Box-Behnken Design Approach Research Articles

Modified mix design and statistical modelling of lightweight concrete with high volume micro fines waste additive via the Box-Behnken design approach

The research paper presents a modified mix design to develop cellular foamed concrete (CFC) using hazardous micro fine in-situ stone sludge waste in the form of low grade dolomitic marble slurry (MS), as a replacement of fine river sand (FRS) and tap water. To utilize maximum amount of MS, optimization was done through the Box-Behnken design (BBD) of response surface methodology (RSM). The effects of three effective variable parameters i.e. temperature of water to be added into foaming agent solution (Tw), flow value (FV) and percentage replacement level of MS (%Rms) on the four responses i.e. oven dry density (ρOD), compressive strength at 28 days (fc.28d), strength performance factor at 28 days (PFc.28d) and thermal conductivity (λ-value) were stastically analyzed.ANOVA and multi-objective optimization was performed to finalize the mix having desirable hardened and functional properties. ρOD, fc.28d and λ-values of the CFC were varied in the range from 985 kg/m3 to 1268 kg/m3, from 3.89 MPa to 4.94 MPa and from 0.340 to 0.441 W/m.K, respectively. Advanced characterization techniques such as- field emission scanning electron microscopy (FE-SEM) and 3D morphological analysis were also performed to investigate the interaction of MS with CFC microstructure and cement paste matrix. The ρOD, compressive strength (CS), and λ-value of the CFC sample, mixed with 60.0 wt% MS; was 1046 kg/m3, 4.19 MPa, and 0.372 W/m⋅K, respectively.

Performance and Emission Analysis of Compression Ignition Engine With Neem Methyl Ester Mixed With Cerium Oxide (CeO2) Nanoparticles

Abstract This paper depicts Box-Behnken design (BBD) approach to optimize the performance and emission characteristics of adjustable compression ratio, single- cylinder diesel engine with nanoparticle-blended biofuel. Cerium oxide (CeO2) nanoparticles and diethyl ether (DEE) are mixed with neem methyl ester (NME) in corresponding ratios as per BBD experimental plan. Engine performance characteristics brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and NOx, CO, HC, and CO2 emissions have been analyzed. To study the influence of input parameters, quadratic models are developed on each output response using analysis of variance (ANOVA). Desirability function approach has been used to optimize the performance of multi-response parameters. The results revealed that nanoparticles mixed blends of NME and DEE enhances the performance characteristics and reduce the harmful emissions.

Kinetic study and Box–Behnken design approach to optimize the sorption process of toxic azo dye onto organo-modified bentonite

Kinetic study was applied for sodium bentonite (Na-B) and hexadecylpyridinium bentonite (HDP-B) under different amounts, namely 50% (50HDP-B), 100% (100HDP-B), and 200% (200HDP-B) with respect to cation exchange capacity (CEC). Pseudo first-order and pseudo second-order kinetic models were performed to optimize the sorption of Congo red (CR) dye from aqueous solution. The experimental data fit the pseudo second order kinetic model well. The sorption capacity (qe) of CR dye by the organo-bentonites at equilibrium was 36.0 mg g−1 (72.1%) for 50HDP-B, 48.05 mg g−1 (96.1%) for 100HDP-B, and 49.2 mg g−1 (98.4%) for 200HDP-B. These results were considerably higher than that found by Na-B. Response surface methodology with three-variable, three-level Box–Behnken design was applied for 100HDP-B to describe the removal of CR dye. The effects of three variables, namely temperature, adsorbent dosage, and initial dye concentration, were studied. Predicted values of adsorption efficiency were found to be in good agreement with the obtained experimental values (R2 = 0.97). A second-order polynomial model successfully described the effects of independent variables on the CR dye removal. At the optimized condition, the toxic azo dye could be quantitatively removed from aqueous solution. The results of the present study suggest that the organo-bentonite can be used as an efficient sorbent for dye removal from aqueous solution.

