Optimization of extraction of linarin from Flos chrysanthemi indici by response surface methodology and artificial neural network

The extraction of linarin from Flos chrysanthemi indici by ethanol was investigated. Two modeling techniques, response surface methodology and artificial neural network, were adopted to optimize the process parameters, such as, ethanol concentration, extraction period, extraction frequency, and solvent to material ratio. We showed that both methods provided good predictions, but artificial neural network provided a better and more accurate result. The optimum process parameters include, ethanol concentration of 74%, extraction period of 2 h, extraction three times, solvent to material ratio of 12 mL/g. The experiment yield of linarin was 90.5% that deviated less than 1.6% from that obtained by predicted result.     

Optimization of Portulaca oleracea L. extract using response surface methodology and artificial neural network and characterization of bioactive compound by high-resolution mass spectroscopy

The well-known medicinal plant Portulaca oleracea L. (PO) is used as a traditional medicine and culinary herb to treat various diseases. Fatty acids, essential oils, and flavonoids were extracted from PO seeds and leaves using ultrasonic, microwave, and supercritical fluid extraction with RSM techniques. However, investigations on the secondary metabolites and antioxidant capabilities of the aerial part of PO (APO) are scarce. In order to extract polyphenols and antioxidants from APO as effectively as possible, this study used heat reflux extraction (HRE), response surface methodology (RSM), and artificial neural network (ANN) modeling. It also used high-resolution mass spectrometry to identify the APO secondary metabolite. A central-composite design (CCD) was used to establish the ideal ethanol content, extraction time, and extraction temperature to extract the highest polyphenolic compounds and antioxidant activity from APO. According to RSM, the highest amount of TPC (8.23 ± 1.06 mgGAE/g), TFC (43.12 ± 1.15 mgCAE/g), DPPH-scavenging activity (43.01 ± 1.25 % of inhibition) and FRAP (35.98 ± 0.19 µM ascorbic acid equivalent) were obtained at 60.0 % ethanol, 90.2 % time, and 50 °C. Statistical metrics such as the coefficient of determination (R²), root-mean-square error (RMSE), absolute average deviation (AAD), and standard error of prediction (SEP) revealed the ANN's superiority. Ninety-one (91) secondary metabolites, including phenolic, flavonoids, alkaloids, fatty acids, and terpenoids, were discovered using high-resolution mass spectrometry. In addition, 21 new phytoconstituents were identified for the first time in this plant. The results revealed a significant concentration of phytoconstituents, making it an excellent contender for the pharmaceutical and food industries.     

Ultrasonic treatment of water contaminated with various pollutants onto copper nanowires loaded on activated carbon using response surface methodology and artificial intelligent

In this study, the potential application of copper nanowires loaded on activated carbon for simultaneous removal of Disulfine blue (DB), Crystal violet (CV) and Sunset yellow (SY) has been described. The relation between adsorption properties with variables such as solution pH, adsorbent value, contact time and initial dyes concentration was investigated and optimized. A three‐layer artificial neural network (ANN) model was utilized to predict dyes removal (%) by adsorbent following conduction of experiments. The training of network at above mention experimental data confirms its ability to forecast the removal performance with a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm and tangent sigmoid transfer function (tansig) with 16 neurons at the hidden layer was applied. Parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) and desirability function. The accuracy of ANN was judged according to both MSE and AAD% at optimal conditions and results indicate its superiority to RSM model in term of higher R² and lower AAD% values. This observation was also corroborated by the parity plots between the predicted and experimental values. The ANN model was better in both data fitting and prediction capability in comparison to RSM model.     

