 333 K), pressures (10, 13 and 16 MPa), and initial solute concentrations (12, 22 and 32 mg ml-1) employing the Box-Behnken design. The results showed that pressure had the most significant effect on the particle size. The average initial particle size of unprocessed pazopanib hydrochloride was about 37.5 ± 8.7 μm. The optimum process parameter values were determined to obtain the smallest particle size using the BBD method. The parameters were optimized at 320 K, 16 MPa, and 12.6 mg ml-1. The average particle size was 311.1 nm, close to the predicted value of 302.3 nm. FTIR analysis indicated that the chemical structure remained unaltered. Furthermore, DSC and XRD results confirmed the reduction in particle size.

A review of the ethnomedicinal, phytochemical, and pharmacological properties of the Ferulago genus based on Structure-Activity Relationship (SAR) of coumarins

BACKGROUND

The Ferluago W.D.J. Koch genus includes 48 accepted perennial herbs that are distributed in the Mediterranean region, Southeast Europe, Central and Middle East of Asia. These plants are widely used in folk and conventional medicine due to their biological benefits such as anti-microbial, anti-inflammatory, anti-cancer, and immunomodulatory properties. Conducting a comprehensive review based on the structure activity relationships (SARs) of the coumarins, which has not been previously documented, can lead to a better insight into the genus Ferulago and its beneficial therapeutic activities.

METHODS

This review covers literature from 1969 to 2023, were collected from various scientific electronic databases to review phytochemical, pharmacological, and ethnopharmacological data of Ferulago species, as well as latest information on the SAR of reported coumarins from this genus.

RESULTS

Phytochemical studies showed that the biological actions of this genus are mediated by the reported specialized metabolites, such as coumarins and flavonoids. Simple coumarins, prenylated coumarins, furanocoumarins, and pyranocoumarins are the largest subclasses of coumarins found in diverse Ferulago species, which have discussed the biological effects of them with a focus on the Structure-Activity Relationship (SAR). For example, prenylated coumarins have shown potential leishmanicidal and anti-neuropsychiatric effects when substituted with a prenyl group at the 7-hydroxy, as well as the C6 and C8 positions in their scaffold. Similarly, furanocoumarins exhibit varied biological activities such as anti-inflammatory, anti-proliferative, and anti-convulsant effects. Modifying substitutions at the C5 and C6 positions in furanocoumarins can enhance these activities.

CONCLUSION

This study conducted a comprehensive review of all available information on the phytochemical and pharmacological characteristics of Ferulago species. Given the high occurrence of coumarins in this genus, which exhibit potential anti-Alzheimer and anti-microbial properties, it presents promising new therapeutic avenues for addressing these common issues. Further investigation is needed to understand the molecular-level mechanisms of action and to explore their clinical applications.

Determination of Regorafenib monohydrate (colorectal anticancer drug) solubility in supercritical CO2: Experimental and thermodynamic modeling

In this study, the solubilities of Regorafenib monohydrate (REG), a widely used as a colorectal anticancer drug, in supercritical carbon dioxide (ScCO2) were measured under various pressures and temperature conditions, for the first time. The minimum value of REG in mole fraction was determined to be 3.06×10-7, while the maximum value was found to be 6.44×10-6 at 338 K and 27 MPa. The experimental data for REG were correlated through the utilization of two types of models: (1) a set of 25 existing empirical and semi-empirical models that incorporated 3-8 parameters according to functional dependencies, (2) a model that relied on solid-liquid equilibrium (SLE) and the newly improved association models. All of the evaluated models were capable of generating suitable fits to the solubility data of REG, however, the average absolute relative deviation (AARD) of Gordillo et al. model (AARD=13.2%) and Reddy et al. model (AARD=13.5%) indicated their superiority based on AARD%. Furthermore, solvation and sublimation enthalpies of REG drug were estimated for the first time.

