Multi-objective Response Surface Methodology Research Articles

A multi-objective flexible manufacturing system design optimization using a hybrid response surface methodology

The present study proposes a hybrid framework combining multiple methods to determine the optimal values of design variables in a flexible manufacturing system (FMS). The framework uses a multi-objective response surface methodology (RSM) to achieve optimum performance. The performance of an FMS is characterized using various weighted measures using the best–worst method (BWM). Subsequently, an RSM approximates the functional relationship between the FMS performance and design variables. The central composite design (CCD) is used for this aim, and a polynomial regression model is fitted among the factors. Eventually, a bi-objective model, including the fitted and cost functions, is formulated and solved. As a result, the optimal percentage for deploying the FMS equipment and machines to achieve optimal performance with the lowest deployment cost is determined. The proposed framework can serve as a guideline for manufacturing organizations to lead strategic decisions regarding the design problems of FMSs. It significantly increases productivity for the manufacturing system, reduces redundant labor and material handling costs, and facilitates production.

Characterisation of emission-performance paradigm of a reactivity-controlled compression ignition engine using artificial intelligent based multi-objective response surface methodology model fuelled with ternary fuel

Petroleum reserves all over the world have been severe to the point of depletion in the previous several decades. Modern transportation and industry employ diesel-fuelled engines because of their higher thermal efficiency. This study investigates the reactivity-controlled compression ignition engine characteristics utilising n-octanol and Moringa oleifera oil blended with diesel. Tests are carried out at maximum load with different bowl geometries to examine the influence of operating parameters, namely injection pressure (500–1000 bar) and static injection timing (23–27°bTDC). Box–Behnken Forecasting input variables like injection pressure and timing with different piston bowls is done using the response surface approach. Maximum brake thermal efficiency and brake power are achieved with this ideal model, and the emission of unburned HC and NO x is decreased. The effects of several variables, including piston bowls, fuel injection timing (FIT) and fuel injection pressure, are examined for an reactivity-controlled compression ignition engine running on Moringa oleifera oil/1-octanol fuel using an L27 orthogonal array. Various models were created and verified using experiment findings as the basis.

Computational fluid dynamics and multi-objective response surface methodology optimization of perforated-finned heat sinks

BackgroundThis study analyses turbulent flow and heat transfer characteristics of a three-dimensional perforated finned heat sink using computational fluid dynamics (CFD) and response surface methodology (RSM) methods. MethodsThe effects of perforation geometry, including size (1.85 ≤ σ ≤ 2) and cross-sectional shape (square, circular, hexagonal, and triangular), as well as the Reynolds number (25, 000 ≤ Re ≤ 40, 000) as design variables, on parameters such as friction drag force, pressure drag force, total drag force, and Nusselt number were investigated. In addition, using the well-known RSM technique, three accurate models were proposed for the percentage of heat transfer enhancement (PHTE), percentage of drag reduction (PDR), and percentage of weight reduction (PWR) as the most significant design objectives for any heat sink. RSM models served as the foundation for two- and three-objective optimizations. Significant FindingsResults indicate that fins with square perforations in high σ and fins with circular perforations in low σ could achieve exceptional thermal performance (PHTE > 70%). Compared to solid fins, fins with square perforations demonstrated improved PDR performance with a 20–40% reduction in total drag force. In addition, using fins with circular perforations can reduce the heat sink's weight by 45–65% compared to solid fins. Moreover, the optimal condition for the perforated finned heat sink can be attained by considering the values 40,000 and 1,946 for Reynolds number and σ, respectively, and by selecting a circular shape for the perforations. PHTE, PDR, and PWR increase in the optimal case by 68.98%, 35.87%, and 58.86%, respectively, compared to the base case (solid fin).