Enhanced adsorption performance of a novel Fe‐Mn‐Zr metal oxide nanocomposite adsorbent for anionic dyes from binary dye mix: Response surface optimization and neural network modeling

A novel adsorbent, Fe‐Mn‐Zr metal oxide nanocomposite was synthesized and investigated for removal of methyl orange (MO) and eosin yellow (EY) dyes from binary dye solution. The magnetic nanocomposite has shown surface area of 143.01 m²/g and saturation magnetization of 15.29 emu/g. Optimization was carried out via response surface methodology (RSM) for optimizing process variables, and optimum dye removal of 99.26% and 99.55% were obtained for MO and EY dye, respectively with contact time 62 min, adsorbent dose 0.45 g/l, initial MO concentration 11.0 mg/l, and initial EY concentration 25.0 mg/l. A feed forward back propagation neural network model has shown better prediction ability than RSM model for predicting MO and EY dye removal (%). Adsorption process strictly follows Langmuir isotherm model, and enhanced adsorption capacities of 196.07 and 175.43 mg/g were observed for MO and EY dye, respectively due to synergistic effects of physicochemical properties of trimetal oxides. Surface adsorption and pore diffusions are the mechanisms involved in the adsorption as revealed from kinetic studies.     

Statistical analysis and optimization of photodegradation efficiency of methyl orange from aqueous solution using cellulose/zinc oxide hybrid aerogel by response surface methodology (RSM)

The photodegradation efficiency of cellulose-X/zinc oxide-Y (CA-X/ZnO-Y) aerogels was studied to degrade methyl orange (MO) as an organic dye pollutant from an aqueous solution under UV light irradiation. In this study, the initial pH of the solution (3, 7, and 11), the photocatalyst dosage (3, 6, and 9 g L^-1), the initial concentration of solution MO (10, 20, and 30 ppm), and the concentration of cellulose in CA-X/ZnO-Y hybrid aerogel (3, 6, and 9 wt%) were selected as four variable parameters, whereas the photoderadation performance was selected as the response. Moreover, the response surface methodology (RSM) analysis was carried out to investigate the influence of four various experimental factors at different times on the degradation of MO. The adequacy result of the proposed models displays that total of the proposed models can predict the photodegradation efficiency of MO by CA-x/ZnO-y aerogel. The optimization results of the process showed that pH = 3 and concentration of MO = 10 ppm, photocatalyst dosage (9 g L^-1), and MCC concentration (9 g) are the optimal level of the studied parameters. Also, the results showed that desirability of 0.96 confirms the acceptance and applicability of the model where the RSM model is a helpful technique for the optimum conditions design.     

Separation and determination of ciprofloxacin in seawater,human blood plasma and tablet samples using molecularly imprinted polymer pipette‐tip solid phase extraction and its optimization by response surface methodology

By synthesizing a molecular imprinted polymer as an efficient adsorbent, ciprofloxacin was micro‐extracted from seawater, human blood plasma and tablet samples by pipette‐tip micro solid phase extraction and determined spectrophotometrically. Response surface methodology was applied with central composite design to build a model based on factors affecting on microextraction of ciprofloxacin; including volume of eluent solvent, number of extraction cycles, number of elution cycles, and pH of sample. Other factors that affect extraction efficiency, such as type of eluent solvent, volume of sample, type, and amount of salt were optimized with one‐variable‐at‐a‐time method. Under optimum extraction condition, pH of sample solution was 7.0, volume of eluent solvent (methanol) was 200 µL, volume of sample solution was 10 mL, and the number of extraction and elution cycles was five and seven, respectively, amount of Na₂SO₄ (as salt) and MIP (as sorbent) were optimized at 150 and 2 mg, respectively. The linear range of the suggested method under optimum extraction factors was 5–150 µg/L with a limit of detection of 1.50 µg/L for the analyte. Reproducibility of the method (as relative standard deviation) was better than 7%.     

