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A study was conducted at different combinations of ozone concentration (50%–100%) and time (10–30 min) to study the effect of aqueous ozone treatment on chlorpyrifos degradation in cucumber. A process was developed and compared using ANN‐GA and RSM techniques. The optimum conditions obtained using RSM were ozone concentration of 95% and treatment time of 10 min. While the optimum conditions achieved by ANN‐GA were ozone concentration of 81% and treatment time of 14 min. However, chlorpyrifos degradation is more at the condition optimized by ANN compared to the condition optimized by RSM. At the optimum condition, the cucumber showed superior physicochemical and microbial qualities, including pesticide degradation (91.08%), color change (3.58), firmness (11.75 N), moisture content (97.49%), water activity (0.93), pH (6.18), titratable acidity (0.06%), total soluble solids (2.1 °Brix), total plate count (1.5 × 105 CFU/g), and yeast/mold count (3 × 105 CFU/g), respectively. Practical applications: Increased awareness of the health benefits associated with the consumption of fruits and vegetables has led to a rise in their consumption. These items are primarily consumed in their raw or fresh state and often feature in salads. However, the presence of pesticide residues on the surface of raw fruits and vegetables can pose significant health risks to consumers. Ozone treatment can be used for effectively removing pesticide residue in the fresh‐cut fruits and vegetables industry. Also, ozone treatment can reduce the microbial count on the surface of fruits and vegetables which enhances the shelf life of fruits and vegetables. Furthermore, it can prevent the natural ripening process of commodities, thereby increasing the consumer's acceptance. Ozone is considered a safe and eco‐friendly alternative to chemical disinfectants and preservatives. It leaves no chemical residue on the produce and breaks down into oxygen, leaving no harmful byproducts, thereby ensuring safe consumption of the commodity. [ABSTRACT FROM AUTHOR]

A green and efficient ultrasonic-assisted acidic natural deep eutectic solvents for the extraction of anthocyanins from Aronia Melanocarpa was developed. The extraction conditions were optimized by the artificial neural networks and genetic algorithms, and the stability and antioxidant activity of anthocyanins extracts were investigated. A series of acidic natural deep eutectic solvents were prepared using betaine and organic acids as hydrogen bond acceptor and hydrogen bond donor respectively. Their physical and chemical properties such as density, viscosity, pH value were measured. The structure and formation mechanism of natural deep eutectic solvents were studied by infrared spectroscopy. The optimal extraction conditions were optimized by artificial neural network combined with genetic algorithm. The photostability, thermal stability and antioxidant activity of anthocyanins extract were evaluated. The experimental results showed that the natural deep eutectic solvents formed by the hydrogen bond interaction between betaine and lactic acid had the characteristics of density as low as 1.19, viscosity as low as 24.75 mPa·s and pH as 2.89. The optimal extraction conditions were as follows: betaine and lactic acid were used to prepare natural deep eutectic solvents, molar ratio was 1:3, water content was 32%, ultrasonic power was 124 W, ultrasonic time was 24 min and initial ultrasonic temperature was 32 ℃. Under this condition, the anthocyanins extraction yield reached 23.62 mg/g. Compared with traditional solvents and other methods, the method is green, efficient and simple to operate. The results of stability and antioxidant experiments showed that light can accelerate the degradation of anthocyanins in the extract. When the temperature was higher than 50 ℃, the thermal degradation of anthocyanins accelerated with the first-order kinetic degradation constant k>0.0234. When the mass concentration was 200 μg/mL, the DPPH free radical scavenging rate and total antioxidant capacity of anthocyanins extracts were 62.30% and 6.39 mmol/L FeSO4·7H2O equivalent, and at a mass concentration of 30 μg/mL, the ABTS+ free radical scavenging rate of anthocyanins extracts was 85.14%. The antioxidant activity of anthocyanins extracts was significantly (P

BACKGROUND: Haskap berries (Lonicera caerulea L.) are rich in anthocyanins. Cold plasma‐assisted enzyme method (CPEM) is an innovative method for green extraction of anthocyanins, which was optimized by an artificial neural network‐genetic algorithm (ANN‐GA) to maximize the yield. In this study, seven factors were screened using by Plackett–Burman design based on single‐factor experiments and optimized by ANN‐GA. RESULTS: The results showed that the maximum total anthocyanin content (TAC, 42.45 ± 0.25 g cyanidin‐3‐glucoside equivalent (C3G) kg−1 dry weight, DW) was obtained under optimal pretreatment power of 192 W, pretreatment time of 29 s and liquid‐to‐solid ratio of 39 mL g−1. Cleavage and porosity appeared on the surface of the treated sample. The active ingredients and antioxidant capacity of the CPEM extracts were identified by ultra‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry (UPLC‐Q‐TOF‐MS). Compared with other extraction technologies, CPEM presents the advantages of shortening the extraction time, reducing the solvent volume, and significantly increasing active ingredients and antioxidant activity. CONCLUSION: The ANN‐GA has better predictive and higher accuracy than the response surface methodology (RSM) model and is more suitable for optimizing the CPEM by greatly improving the process yield and the utilization of biomass, thus contributing to the sustainability of the agri‐food chain. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