Machine learning approaches for the treatment of textile wastewater using sugarcane bagasse (Saccharum officinarum) biochar

Most dyes present in wastewater from the textile industry exhibit toxicity and are resistant to biodegradation. Hence, the imperative arises for the environmentally significant elimination of textile dye by utilising agricultural waste. The achievement of this objective can be facilitated through the utilisation of the adsorption mechanism, which entails the passive absorption of pollutants using biochar. In this study, we compare the efficacy of the response surface methodology (RSM), the artificial neural network (ANN), the k-nearest neighbour (kNN), and adaptive neuro-fuzzy inference system (ANFIS) in removing crystal violet (CV) from wastewater. The characterisation of biochar is carried out by scanning electron microscope (SEM) and Fourier transform infrared (FTIR). The impacts of the solution pH, adsorbent dosage, initial dye concentration, and temperature were investigated using a variety of models (RSM, ANN, kNN, and ANFIS). The statistical analysis of errors was conducted, resulting in a maximum removal effectiveness of 97.46% under optimised settings. These conditions included an adsorbent dose of 0.4 mg, a pH of 5, a CV concentration of 40.1 mg/L, and a temperature of 20 °C. The ANN, RSM, kNN, and ANFIS models all achieved R2 0.9685, 0.9618, 0.9421, and 0.8823, respectively. Even though all models showed accuracy in predicting the removal of CV dye, it was observed that the ANN model exhibited greater accuracy compared to the other models.

Optimal pretreatment of plantain peel waste valorization for biogas production: Insights into neural network modeling and kinetic analysis

This work proposed a model for the substrate treatment stage of biogas production process in an anaerobic digestion system. Adaptive neuro-fuzzy inference system (ANFIS), response surface method (RSM), and artificial neural network (ANN) were comparatively used in the simulation and modeling of the treatment process for improved biogas yield. Waste plantain peels were pretreated and used as substrate. FTIR and SEM results revealed that the pretreatment improved the substrate's desirable qualities. The amount of biogas yield was controlled by time, NaOH concentration, and temperature of the substrate pretreatment. Optimum pretreatment conditions obtained were a temperature of 102.7 °C, time of 31.7 min and NaOH concentration of 0.125 N. RSM, ANN, and ANFIS modeling techniques were proficient in simulating the biogas production, as evidenced by high R2values of 0.9281, 0.9850, and 0.9852, respectively. Furthermore, the values of the calculated error terms such as RMSE (RSM = 0.04799, ANN = 0.00969, and ANFIS = 0.00587) and HYBRID (RSM = 18.556, ANN = 0.803, and ANFIS = 0.0447) were low, indicating a satisfactory correlation between experimental and predicted values. Scrubbing of the biogas with caustic soda and activated charcoal increased the methane content to 94 %. The kinetics of the cumulative biogas yield were best fit with the Logistics and Modified Logistics models. The low C/N ratio in addition to the presence of potassium, nitrogen, and phosphorus suggested that the spent plantain peel slurry can be utilized as an agricultural fertilizer in crop production. The observations of this study therefore recommends the pre-treatment of biodigestion substrates as a key means to enhance beneficiation of methane production.

Extraction and Emulsification of Carotenoids from Carrot Pomaces Using Oleic Acid

This study aimed to use oleic acid-based ultrasonic-assisted extraction (UAE) to recover carotenoids from carrot pomace and emulsify the enriched-carotenoid oleic acid using spontaneous and ultrasonic-assisted emulsification. The extraction performance of oleic acid was compared with traditional organic solvents, including hexane, acetone, and ethyl acetate. The one-factor experiments were employed to examine the impact of UAE conditions, including liquid-to-solid ratios, temperature, ultrasonic power, and time, on the extraction yield of carotenoids and to find the conditional ranges for the optimization process. The response surface methodology was employed to optimize the UAE process. The second-order extraction kinetic model was used to find the mechanism of oleic acid-based UAE. After that, the enriched-carotenoid oleic acid obtained at the optimal conditions of UAE was used to fabricate nanoemulsions using spontaneous emulsification (SE), ultrasonic-assisted emulsification (UE), and SE-UE. The effect of SE and UE conditions on the turbidity of nanoemulsion was determined. Then, the physiochemical attributes of the nanoemulsion from SE, UE, and spontaneous ultrasonic-assisted emulsification (SE-UE) were determined using the dynamic light scattering method. The extraction yield of carotenoids from carrot pomace by using sonication was the highest. The adjusted optimal conditions were 39 mL/g of LSR, 50 °C, 12.5 min, and 350 W of ultrasonic power. Under optimal conditions, the carotenoid content attained was approximately 163.43 ± 1.83 μg/g, with the anticipated value (166 μg/g). The particle sizes of nanoemulsion fabricated at the proper conditions of SE, UE, and SE-UE were 31.2 ± 0.83, 33.8 ± 0.52, and 109.7 ± 8.24 nm, respectively. The results showed that SE and UE are suitable methods for fabricating nanoemulsions. The research provided a green approach for extracting and emulsifying carotenoids from carrot pomace.