Application of response surface methodology for optimization of trace amount of diazinon preconcentration in natural waters and biological samples by carbon mesoporous CMK‐3

Preconcentration of trace amounts of diazinon by carbon mesoporous CMK‐3 in water and biological samples and measurement by high‐performance liquid chromatography were investigated. CMK‐3 was prepared using hexagonal SBA‐15 as the template. The synthesized materials were characterized by X‐Ray diffraction (XRD), Fourier transform infrared spectroscopy, Brunaur–Emmet–Teller, transmission electron microscopy and Boehm titration method. The preconcentration procedure was optimized using a multivariate optimization approach following a two‐stage process. The effect of analytical parameters including the amount of the CMK‐3 as an adsorbent, pH, type and volume of eluent and flow rate of eluent and sample were studied by a screening project, then the effective parameters were optimized by response surface methodology based on central composite design. The average extraction efficiency of diazinon under optimal conditions (CMK‐3 dosage = 25 mg, sample flow rate = 2.5 mL min⁻¹, eluent flow rate = 1.25 mL min⁻¹, volume of methanol as an eluent =3.5 mL and initial pH = 6) was 97.11%, which agrees well with the predicted response value (97.93%). The linearity of the method was in the range of 0.5–100 μg L⁻¹ with a correlation coefficient of 0.997. Enrichment factor, limit of detection and limit of quantification were 285.7, 0.09 and 0.23 μg L⁻¹, respectively. The relative standard deviation (RSD) under optimum conditions was 2.21% (n = 5). The proposed method was applied to determine diazinon in real water and biological samples. Recovery of diazinon from real samples was between 95.80 and 104.94% with an RSD of 0.19–4.65%. Thus, this method is suitable for the preconcentration and determination of diazinon in real water and biological samples.     

Dispersive magnetic solid‐phase extraction of phthalate esters from water samples and human plasma based on a nanosorbent composed of MIL‐101(Cr) metal–organic framework and magnetite nanoparticles before their determination by GC–MS

In this study, a magnetic metal–organic framework was synthesized simply and utilized in the dispersive magnetic solid‐phase extraction of five phthalate esters followed by their determination by gas chromatography with mass spectrometry. First, MIL‐101(Cr) was prepared hydrothermally in water medium without using highly corrosive hydrofluoric acid, utilizing an autoclave oven heat supply. Afterward, Fe₃O₄ nanoparticles were decorated into the matrix of MIL‐101(Cr) to fabricate magnetic MIL‐101 nanocomposite. The nanocomposite was characterized by various techniques. The parameters affecting dispersive magnetic solid‐phase extraction efficiency were optimized and obtained as: a sorbent amount of 15 mg; a sorption time of 20 min; an elution time of 5 min; NaCl concentration, 10% w/v; type and volume of the eluent 1 mL n‐hexane/acetone (1:1 v/v). Under the optimum conditions detection limits and linear dynamic ranges were achieved in the range of 0.08–0.15 and 0.5–200 μg/L, respectively. The intra‐ and interday RSD% values were obtained in the range of 2.5–9.5 and 4.6–10.4, respectively. Ultimately, the applicability of the method was successfully confirmed by the extraction and determination of the model analytes in water samples, and human plasma in the range of microgram per liter and satisfactory results were obtained.     

A rapid and efficient extraction method based on industrial MCM‐41‐miniaturized matrix solid‐phase dispersion extraction with response surface methodology for simultaneous quantification of six flavonoids in Pollen typhae by ultra‐high‐performance liquid chromatography

An industrial MCM‐41‐miniaturized matrix solid‐phase dispersion extraction coupled with response surface methodology was explored to determine L‐epicatechin, typhaneoside, isorhamnetin‐3‐O‐neohespeidoside, naringenin, kaempferol, and isorhamnetin in Pollen typhae by ultra‐high performance liquid chromatography connected to a photodiode array detection. Several variables were optimized in detail, including mesh number of sieve, type of adsorbent, mass ratio of sample to adsorbent, grinding time, methanol concentration, and elution volume. Central composite design was applied to optimize the best conditions for the maximum yields of the total flavonoids. The results displayed a good linear relationship (R > 0.9992) and the recoveries ranged from 92.9 to 103% (RSD < 4.53%) of the six flavonoids. The optimal method with high efficiency and low consumption was obviously better than heating reflux and ultrasonic extraction. It was proven that the developed industrial MCM‐41‐miniaturized matrix solid‐phase dispersion extraction coupled with simple ultra‐high performance liquid chromatography method could be a rapid and efficient tool for extraction and determination of flavonoids in natural products.