• Optimal route for African star apple seed oil base methanolysis is presented. • Temperature was found to be the most significant parameter. • ANN and RSM showed adequate prediction of the production conditions. • ANN-GA showed superior prediction capability with 1.86% more biodiesel yield than RSM. • ANN-GA gave 9.40% faster,15.88% less energy and 16.82% less catalyst than RSM. Efficient and sustainable commercial production of biodiesel requires process optimization and modeling using artificial intelligence techniques. Artificial neural network-genetic algorithm (ANN-GA) and response surface methodology (RSM) were used to predict the optimal process conditions for biodiesel synthesis from novel chrysophyllium albidum seed oil. Application of Levenberg -Marquardt algorithm and full factorial central composite design-desirability function analysis were carried out for ANN and RSM respectively. The biodiesel obtained at the optimum conditions was characterized using gas chromatography-mass spectrophotometer (GC–MS), Fourier transform infrared (FTIR), AOAC and ASTM D standard methods. The ANN and RSM correlation coefficients were 0.98 and 0.95 respectively. The best RSM fitted model was second order polynomial while the ANN best architecture topology consists of three layers: input layer with four input variables, hidden layer with ten hidden neurons and an output layer with single output variable. Optimization of the process shows that: RSM optimum conditions for biodiesel yield of 85.91wt% validated at 86.85wt% were 65.62°C, 2.14wt% catalyst concentration, 62.04 min and 5.88 methanol: oil molar ratio with overall desirability of 1.00. ANN-GA gave 9.40% faster reaction time and required 15.88% less energy, 3.76% more alcohol and 16.82% less catalyst with 1.86wt% more biodiesel yield than RSM optimized conditions. Ascertained fuel properties, GC–MS and FTIR characterizations confirmed unsaturation and good cold-flow qualities of the biodiesel. Overall results presented ANN-GA as better than RSM and a reliable modeling and optimization technique for viable and sustainable production of biodiesel from chrysophyllum albidum seed. [Display omitted] [ABSTRACT FROM AUTHOR]

• The SUPRAS prepared by 1-octanol and THF was used for the extraction of coumarins. • The composition, viscosity, structure and micromorphology of SUPRAS were studied. • The extraction mechanism of SUPRAS was explored by inverted fluorescence microscope. • The extraction conditions were optimized using OFAT, RSM and ANN-GA. • In present method, less extraction time and extraction solvent were consumed. A simple, fast, and efficient ultrasonic-assisted supramolecular solvent microextraction combined with high performance liquid chromatography method was developed for the determination of coumarins in Cortex fraxini , including esculin, esculetin and fraxetin. In this study, a novel supramolecular solvent was prepared with 1-octanol, tetrahydrofuran and water for the first time, and its composition, viscosity, density, structure, and micromorphology were characterized. The prepared supramolecular solvent exhibited vesicular structures and had the characteristics of low viscosity. Through single-factor experiments, response surface methodology and artificial neural network-genetic algorithm, the optimal extraction conditions were obtained as follows: NaCl concentration of 1 mol mL−1, pH value of 10, solid-liquid ratio of 10:1, vortex time of 30 s, ultrasonic power of 100 W, ultrasonic temperature of 60 °C, ultrasonic time of 15 min, centrifugation speed of 5000 rpm, and centrifugation time of 1 min. The results demonstrated that the artificial neural network model exhibited maximum R -values of 0.98703, 0.97440, 0.99836, and 0.95447 for training, testing, validation, and all dataset, respectively. The minimum mean square errors were 0.75, 10.15, 1.99, and 2.63, respectively. This indicated that the predicted values were almost consistent with the actual values. Under the optimal conditions, the total extraction yields of target analytes reached 2.80 %. The calibration curves for each analyte exhibited excellent linearity within the linear range (r > 0.9993). The limits of detection and quantification ranged from 4.87 to 6.55 ng mL−1 and 16.24 to 21.84 ng mL−1, respectively. The recoveries ranged from 98.71 % to 111.01 % with relative standard deviations of less than 3.6 %. The present method had the advantages of short extraction time (15 min) and less solvent consumption (0.5 mL). The prepared supramolecular solvent was proved to have great potential in extracting coumarins from medicinal plants. [ABSTRACT FROM AUTHOR]