Characterization of phyto-components with antimicrobial traits in supercritical carbon dioxide and soxhlet Prosopis juliflora leaves extract using GC-MS

This study aimed to screen the bioactive compounds from Prosopis juliflora leaf supercritical fluid extract and to assess its antimicrobial properties. Supercritical carbon dioxide and Soxhlet methods were used for extraction. The extract was subjected to Gas Chromatography-Mass Spectrometer (GC-MS) and Fourier Transform Infrared for the characterization of the phyto-components. When compared to soxhlet extraction, more components (35) were eluted by supercritical fluid extraction (SFE), according to GC-MS screening. Rhizoctonia bataticola, Alternaria alternata, and Colletotrichum gloeosporioides were all successfully inhibited by P. juliflora leaf SFE extract, which demonstrated strong antifungal properties with mycelium percent inhibition of 94.07%, 93.15%, and 92.43%, respectively, compared to extract from Soxhlet, which registered 55.31%, 75.63% and 45.13% mycelium inhibition respectively. Also, SFE P. juliflora extracts registered higher zone of inhibition 13.90 mm, 14.47 mm and 14.53 mm against all three test food-borne bacterial pathogens viz Escherichia coli, Salmonella enterica and Staphylococcus aureus respectively. Results obtained from GC-MS screening revealed that SFE is more efficient than soxhlet extraction in recovering the phyto-components. P. juliflora may provide antimicrobial agents, a novel natural inhibitory metabolite.

Solubility of palbociclib in supercritical carbon dioxide from experimental measurement and Peng-Robinson equation of state

Palbociclib is a poorly water-soluble medicine which acts against metastatic breast cancer cells. Among various techniques to improve the solubility of this medicine, applying supercritical technologies to produce micro- and nano-sized particles is a possible option. For this purpose, extraction of solubility data is required. In this research, the solubility of palbociclib in supercritical carbon dioxide (ScCO₂) at different equilibrium conditions was measured at temperatures between 308 and 338 K and pressures within 12-27 MPa, for the first time. The minimum and maximum solubility data were found to be 8.1 × 10^-7 (at 338 K and 12 MPa) and 2.03 × 10^-5 (at 338 K and 27 MPa), respectively. Thereafter, two sets of models, including ten semi-empirical equations and three Peng-Robinson (PR) based integrated models were used to correlate the experimental solubility data. Bian's model and PR equation of state using van der Waals mixing rules (PR + vdW) showed better accuracy among the examined semi-empirical and integrated models, respectively. Furthermore, the self-consistency of the obtained data was confirmed using two distinct semi-empirical models. At last, the total and vaporization enthalpies of palbociclib solubility in ScCO₂ were calculated from correlation results of semi-empirical equations and estimated to be 40.41 and 52.67 kJ/mol, respectively.

Comparative study on the extraction efficiency, characterization, and bioactivities of Bletilla striata polysaccharides using response surface methodology (RSM) and genetic algorithm-artificial neural network (GA-ANN)

This research established the optimal conditions for alkali-assisted extraction (AAE) of bioactive polysaccharides from Bletilla striata integrated with response surface methodology (RSM) and the genetic algorithm-artificial neural networks (GA-ANN). In comparison with RSM, the ANN model showed a relatively higher determination coefficient in the global output values (RSM: ANN = 0.9270: 0.9742) performing more satisfactorily in the validation. Under the optimum conditions (52 °C; 167 min, and 0.01 mol/L NaOH), the extraction yields, IC₅₀ of ABTS, and FRAP value were 29.53 ± 0.97 %, 3.41 mg/mL, and 39.11 μmol Fe²⁺/g, respectively. The results indicated that BSPs-A was mainly composed of glucose and mannose with small amounts of arabinose, galactose, and galacturonic acid, while possessed a molecular weight of about 305.94 kDa (Mw). The structural characterization of BSPs-A was initially characterized by FT-IR, SEM, and Congo red tests, which indicated that BSPs-A possessed a triple helix conformation of typical Bletilla striata polysaccharides. In addition, BSPs-A exhibited excellent antioxidant activity, which was further confirmed by a series of in vitro antioxidant activity assays including DPPH, ABTS, FRAP, and ORAC. After incubation in the BSA-glucose system for 15 days, BSPs-A showed inhibition of the advanced glycation end products (AGEs) formation for the first time.