Design Analysis of Heat Sink Using the Field Synergy Principle and Multitarget Response Surface Methodology

This study describes a novel heat sink design approach employs the field synergy concept and multitarget response surface methodology (RSM). The multiobjective response surface methodology can be used to determine the simulation equations that will maximize the heat transfer of fins at various fin heights, fin angles, and fin circumferences, when considering the impact of jet flow heat exchange. The goal of the response value was to maintain the minimum possible average field coangle and fin temperature. The results show that the ideal heat sink size would be the following: fins with a height of 50 mm, an angle of 60 degrees, and the number of fins equal to five. We examined the impact of wall speed on the heat transfer caused by the field synergy angle. Our findings suggest that with the synchromesh of the display field, heat-dissipation efficiency rises.

Multi-attribute optimization of sustainable aviation fuel production-process from microalgae source

The aviation sector has been one of the most significant contributors to greenhouse gas (GHG) emissions; there is an urgent need to transition from traditional fossil-based jet fuel to sustainable aviation fuel to meet net-zero targets by 2030. The use of renewable aviation fuel may be regarded as the most effective option for reducing GHG emissions in the aviation sector while allowing for long-term growth. In this study, the microalgae oil was transformed to develop hydrocarbons with the boiling point range of jet fuel. Following the transesterification of algal oil fatty acids to algal oil methyl ester, fractional distillation was carried out. The Box-Behnken technique was used to design experiments for efficient resource utilization by minimizing the number of tests during transesterification. The quantitative relationship function between input (molar ratio of methanol to oil, catalyst concentration, and temperature) and % yield of methyl ester as an output of the process was developed using analysis of variance (ANOVA). The ANFIS (adaptive neuro-fuzzy inference system) was utilized to develop a prognostic model with good prediction effectiveness. MORSM (multi-objective response surface methodology) was also used to build a prediction model using correlations. A set of statistical indicators and Theil's U2 were used to examine the prediction efficacy and model uncertainty of ANFIS and MORSM. On both statistical indices and Theil's U2, the ANFIS-based model outperformed. To get the best results, the desirability approach was used to improve operating parameters. According to the desirability approach, the best operating parameters were catalyst concentration at 2.09%, methanol to oil ratio at 9.17%, and temperature at 60.49 °C, which resulted in the greatest yield of 91%.

Prediction-optimization of the influence of 1-pentanol/jatropha oil blends on RCCI engine characteristics using multi-objective response surface methodology

Despite their higher viscosity and longer ignition delay, alternative fuels like biodiesel can be used instead of fossil fuels. This study investigated the potential of the 1-pentanol (10, 20, and 30%) as the low reactivity fuel, which is a cetane value improver, and jatropha oil blended with diesel is considered the high reactivity fuel. 1-Pentanol was found to affect emission and combustion properties. Response surface methodology is adopted to forecast the operating parameters such as injection timing (23, 25, and 27° bTDC) and engine load (50, 75, and 100%). This ideal model is used to obtain engine characteristics for different fuel blends. The model's robustness was demonstrated because Theil's uncertainty in its predictive abilities was less than 0.1189(Theil's U2). With a mean absolute percentage error of less than 1.18%, Nash-Sutcliffe efficiency was outstanding (0.9885–0.9995). With the help of results obtained from experiments, various models were developed and validated. The ideal engine parameters found were 73% of engine load, PES of 24%, and FIT of 24.9° bTDC (advanced), and under this configuration, better engine characteristics are achieved. Also, ANOVA, a statistically valid test, is used to develop a regression model. The regression model is adequate for the following R2 values obtained, according to the test results: BTE: 99.57%, BSFC: 98.86%, Pmax: 98.83%, NOx: 98.22%, HC: 99.70% and CO: 98.38% respectively.

Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi-Objective Response Surface Methodology