[Display omitted] • Engineered S. elongatus UTEX 2973 has potential to produce isoprene in a sustainable way. • Use of alendronate, an inhibitor of CrtE, significantly improved isoprene production. • ANN-GA model outperforms statistical model for process factors optimization. • A 29.52-fold increase in isoprene yield was seen at ANN-GA optimized conditions with alendronate. • Engineered cyanobacteria could be a suitable platform for renewable production of chemicals. An engineered Synechococcus elongatus UTEX 2973- IspS.IDI is used to enhance isoprene production through geranyl diphosphate synthase (CrtE) inhibition and process parameters (light intensity, NaHCO 3 and growth temperature) optimization approach. A cumulative isoprene production of 1.21 mg/gDCW was achieved with productivity of 12.6 μg/gDCW/h in culture supplemented with 20 μg/mL alendronate. This inhibition strategy improvises the cumulative isoprene production 5.76-fold in presence of alendronate. The maximum cumulative production of isoprene is observed to be 5.22 and 6.20 mg/gDCW (54.4 and 64.6 μg/gDCW/h) at statistical and artificial neural network genetic algorithm (ANN-GA) optimized conditions, respectively. The overall increase of isoprene production is found to be 29.52-fold using an integrated approach of inhibition and ANN-GA optimization in comparison to unoptimized cultures without alendronate. This study reveals that alendronate use as a potential inhibitor and machine learning based optimization is a better approach in comparison to statistical optimization to enhance the isoprene production. [ABSTRACT FROM AUTHOR]

Abstract: This study is based on the application of hydroxyapatite nanoparticles loaded on Zein (Zein/nHAp) as an efficient adsorbent for the removal of Congo red from aqueous solutions. The properties of the adsorbent were characterized using various techniques including FT-IR, XRD, FE-SEM, and BET. The influence of five parameters such as pH, temperature, contact time, initial dye concentration, and adsorbent dosage on the removal percentage of the dye was investigated. The optimum conditions of 5.83, 34.32 °C, 5.20 min, 392.10 ppm, and 0.007 g were achieved for pH, temperature, contact time, initial dye concentration, and adsorbent dosage, respectively. The maximum removal percentage of 99.48% was obtained under the optimum condition that exhibited high adsorption potential of the used adsorbent. Central composite design (CCD) under response surface methodology and artificial neural network-genetic algorithm (ANN-GA) were utilized for optimization of parameters. Comparison of the results of the two models in terms of coefficient of determination (R2) and mean absolute percentage error confirmed the prediction potential of CCD and ANN-GA. Higher ability and accuracy of ANN-GA in prediction was found based on given results. The experimental equilibrium data were studied by Langmuir, Freundlich, Temkin and Dubinin-Radushkevic isotherm models and explored that the data well presented by Langmuir model with maximum adsorption capacity of 416.66 mg/g. The adsorption kinetic was well-fitted by the pseudo-second-order model. The thermodynamics of the adsorption displayed spontaneous and endothermic nature of the process. Regeneration investigation showed that Zein/nHAp can impressively be reused, indicating that the adsorbent was a promising one for the removal of Congo red from aqueous solution.Graphical Abstract: [ABSTRACT FROM AUTHOR]

The commercially available nanoscale zerovalent zinc (nZVZ) was used as an adsorbent for the removal of malachite green (MG) from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration) and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO) and artificial neural network coupled with genetic algorithm (ANN-GA) were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12%) was compatible with the experimental value (90.72%). Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG°, ΔH° and ΔS°) were calculated from the Van't Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG. [ABSTRACT FROM AUTHOR]

We studied the crucial components which elevate the expression of recombinant novel glutaminase free L-asparaginase II (rL-asp II) from Bacillus subtilis WB800N. The Plackett-Burman tool identified sucrose, NHCl, NaH-PO and MgSO as the significant influencing factors (p<0.05). Further investigations showed that artificial neural network-genetic algorithm (ANN-GA) was more effective than central composite design (CCD) in optimizing the influencing factors. The maximum rL-asp II expression was found to be 389.56 IU/ml and 525.98 IU/ml using CCD (R=90.4%) and ANN-GA (R=96.2%), respectively. The validation experiments were carried out in a 3 L batch bioreactor where kinetic modelling of the obtained data was done. The rL-asp II expressed effectively inhibiting the polyacrylamide formation in vitro where no solidification was observed, when 2ml of purified rL-asp II used even after 60 min of incubation. This is the first study to report highest production of rL-asp II in B. subtilis WB800N (525.98 IU/ml) till date by combining statistical designs with consecutive intermittent addition of IPTG in batch reactor. [ABSTRACT FROM AUTHOR]