Cellulose-based hydrogel for adsorptive removal of cationic dyes from aqueous solution: isotherms and kinetics

The development of economic and recyclable adsorbents for removing pollutants from contaminated water is gaining increasing attention. Agro residue or nature-based material sourced absorbents could revolutionize the future of wastewater treatment. Hence in this study, nanocellulose was synthesized from coconut husk fiber and immobilized onto chitosan to form hydrogel beads. The BET surface area and zeta potential of the adsorbent nanocrystalline cellulose-chitosan hydrogel (NCC-CH) bead was 25.77 m2 g-1 and +50.6 mV, respectively. The functional group analysis also confirmed that the adsorbent had functional groups appropriate for the adsorption of textile dyes. The adsorption performance of NCC-CH and also the influence of initial dye concentration, adsorbent dose, pH, and contact time was evaluated by batch adsorption studies with crystal violet (CV) and methylene blue (MB) dyes. The most favorable operational conditions achieved through I-optimal design in response surface methodology were 0.5 g NCC-CH, 1 h, 9 pH, and 60 mg L-1 for CV removal (94.75%) and 0.13 g NCC-CH, 1 h, 9 pH, and 30 mg L-1 for MB removal (95.88%). The polynomial quadratic model fits the experimental data with an R 2 value of 0.99 and 0.98 for CV and MB removal, respectively. The optimum depiction of the isotherm data was obtained using the Freundlich model for MB adsorption and Freundlich and Langmuir model for CV adsorption. The Dubinin-Radushkevich (D-R) isotherm was also a good fit to the adsorption of CV and MB dye, suggesting the physisorption due to its free energy of adsorption < 8 kJ mol-1. The kinetics were effectively explained by a pseudo-second order model for both the dyes suggesting that chemical mechanisms influenced the adsorption of CV and MB dyes onto NCC-CH. The intraparticle diffusion model best suited the MB adsorption with three stages rather than the CV with a single step process. Also, the removal efficiency of adsorbent was retained at above 60% even after seven adsorption-desorption cycles indicating the effectiveness of the NCC-CH hydrogel beads for the removal of textile dyes.

Separation of hydrocarbons from activated carbon as a porous substance in a glycol regeneration process using supercritical carbon dioxide

Activated carbons are used in industrial applications; their cost is a major barrier to their more widespread application. Regeneration of activated carbons is indispensable to minimize operational costs and product wastage. Supercritical carbon dioxide (SC-CO₂) as green technology was used to regenerate activated carbons. In this work, response surface methodology was employed to optimize the supercritical regeneration process and to evaluate the effect of the operational parameters including pressure (100-300 bar), temperature (313-333 K), flow rate (2-6 g/min), and dynamic time (30-150 min) on the regeneration yield. The maximum regeneration yield (93.71%) was achieved at 285 bar, 333 K, 4 g/min, and 147 min. Mathematical modeling was done using two one-parameter kinetics models, which agree well with the experimental data. The fitting parameter of the model was obtained by using a differential evolution algorithm. The chemical composition of the substances extracted from the activated carbon was identified by gas chromatography. The results showed that the regeneration of activated carbon by SC-CO₂ can be an alternative method to conventional methods.

Hydro-Distilled and Supercritical Fluid Extraction of Eucalyptus camaldulensis Essential Oil: Characterization of Bioactives Along With Antioxidant, Antimicrobial and Antibiofilm Activities