Sustainable Development Goals were established by the United Nations General Assembly to ensure that everyone has access to clean, affordable, and sustainable energy. Third-generation biodiesel derived from algae sources can be a feasible option in tackling climate change caused by fossil fuels as it has no impact on the human food supply chain. In this paper, the combustion and emission characteristics of Azolla Pinnata oil biodiesel-diesel blends are investigated. The multi-objective response surface methodology (MORSM) with Box–Behnken design is employed to decrease the number of trials to conserve finite resources in terms of human labor, time, and cost. MORSM was used in this study to investigate the interaction, model prediction, and optimization of the operating parameters of algae biodiesel-powered diesel engines to obtain the best performance with the least emission. For engine output prediction, a prognostic model is developed. Engine operating parameters are optimized using the desirability technique, with the best efficiency and lowest emission as the criteria. The results show Theil’s uncertainty for the model’s predictive capability (Theil’s U2) to be between 0.0449 and 0.1804. The Nash–Sutcliffe efficiency is validated to be excellent between 0.965 and 0.9988, whilst the mean absolute percentage deviation is less than 4.4%. The optimized engine operating conditions achieved are 81.2% of engine load, 17.5 of compression ratio, and 10% of biodiesel blending ratio. The proposed MORSM-based technique’s dependability and robustness validate the experimental methods.

Highly effective pre-concentration of thymol and carvacrol using nano-sized magnetic molecularly imprinted polymer based on experimental design optimization and their trace determination in summer savoury, Origanum majorana and Origanum vulgare extracts

To ascertain thymol and carvacrol in pharmaceutical syrups, a valid and effective magnetic molecular imprinted polymer dispersive solid phase microextraction (MMIP-DSPME) process was developed in this study, which was in combination with a high performance liquid chromatography-ultra violet (HPLC-UV) technique for the assessment of thymol and carvacrol separation and pre-concentration. Contact time, eluent kind and volume, pH, the mass of the MMIP were all taken into consideration as key factors. Design expert and multi-objective response surface methodology (RSM) were used to optimize these variables. The mass of the MMIP, sample pH, eluent kind, time of sorption, the volume of eluent, and time of elution were 10 mg, 6, acetonitrile, 28 min, 200 µL, and 5.5 min, respectively, for the maximum extraction recovery of the analytes. The limit of detection (LOD) was 0.042 ng mL−1 at the optimal conditions, while the value for the limit of quantification (LOQ) was 0.140 ng mL−1. At the optimized conditions for thymol and carvacrol, the suggested MMIP sorbent had sorption capacities of 64.1 and 72.6 mg g−1, respectively. Furthermore, for triplicate measurements, the linear dynamic range (LDR) was 0.40–5000 ng mL−1, and the method's accuracy (RSD %) was 6.26%. The saturation magnetization for the MMIP was 19.0 emu g−1 obtained by VSM, allowing the sorbent to be separated quickly. The sorption experiments confirmed the large sorption capacity of the MMIP for thymol and carvacrol, as well as its homogeneous binding sites. The extraction recovery for thymol and carvacrol was 96.9–103.8% and 96.6–105.4%, respectively, at all spiked amounts (20, 100, 200, and 500 ng mL−1). The findings of seven desorption–regeneration cycles using MMIP demonstrated the high stability of the sorbent. The MMIP revealed a particular behavior of sorption for thymol and carvacrol, implying a selective, simple, effective, and flexible analytical method.

Prediction-Optimization of the Effects of Di-Tert Butyl Peroxide-Biodiesel Blends on Engine Performance and Emissions Using Multi-Objective Response Surface Methodology

Abstract Alternative fuels, such as biodiesel, can be used in place of fossil fuels, although they have a greater viscosity and a longer igniting delay. To compensate for these limitations, several additives are added to biodiesel. The cetane improver di-tert butyl peroxide (DTBP) was investigated as an additive in this work. DTBP was shown to influence the combustion and emission properties of waste cooking oil biodiesel-diesel blends. The multi-objective response surface technique (MORSM) with Box-Behnken design was used to decrease the number of trials to conserve precious resources such as human effort, time, and money. Theil's uncertainty for the model's predictive capabilities (Theil's U2) was less than 0.1189, demonstrating its robustness. Nash-Sutcliffe efficiency was excellent (0.9885–0.9995), with a mean absolute percentage error of less than 1.32%. The engine operating parameters that were optimized were 71.64% engine load, 4964 ppm DTBP additive, and 24.98-deg advance ignition timing. The MORSM-based proposed technique's reliability and robustness validate the usage of DTBP with biodiesel blends, model prediction, and optimization.