In this research, a selective, simple and rapid ultrasound assisted dispersive solid-phase micro-microextraction (UA-DSPME) was developed using cobalt ferrite nanoparticles loaded on activated carbon (CoFe 2 O 4 -NPs-AC) as an efficient sorbent for the preconcentration and determination of Maxilon Red GRL (MR-GRL) dye. The properties of sorbent are characterized by X-ray diffraction (XRD), Transmission Electron Microscopy (TEM), Vibrating sample magnetometers (VSM), Fourier transform infrared spectroscopy (FTIR), Particle size distribution (PSD) and Scanning Electron Microscope (SEM) techniques. The factors affecting on the determination of MR-GRL dye were investigated and optimized by central composite design (CCD) and artificial neural networks based on genetic algorithm (ANN-GA). CCD and ANN-GA were used for optimization. Using ANN-GA, optimum conditions were set at 6.70, 1.2 mg, 5.5 min and 174 μL for pH, sorbent amount, sonication time and volume of eluent, respectively. Under the optimized conditions obtained from ANN-GA, the method exhibited a linear dynamic range of 30–3000 ng mL −1 with a detection limit of 5.70 ng mL −1 . The preconcentration factor and enrichment factor were 57.47 and 93.54, respectively with relative standard deviations (RSDs) less than 4.0% (N = 6). The interference effect of some ions and dyes was also investigated and the results show a good selectivity for this method. Finally, the method was successfully applied to the preconcentration and determination of Maxilon Red GRL in water and wastewater samples. [ABSTRACT FROM AUTHOR]

Multi-objective modeling and optimization of ultrasound-assisted bleaching of olive oil were accomplished by a hybrid artificial neural network (ANN) and genetic algorithm (GA) method using an ultrasonic bath with a frequency of 25 kHz. The influence of process parameters including ultrasonic power, bleaching clay dosage, process temperature and time (inputs) on final Lovibond red (Lr) and peroxide value (PV) (outputs) was modeled by a multilayer feed-forward back propagation ANN. The accurate 2-hidden layer model with 20 neurons in each, high R 2 (up to 90%) and minimum mean square error (MSE) obtained by ANN was introduced to GA to find the best operation conditions to achieve minimum Lr and PV. The optimum treatment was found with ultrasonic power of 30%, bleaching clay of 1.2%, bleaching time of 13 min and temperature of 65 °C. Under optimal conditions, Lr and PV were 2.47 and 6.49 (meqO 2 /kg), respectively, that were consistent with predicted values. Optimally ultrasonic bleached olive oil and an industrially bleached olive oil were compared. In most cases, the results indicated no detrimental effects of ultrasound on oil structure. Thus, 40% reduction in bleaching clay dosage, 35% reduction in process temperature and 57% reduction in time over ultrasound-assisted bleaching which not only provided economic and environmental benefits, but also retained edible oil nutritional value in comparison to common bleaching procedure. The results of this study confirm the applicability of ultrasound-assisted bleaching by ultrasonic bath as an economic and feasible approach for bleaching of olive oil to reduce high bleaching costs. [ABSTRACT FROM AUTHOR]

The current study is focused on optimizing the parameters involved in enzymatic processing of red rice bran for maximizing total polyphenol (TP) and free radical scavenging activity (FRSA). The sequential optimization strategies using central composite design (CCD) and artificial neural network (ANN) modeling linked with genetic algorithm (GA) was performed to study the effect of incubation time (60–90 min), xylanase concentration (5–10 mg/g), cellulase concentration (5–10 mg/g) on the response, i.e., total polyphenol and FRSA. The result showed that incubation time has a negative effect on the response, while the square effect of xylanase and cellulase showed positive effect on the response. A maximum TP of 2,761 mg ferulic acid Eq/100 g bran and FRSA of 778.4 mg Catechin Eq/100 g bran was achieved with incubation time (min) = 60.491; xylanase (mg/g) = 5.4633; cellulase (mg/g) = 11.5825. Furthermore, ANN-GA-based optimization showed better predicting capabilities as compared to CCD. [ABSTRACT FROM PUBLISHER]