Present research work evaluates variation in volatile chemicals profile and biological activities of essential oil (EO) obtained from the leaves of eucalyptus (Eucalyptus camaldulensis Dehnh.) using hydro-distillation (HD) and supercritical fluid extraction (SFE). The yield (1.32%) of volatile oil by HD was higher than the yield (.52%) of the SFE method (P < .05). The results of physical factors like density, color, refractive index, and solubility of the EOs produced by both the methods showed insignificant variations. Gas chromatography - mass spectrometry (GC-MS) compositional analysis showed that eucalyptol (31.10% and 30.43%) and α-pinene (11.02% and 10.35%) were the main constituents detected in SFE and HD extracted Eucalyptus camaldulensis EO, respectively. Antioxidant activity-related parameters, such as reducing ability and DPPH free radical scavenging capability exhibited by EO obtained via SFE were noted to be better than hydro-distilled EO. Supercritical fluid extracted and hydro-distilled essential oils demonstrated a considerable but variable antimicrobial potential against selected bacterial and fungal strains. Interestingly, oil extracted by SFE showed relatively higher hemolytic activity and biofilm inhibition potential. The variation in biological activities of tested EOs can be linked to the difference in the volatile bioactives composition due to different isolation techniques. In conclusion, the EO obtained from Eucalyptus leaves by the SFE method can be explored as a potential antioxidant and antimicrobial agent in the functional food and nutra-pharmaceutical sector.

Process Optimization for Supercritical Carbon Dioxide Extraction of Origanum vulgare L. Essential Oil Based on the Yield, Carvacrol, and Thymol Contents

Background

Origanum vulgare L. essential oil (OEO) is widely known for its good biological activity, but different extraction methods with significant implications on the yield of OEO and the content of the thymol and carvacrol. As an efficient method for extracting essential oils (EO), the supercritical carbon dioxide extraction (SC-CO2) can improve the yield of EOs while protecting their main active components from loss.

Objective

In this study, the process optimization of SC-CO2 of OEO was carried out. The effects of extraction pressure, temperature, time, and modifier concentration on the composite score of OEO extraction process were investigated.

Method

Response surface analysis was performed using a Box-Behnken design with three levels and four independent variables. Steam distillation (SD) and lipophilic solvents (n-hexane) extraction (LSE) were compared with SC-CO2 for OEO yields. OEOs extracted by the three methods were qualitatively and semi-quantitatively analyzed by gas chromatography quadrupole-time-of-flight mass spectrometry and gas chromatography-flame ionization detector.

Results

The results showed that extraction pressure was the most significant factor affecting the OEO yield, thymol, and carvacrol content. In the optimal conditions (pressure: 217 bar, temperature: 54°C, time: 2 h, modifier concentration: 14%), the yield of OEO reached up to 1.136%, and the contents of thymol and carvacrol reached 53.172  and 41.785 mg/g, respectively.

Conclusions

SC-CO2 was the best extraction method compared to the other two methods. Under the optimal conditions, yield and the content of main components can be effectively improved. It can provide a theoretical basis for the industrial extraction of OEO.

Highlights

Taking the comprehensive score as the index, the interaction between the four independent variables in the supercritical fluid extraction process was evaluated by the response surface method. The effects of extraction parameters on the yield of EOs and the contents of thymol and carvacrol were comprehensively investigated.

Soft computing-based models and decolorization of Reactive Yellow 81 using Ulva Prolifera biochar

The present research explores the application of optimization tools namely Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the decolorization of Reactive Yellow 81 (RY81) from an aqueous solution. The characterization of the biochar was carried out using FTIR, elemental analysis, proximate analysis, BET analysis and Thermogravimetric analysis. Five independent variables namely solution pH, biochar dose, contact time, initial dye concentration and temperature were analyzed using RSM, ANN and ANFIS models. The maximum removal efficiency of 86.4% was obtained and the statistical error analysis was calculated. The correlation coefficient of 0.9665, 0.9998 and 0.9999 was obtained for RSM, ANN and ANFIS models, respectively. Adsorption Isotherm models and kinetic models were used to understand the adsorption mechanism. Maximum monolayer adsorption of 225 mg g^-1 was predicted by Hill isotherm model. A partition coefficient of 4.09 L g^-1 was obtained at an initial dye concentration of 250 mg L^-1. It was revealed from the thermodynamic studies that reactions are endothermic and spontaneous. Further, to check the potential of the biochar, regeneration cycle was studied. The desorption efficiency of 99.5% was achieved at an S/L ratio of 3, regeneration cycles of 2, and sodium hydroxide was found as the best elutant for the desorption.