Characterization of emission-performance paradigm of a DI-CI engine using artificial intelligent based multi objective response surface methodology model fueled with diesel-biodiesel blends

ABSTRACT The trade-off between performance and emissions must be in proper balance with the mineral fuels in executing a novel idea of renewable fuel design for existing and future combustors. Higher alcohols have superior activation energy and are an ideal choice as a diesel-biodiesel additive. Therefore, an investigative study is carried out to analyze the compatibility of biodiesel-diesel with n-Octanol in different proportions. An ignition improver, DEE is also added in different proportions in lower heating values and Cetane numbers standpoint. Results enumerated the benefit of prolonged ignition delay of higher alcohol and DEE have raised the peak pressures and better performance in terms of BSEC is recorded, and the emissions became leaner as NOx, unburnt Hydro Carbons and CO emissions are decreased. Furthermore, the engine responses were fed to an Artificial Intelligent model, Multi-Objective Response Surface Methodology to check the inherent capability of predicting the engine behavior and to optimize the input parameters. The model was tested on a statistical base and found robust as the regression coefficient R, R2 (Adj.), R2 (Pred.), MSRE and a special performance correlation metric like NSE which were found satisfactory. The model fetched a range of solutions based on the desirability criteria and suggested optimum blend composition as 20% biodiesel, 10% n-octanol, 2.526% DEE with 67.474% petrodiesel with the desirability of 0.985. The biodiesel along with the additives replaced diesel up to 34% by volume. Abbreviations: AI: Artificial Intelligence; FFD: Full Factorial Design; ANOVA: Analysis of Variance; HC.u: Hydrocarbons (Unburnt); BSEC: Brake Specific Energy Consumption; MORSM: Multi Objective Response Surface Methodology; CO: Carbon Monoxide; MSRE: Mean Squared Relative Error; DEE: Di Ethyl Ether; NSE: Nash–Sutcliffe Coefficient of Efficiency; DI: Direct Injection; NOx: Nitrogen Oxides; DICI: Direct injection Compression Ignition; TheilU2: TheilU2 uncertainity

Selection of best process parameters for friction stir welded dissimilar Al-Cu alloy: a novel MCDM amalgamated MORSM approach

The paper focuses on developing a novel decision-making model, to select the best welding parameters for friction stir welding of dissimilar aluminium to copper plates. A set of 21 experiments are carried out based on a d-optimal design matrix. Apart from considering the rotational speed and traverse speed as input variables, the position of aluminium and copper either in the advancing or retreating side is also considered as an input variable. On the successful fabrication of joints, the ultimate tensile strength, elongation, hardness and impact energy are estimated experimentally which are considered as performance parameters. The proposed decision-making process is categorized under multi-criteria decision-making (MCDM) methods. In the first phase, the novel MCDM model is applied to select the best welding parameters. The concept of the model is to minimize the risk of not attaining the global best result. The results obtained by the MCDM method is justified by carrying out a sensitivity analysis and finally validated with the results obtained from the fuzzy logic decision-making approach. The second phase is related to the computation of optimized values for the joining process that reports the development of a mathematical model by response surface methodology to predict different performance parameters. This is followed by conducting a trade-off analysis by developing a multi-objective response surface methodology (MORSM) that computes the optimal welding parameters. Finally, a confirmatory test is carried out for validation of the proposed MORSM model.

Nano-sized Fe3O4@SiO2-molecular imprinted polymer as a sorbent for dispersive solid-phase microextraction of melatonin in the methanolic extract of Portulaca oleracea, biological, and water samples