Wind energy is increasingly being utilized globally, in part as it is a renewable and environmental-friendly energy source. The uncertainty caused by the discontinuous nature of wind energy affects the power grid. Hence, forecasting wind behavior (e.g., wind speed) is important for energy managers and electricity traders, to overcome the risks of unpredictability when using wind energy. Forecasted wind values can be utilized in various applications, such as evaluating wind energy potential, designing wind farms, performing wind turbine predictive control, and wind power planning. In this study, four methods of forecasting using artificial intelligence (artificial neural networks with radial basis function, adaptive neuro-fuzzy inference system, artificial neural network-genetic algorithm hybrid and artificial neural network-particle swarm optimization) are utilized to accurately forecast short-term wind speed data for Tehran, Iran. A large set of wind speed data measured at 1-h intervals, provided by the Iran Renewable Energy Organization (SUNA), is utilized as input in algorithm development. Comparisons of statistical indices for both predicted and actual test data indicate that the artificial neural network-particle swarm optimization hybrid model with the lowest root mean square error and mean square error values outperforms other methods. Nonetheless, all of the models can be used to predict wind speed with reasonable accuracy. [ABSTRACT FROM AUTHOR]

The commercially available nanoscale zerovalent zinc (nZVZ) was used as an adsorbent for the removal of malachite green (MG) from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration) and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO) and artificial neural network coupled with genetic algorithm (ANN-GA) were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12%) was compatible with the experimental value (90.72%). Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG0, ΔH0 and ΔS0) were calculated from the Van’t Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

In present investigation, an attempt has been made for the synthesis of cupric oxide nanoparticles (CuONPs) through a green route by utilizing lemon juice extract as a bioreductant. The synthesized CuONPs were characterized through UV-visible spectroscopy, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM). The CuONPs were utilized for Cr(VI) removal from water through adsorption method in batch mode at different initial Cr(VI) concentration, pH, temperature and CuONPs dosage. The maximum uptake capacity of CuONPs was found to be 16.63 mg of Cr(VI)/g at pH 4.0. Implementation of response surface methodology (RSM) followed by artificial neural network hybridized with genetic algorithm (ANN-GA) approach has resulted maximum Cr(VI) adsorption of 98.8% under the optimized conditions of initial metal concentration 22.5 mg/L, pH 3.81, CuONPs dose 1.28 g/L and temperature 37.1 °C. Under optimum conditions, adsorption isotherm study was conducted, which showed that the fitness of experimental data was well achieved with Langmuir isotherm model illustrating monolayer pattern of adsorption. Thermodynamic study revealed that the process was spontaneous and endothermic in nature, while adsorption kinetics was best explained by pseudo-second order kinetic model. [ABSTRACT FROM AUTHOR]

Abstract: Recycling represents a valid alternative to the disposal of post-consumer materials if it is possible to obtain new materials with good properties. In this work, waste polypropylene (WPP)/waste ground rubber tire (WGRT) powder blends were studied with respect to the effect of bitumen and maleic anhydride-grafted styrene–ethylene–butylene–styrene (SEBS-g-MA) content by using the design of experiments (DOE) approach, whereby the effect of the four polymers content on the final mechanical properties were predicted. Uniform design method was especially adopted for its advantages. Optimization was done using hybrid artificial neural network–genetic algorithm (ANN–GA) technique. The results indicated that the blends show fairly good ductibility provided that it had a relatively higher concentration of bitumen and SEBS-g-MA under the studied condition. A quantitative relationship was presented between the material concentration and the mechanical properties as a set of contour plots, which were confirmed experimentally by testing the optimum ratio. [Copyright &y& Elsevier]

Abstract: Various ultrasonic conditions were employed to prepare polysaccharides from longan fruit pericarp (PLFP) and the Lineweaver–Burk equation was then used to determine the effect of PLFP on inhibition of tyrosinase activity. This result showed that PLFP acted as a non-competitive inhibitor of tyrosinase. The highest slope was observed for ultrasonic extraction, followed by the hot-water extraction, suggesting that the ultrasonic treatment of PLFP increased the inhibition of tyrosinase activity. Furthermore, a multilayer feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of ultrasonic power, time and temperature on the slope value. The trained network gave a regression coefficient (R 2) of 0.98 and a mean squared error (MSE) of 0.58, implying a good agreement between the predicted value and the actual value of the slope, and confirmed a good generalization of the network. Based on the artificial neural network-genetic algorithm, the optimal ultrasonic extraction conditions to obtain the highest slope value (154.1) were determined to be 120W, 12min and 57°C. Application of response surface plots showed the slope value as a function of every two factors under various ultrasonic extraction conditions, which can be observed directly. Therefore, the artificial neural network provided a model with high performance and indicated the non-linear nature of the relation between ultrasonic conditions and slope value. [Copyright &y& Elsevier]