The ethnobotany, phytochemistry and biological properties of genus Ferulago - A review

ETHNOPHARMACOLOGICAL RELEVANCE

The genus Ferulago, belonging to the Apiaceae family, is found mainly in the Mediterranean area, Southwest and Middle Asia, the Caucasus and North Africa. Since ancient times, species of this genus have been largely employed in traditional medicine for their biological properties such as antimicrobial, anti-inflammatory, antispasmodic, insecticidal, and anti-malaria, cholinesterase inhibition effects, etc. AIMS: The scope of this paper is to present a comprehensive respect review of this interesting genus including traditional uses, chemical composition of volatile and non-volatile metabolites, and in vitro and in vivo biological properties either utilizing the crude extracts or essential oils, or the single isolated compounds. Furthermore, critical considerations of the published data have been highlighted by comparing them with the results obtained from species of other genus belonging to the Apiaceae family.

MATERIALS AND METHODS

The available information on these genera was collected from scientific databases and cover from 1967 up to 2020. The following electronic databases were used: PubMed, SciFinder, Science Direct, Scopus, Web of Science and Google Scholar. The search terms used for this review included Ferulago, all the botanical names of the species, both accepted names or synonyms, essential oils, volatile components, traditional uses, activity, pharmacology, and toxicity. No limitations were set for languages. A total of 230 articles were included in the present review.

RESULTS

Researches performed on either crude extracts, solvent fractions or isolated pure compounds from species of genus Ferulago showed several biological properties such as antibacterial, antifungal, antioxidant, antidiabetic, enzymatic, cytotoxic, anticancer, hepatoprotective, nephroprotective, antileishmanial, antimalarial, anticoagulant, anti-inflammatory, insecticidal, etc. Activities. Phytochemical investigations of Ferulago species have revealed that coumarins are the main constituents of the genera. A large number of flavonoids, terpenoids and other metabolites were also identified. Furthermore, a complete review on the essential oil composition of all the taxa studied so far has been also included.

CONCLUSION

In the present study, we have provided scientific information and research developments on traditional uses, phytochemical profiles, biological activities and industrial practices on the Ferulago genus. The commercial use and the applications in agri-food sector of some of these species have been also considered. In fact, the various extracts and essential oils have been used as antioxidants and/or as antimicrobial agents, for the stabilization of sunflower and soybean oil, for food packagings, as antioxidants of mayonnaise and yogurt during their storage and, also, as economically valid source for obtaining single compounds, more expensive at a synthetic level.

Inulin from Pachyrhizus erosus root and its production intensification using evolutionary algorithm approach and response surface methodology

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The ultrasound assisted extraction of inulin from Pachyrhizus erosus roots.

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Compared with microwave assisted and conventional extraction technique.

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Optimization the extraction by RSM and genetic algorithm.

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Purity profiling and degree of polymerization of extracted inulin.

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Significant prebiotic activity recorded using Lactobacillus fermentum.

Production of inulin from yam bean tubers by ultrasonic assisted extraction (UAE) was optimized by using response surface methodology (RSM) and genetic algorithms (GA). Yield of inulin was obtained between 11.97%–12.15% for UAE and 11.21%–11.38% for microwave assisted extraction (MAE) using both the methodologies, significantly higher than conventional method (9.9 %) using optimized conditions. Under such optimized condition, SEM image of root tissues before and extraction showed disruption and microfractures over surface. UAE provided a shade better purity of extracted inulin than other two techniques. Degree of polymerization in inulin was also recorded to be better, might be due lesser degradation during extraction. Significant prebiotic activity was recorded while evaluation using Lactobacillus fermentum and it was 36 % more than glucose treatment. Energy density by UAE was few fold lesser than MAE. Carbon emission was far more less in both these methods than the conventional one.

Comprehensive study on applications of artificial neural network in food process modeling

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed.HIGHLIGHTS1. This paper discusses application of ANN in different emerging trends in food process.2. Application of ANN to develop non-linear multivariate modeling is illustrated.3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy.4. ANN resulted in fast response making it suitable for application in real time systems.

Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

The effect of pressure on the characteristics of supercritical carbon dioxide extracts from Calamintha nepeta subsp. nepeta

Chemists and industrialists are continuously attempting to develop greener and more environmentally benign chemical processes to extract essential oils and bioactive metabolites of high purity, finding various applications in cosmetics, detergents, nutraceuticals and pharmaceuticals. An increase preferenced for natural products over synthetic ones has made supercritical fluid technology a primary alternative for the generation of high-value bioactive ingredients. This effective technique requires only moderate temperatures, eliminates clean-up steps and avoids the use of harmful organic solvents. In this context, our study was focused on the chemical analysis of Calamintha nepeta subsp. nepeta aromatic extracts obtained with supercritical carbon dioxide. The effect of different operating conditions on the capacity of the lipophilic solvent to extract the targeted volatile components was also studied. The process was carried out at a fairly low constant temperature of 40°C, and with varying the pressure from 90 to 300 bar. The chemical composition of the extracts was analyzed by gas chromatography-mass spectroscopy. The results showed that the composition pattern, the concentrations of individual components and the quality of the extractable analytes were affected by pressure increase. The extraction yields varied from 0.73 to 1.21 wt% at 90 and 300 bar, respectively.

High level extracellular production of recombinant human interferon alpha 2b in glycoengineered Pichia pastoris: culture medium optimization, high cell density cultivation and biological characterization

AIMS

The present study was aimed at design of experiments (DoE)- and artificial intelligence-based culture medium optimization for high level extracellular production of a novel recombinant human interferon alpha 2b (huIFNα2b) in glycoengineered Pichia pastoris and its characterization.

METHODS AND RESULTS

The artificial neural network-genetic algorithm model exhibited improved huIFNα2b production and better predictability compared to response surface methodology. The optimized medium exhibited a fivefold increase in huIFNα2b titre compared to the complex medium. A maximum titre of huIFNα2b (436 mg l^-1 ) was achieved using the optimized medium in the bioreactor. Real-time capacitance data from dielectric spectroscopy were utilized to model the growth kinetics with unstructured models. Biological characterization by antiproliferative assay proved that the purified recombinant huIFNα2b was biologically active, exhibiting growth inhibition on breast cancer cell line.

CONCLUSIONS

Culture medium optimization resulted in enhanced production of huIFNα2b in glycoengineered P. pastoris at both shake flask and bioreactor level. The purified huIFNα2b was found to be N-glycosylated and biologically active.

SIGNIFICANCE AND IMPACT OF THE STUDY

DoE-based medium optimization strategy significantly improved huIFNα2b production. The antiproliferative activity of huIFNα2b substantiates its potential scope for application in cancer therapy.

Deactivation of Chlorinated Pt/Al2O3 Isomerization Catalyst Using Water Containing Feed

Light naphtha isomerization is a significant process in a crude oil refinery which is responsible for upgrading low-octane light naphtha to the high-octane and low-aromatic content gasoline. In this work, deactivation of an industrial chlorinated Pt/Al2O3 isomerization catalyst was studied in a laboratory scale plant. Experiments were carried out under temperatures in the range of 120–180 °C, liquid hourly space velocities (LHSV) of 0.7–2 h−1 and hydrogen to hydrocarbon molar ratios (H2/Oil) of 0.7–1.5. Moreover, the total water content of the combined feed, i. e. n-hexane and hydrogen, was 70 ppmwt. During 75 h time on stream (TOS), 42 data sets were collected and applied for training, testing and validating a hybrid-artificial neural network model (hybrid-ANN or HANN) to estimate the activity of the catalyst. Results showed that the activity decreased to 0.56 at the end of the operation mostly due to water poisoning. Furthermore, using the estimated activity, HANN could simulate the conversion and selectivity of the isomerization process with the absolute average deviations (AAD%) of 0.97 % and 0.0766 % and the mean squared errors (MSE) of 0.311 and 0.0156, respectively.

Mathematical modelling for extraction of oil from Dracocephalum kotschyi seeds in supercritical carbon dioxide

Extraction of oil from Dracocephalum kotschyi Boiss seeds using supercritical carbon dioxide was designed using central composite design to evaluate the effect of various operating parameters including pressure, temperature, particle size and extraction time on the oil yield. Maximum extraction yield predicted from response surface method was 71.53% under the process conditions with pressure of 220 bar, temperature of 35 °C, particle diameter of 0.61 mm and extraction time of 130 min. Furthermore, broken and intact cells model was utilised to consider mass transfer kinetics of extracted natural materials. The results revealed that the model had a good agreement with the experimental data. The oil samples obtained via supercritical and solvent extraction methods were analysed by gas chromatography. The most abundant acid was linolenic acid. The results analysis showed that there was no significant difference between the fatty acid contents of the oils obtained by the supercritical and solvent extraction techniques.