In this study, a magnetic molecularly imprinted polymer (MMIP (Fe3O4@SiO2-MIP)) was used for the dispersive magnetic solid-phase microextraction (d-MSP-μ-E) to design an easy and effective method for melatonin (MLT) extraction in the methanolic extract of Portulaca oleracea, human urine and plasma, and water samples. HPLC with UV detection was utilized, and pH, the type and volume of eluent, MMIP mass, and contact time were considered as effective factors in the study of MLT separation and pre-concentration. These factors were optimized by Plackett–Burman and multi-objective response surface methodology (RSM). The values were 10 mg, 14 min, 4.2, methanol, 0.180 mL, 2.5 min, for the MMIP mass, time of sorption, sample pH, eluent type, eluent volume, and time of elution, respectively. At the optimum conditions, the limit of detection (LOD) was 0.046 ng mL−1, and the limit of quantification (LOQ) was 0.156 ng mL−1. The sorption capacity of the proposed MMIP sorbent was 109.1 mg g−1 at the optimum conditions. Besides, linear dynamic range (LDR) was 0.2–4200 ng mL−1, and the precision of the method (RSD %) for triplicate measurements was <6.1%. The MMIP showed saturation magnetization of 19.75 emu g−1, resulting in fast separation of the sorbent. The sorption test revealed the high sorption capacity of the MMIP for MLT and its homogeneous binding sites. In all spiked levels (50, 100, 200, and 500 ng mL−1), 93.07–104.1% was the range obtained for the recovery of MLT. The relative selectivity factor (β) values of MLT/tryptophan, MLT/serotonin, MLT/ferulic acid, MLT/mefenamic acid, MLT/quercetin, MLT/luteolin, and MLT/chlorogenic acid were 1.60, 1.68, 2.02, 2.38, 2.32, 2.40, and 2.50, respectively. The results of desorption–regeneration cycles (seven times) by employing the MMIP showed the high stability of the resultant material. In conclusion, the MMIP combined with the magnetic separation showed a specific sorption behavior for MLT and suggested a simple, flexible, selective, and powerful analytical tool.

Modelling and optimizing performance parameters in the wire-electro discharge machining of Al5083/B4C composite by multi-objective response surface methodology

Composite is an artificial multiphase material that constituent phases which are chemically dissimilar and separated by a distinct interface. As a result of such anisotropic and non-homogeneous formation machining of composites is a strenuous task. Therefore, the need arose for formulating a stable predictive model. The comprehensive intention of this study is to develop a highly robust and stable predictive soft-computing model for forecasting the machining performance of Al-5083 alloy reinforced with B4C particles (Al5083/B4C). The study mostly emphasize on selecting the best machining parameters among the conducted experiments and evaluating the optimal machining parameters for Al5083/B4C composite in wire-electro discharge machin. The paper is an integration of equally weighted experimental as well as computational study. In the experimental part of the study, 5 different specimen of Al5083/B4C is prepared by the ex situ technique through stir casting process. The experimental part includes design of experiment by Taguchi’s method. The computational part of the study comprised of three different stages. The first stage involves the mathematical modelling of the performance measures and statistical scrutiny of the models. In the second stage, the best machining parameters are selected based on the fuzzy IF–then rules. The final stage of the manuscript is the trade-off analysis conducted to obtain the optimal machining parameters. In order to test the robustness of the formulated model an experimental validation is carried out at the optimal machining combination. The error calculated from the comparison is within the range of 2–5% which justifies the objective of the study.

Novel Multi-objective Decision-Making and Trade-Off Approach for Selecting Optimal Machining Parameters of Inconel-800 Superalloy

Inconel-800 is a superalloy having moderate strength, sufficient resistance to oxidation at high temperature and carburization. Work hardening nature of the material makes it very hard to machine using traditional methods. Wire-cut electrical discharge machining (WEDM) is most widely applied for difficult-to-machine materials because of its capability to produce jobs with minute accuracy and precision. Time taken to process job is the major drawback of the WEDM technique. Therefore, the need arose for formulating a stable predictive model which reduces human effort and maximizes production. The comprehensive intention of this study is to develop robust and stable multi-objective decision-making and soft computing models for selecting the best machining parameters and forecasting the machining performance of Inconel-800 superalloy. Taguchi’s method is applied for designing the experimental phase of the study. The computational part is a three-stage process. The first stage is selecting the best machining parameters of WEDM by multi-criteria decision-making (MCDM) technique. A novel MCDM technique is proposed that is based on the concept of risk minimization. The second stage involves the formulation of predictive model using soft computing technique like response surface methodology (RSM). The performance of the technique was scrutinized on a statistical platform to check the gullibility of the model. The final stage is the trade-off analysis by multi-objective RSM (MORSM) which strategically draws balance among the multiple outputs of the problem. Finally, the proposed MCDM and MORSM models are applied for selecting the best and optimal combination of machining Inconel-800 by WEDM technique.

Optimized doxycycline-loaded niosomal formulation for treatment of infection-associated prostate cancer: An in-vitro investigation

Developing drug delivery systems with both antibacterial and anti-cancer effects is of importance in the treatment process of infection-associated cancers, especially prostate cancer. In this study, Span 60, Tween 60, and cholesterol were used to formulate doxycycline-loaded niosomes as a promising drug carrier system as either antibacterial or anticancer formulation. The formulation process was optimized by multi-objective response surface methodology (RSM), and then characterized. The developed niosomal formulation showed great storage stability for up to 2 weeks. In addition, they showed remarkable drug release in acidic solution (pH = 3) compared with physiological pH (7.4). The in-vitro performances of the as-developed niosomal formulations were investigated for two different applications: antibacterial and anticancer. The antibacterial properties of doxycycline-loaded niosome were investigated using different Gram-negative, and Gram-positive bacteria, where considerable (50–75%) decreases in MIC values were observed. Moreover, the niosomal formulation also possessed promising chemotherapy effects against prostate cancer cells (PC3) but enhanced biocompatibility against normal HEK293 cells. The enhanced chemotherapy effect was correlated to the regulation of different genes as well as changes in cell cycle of PC3 cells after treatment with the niosomal formulation. These carriers could be served as a potential drug delivery system for the treatment of prostate cancer.

Synergistic effect of silica and pure palm oil on the machining performances of Inconel 690: A study for promoting minimum quantity nano doped-green lubricants

Nanofluid based lubricants have immense acceptability in sustainable and eco-friendly machining of superalloys – this is due to their superior heat transfer ability. Extreme demands for sustainable manufacturing compelled the manufacturers to avail synergistic benefits with higher machinability and lower carbon footprint on the environment. For that purpose, the performance of an indigenously developed minimum quantity nano-green lubricant is investigated in milling of Inconel 690. Accordingly, nano-silica (vol% varying from 0.5 to 1.5%) was reinforced into palm oil and sprayed into the tool-chip interface through minimum quantity lubrication set up. The milling operations were executed under five different lubricating mediums: dry, minimum quantity palm oil, 0.5% silica deposited palm oil, 1% silica deposited palm oil, and 1.5% silica deposited palm oil. Comparative tool life, surface roughness, resultant cutting force, and cutting temperature were assessed. Subsequently, SEM and EDX images were analyzed to confirm the governing tool wear mechanisms. Experimental data shows that 1% silica deposited palm oil medium performs better concerning all machining responses. Furthermore, a multi-objective response surface methodology (MORSM) confirmed that speed of 88.18 m/min, feed of 0.11 mm/tooth, depth of cut of 0.50 mm, and 1%-MQNGL based lubricating condition are the optimal design parameters to achieve sustainable working environment.

Optimization of mini plantation tractor chassis by using multi-objective response surface methods

To meet the requirement of the oil palm plantation industry, mini plantation tractor chassis is developed with increasing of transport capacity from 500 kg to 750 kg. The design goals of optimum design are safe, transport efficiency and lightweight chassis weight. The aim of this study is to determine the effect and optimum conditions of design parameters in the form of profile width, profile height, and profile thickness of mini plantation tractor chassis. The parameter design is set by 3 levels. Chassis modeling is done by using Finite Element Analysis software with 3-dimensional model. The deformation and stress behavior are observed on the chassis due to vertical bending and the torsional loading model. Multi-Objective Response Surface Methodology was applied to obtain the optimum design parameter conditions on the results of stress and deformation observation on the chassis. Based on the RSM optimization results, three optimum chassis design candidates were produced. In the vertical bending loading model, it is found that the profile width has the greatest influence to stress reduction contribution, and the profile height has the greatest influence to deformation reduction contribution. In the torsional loading model, the profile height has the greatest influence to stress reduction contribution, and the profile thickness has the greatest influence to deformation reduction contribution. The optimum design based on consideration in both loading models is profile thickness = 5.5 mm, profile height = 100 mm, and profile width = 55 mm.

A novel electrospun β-CD/CS/PVA nanofiber membrane for simultaneous and rapid removal of organic micropollutants and heavy metal ions from water

In this work, a novel electrospun β-cyclodextrin (β-CD)/chitosan (CS)/polyvinyl alcohol (PVA) nanofiber membrane was prepared to simultaneously and rapidly remove organic and heavy metal micropollutants. The preparation conditions were optimized by multi-objective response surface methodology, and obtained as follows: 100% of the amount of β-CD (relative to PVA), 49.49% of the amount of CS (relative to PVA), 20 cm of the distance between the needle tip and the collector drum, 0.23 mL h−1 of the flow rate of the spinning solution, and 8.72% of the amount of PVA (relative to water). The characterization by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) showed a well cross-linked β-CD/CS/PVA nanofiber membrane was formed. In the static and dynamic adsorption experiments, the results demonstrated that the β-CD/CS/PVA nanofiber membrane was highly efficient in simultaneous and rapid removal of organic and heavy metal micropollutants in water treatment process.

Optimization of Vortex-Assisted Dispersive Liquid-Liquid Microextraction for the Simultaneous Quantitation of Eleven Non-Anthocyanin Polyphenols in Commercial Blueberry Using the Multi-Objective Response Surface Methodology and Desirability Function Approach.

In the present study, 11 non-anthocyanin polyphenols, gallic acid, protocatechuate, vanillic acid, syringic acid, ferulic acid, quercetin, catechin, epicatechin, epigallocatechin gallate, gallocatechin gallate and epicatechin gallate—were firstly screened and identified from blueberries using an ultra performance liquid chromatography–time of flight mass spectrography (UPLC-TOF/MS) method. Then, a sample preparation method was developed based on vortex-assisted dispersive liquid-liquid microextraction. The microextraction conditions, including the amount of ethyl acetate, the amount of acetonitrile and the solution pH, were optimized through the multi-objective response surface methodology and desirability function approach. Finally, an ultra performance liquid chromatography–triple quadrupole mass spectrography (UPLC-QqQ/MS) method was developed to determine the 11 non-anthocyanin polyphenols in 25 commercial blueberry samples from Sichuan province and Chongqing city. The results show that this new method with high accuracy, good precision and simple operation characteristics, can be used to determine non-anthocyanin polyphenols in blueberries and is expected to be used in the analysis of other fruits and vegetables.

Optimization of chitosan/activated charcoal-based purification of Arthrospira platensis phycocyanin using response surface methodology

In the present paper, a mild and rapid purification method for phycocyanin from Arthrospira platensis was optimized by multi-objective response surface methodology. The independent variables were chitosan content (0.1–0.5% w/v), activated charcoal content (3–15% w/v), and stirring time (2–18 min). Under the optimum conditions (i.e., 0.24% w/v chitosan, 8.4% w/v activated charcoal, and 10.2 min stirring time), the maximum phycocyanin purity and concentration of 3.14 ± 0.12 and 0.27 ± 0.01 mg mL−1 were attained, respectively, which were in fact 1.67-folds and 1.17-folds higher than those obtained by the conventional method, i.e., ammonium sulfate precipitation. The phycocyanin purity recorded in this study met the purity requirements considered for reactive grade. Moreover, higher antioxidant activity and fluorescence intensity were recorded for the phycocyanin purified by chitosan and activated charcoal vs. the conventional method. It was also found that antioxidant activity and fluorescence intensity of phycocyanin exhibited a direct relationship. The maximum phycocyanin stability was observed at pH 8 and temperature values < 45 °C. Additionally, the stability was gradually diminished under alcohol and light stresses. Conclusively, the phycocyanin obtained by the method developed herein possessed a considerable purity and concentration with noteworthy functional properties.