Separation of Methylparaben by emulsion liquid membrane: Optimization, characterization, stability and multiple cycles studies

Parabens are esters of p-hydroxybenzoic acid and are commonly used as preservatives in personal care products, pharmaceutical drugs and foodstuffs. The main concern arises from their endocrine disrupting potential. Investigations were carried out for the separation of methylparaben from aqueous solution by emulsion liquid membrane. Extraction efficiency of 93.54% was achieved using emulsion consisting of 2% w/v TOA, 2% w/v Span 80 and 0.1 N NaOH. Box-Behnken Design approach was used to optimize the process variables. Studies were also carried out to investigate the influence of hydrophile-lipophile balance (HLB) and Multiwall carbon nanotubes (MWCNT) on the emulsion stability. Characterization of emulsions were performed using Photomicrographs, Dynamic light scattering, Turbiscan, Atomic force microscope and Fourier transform infrared spectroscopy. 100% extraction was obtained at a contact time of 10 minutes using 2% w/v blended surfactant: Span 80 and Span 20 (HLB 5.7) with MWCNT. Multiple cycles studies revealed that the emulsion can be used in batch process up to fourth cycle without de-emulsification.

Parametric analysis and optimization of hard turning at different levels of hardness using wiper ceramic insert

In this paper, an attempt is made to evaluate the performance of wiper ceramic cutting insert and observe the influence of hardness on Arithmetic Mean Roughness (Ra), Mean depth of roughness (Rz), Total roughness (Rt) and Tool wear (Vb) during the hard turning of AISI4340 steel. Twenty-seven experimental runs were planned according to the Response Surface Methodology based Box-Behnken Design approach and those are performed on a CNC lathe. Analysis of Variance is employed to analyze the significance of the input parameters and in terms of their contribution percentages. Mathematical models for surface roughness characteristics and tool wear were developed using the Response Surface Methodology. Results reveal that the hardness and feed rate have significant effects on the surface roughness characteristics and tool wear. Finally, the experimental data further analysed to predict optimal machining conditions for multiple response factors optimization through Desirability function approach.

Novel organic-based postharvest sanitizer formulation using Box Behnken design and mathematical modeling approach: A case study of fresh pistachio storage under modified atmosphere packaging

In the present study Box Behnken Design (BBD) and Artificial Neural Network-Genetic Algorithm (ANN-GA) hybrid system were used for predicting and optimizing a new organic-based postharvest sanitizer for fresh pistachio nuts under modified atmosphere packaging, combining different concentrations of five generally recognized as safe (GRAS) ingredients including H2O2 (1, 3 and 5%), Na2CO3 (1, 3 and 5%), K2CO3 (1, 3 and 5%), citric acid (CA) and acetic acid (AA, 1000, 5500 and 10,000 mg L−1). The nuts were submerged in sanitizer solutions for two minutes then dried for five minutes in ambient condition and packaged in polyethylene bags injected with ambient atmospheric gas (21% O2, 0.03% CO2, and 87% N2). BBD as a computer-based design of experiment (DOE) tool reduced the cost, labor and time needed to perform the experiment by reducing the number of treatments from 243 to 40 while ensuring the well-sampled experiment design. NaOCl (100 mg L−1) and distilled water (DW) were used as controls for the validation experiment. The ANN-GA described relations between five input (H2O2, Na2CO3, K2CO3, CA, and AA) and four output (total viable count, enzymatic browning, overall acceptability, and taste) variables. Sensitivity analysis was used to find the most important ingredient as input variable affecting output variables. ANN-based models could effectively fit the supplied data on the total viable count and quality parameters of pistachios to various concentrations of the ingredients in the sanitizers. Based on the ANN-GA results, the input variables concentrations of 3.6% H2O2, 3.9% Na2CO3, 3.2% K2CO3, 8118.5 (mg L−1) CA, and 8202.8 mg L−1 AA could result in the lowest total viable count (0.07 log colony forming units (CFU g-1)). The lowest amount of enzymatic browning (1.48%) can be obtained by applying a mixture of 4.26% H2O2, 3.7% Na2CO3, 3.7% K2CO3, 7785.4 mg L−1 CA, and 7363.5 mg L−1 AA. The highest overall acceptability (1.38) can be achieved using a mixture of 3.0% H2O2, 3.0% Na2CO3, 3.4% K2CO3, 8190.1 mg L−1 CA, and 8316.7 mg L−1 AA. The best taste (1.21) was predicted to be attained using a combination of 3.3% H2O2, 4.1% Na2CO3, 3.9% K2CO3, 8114.5 mg L−1 CA, and 7574.0 mg L−1 AA. According to the sensitivity analysis, AA was the most important factor in reducing total viable count and enzymatic browning and enhancing overall acceptability. The validation test showed that the optimized sanitizer for total viable count was superior to the 100 mg L−1 NaOCl which is the most used commercial sanitizer for fresh crops.

Improvement in Machined Surface with the use of Powder and Magnetic Field Assisted on Machining Aluminium 6061 Alloy with EDM

Surface characteristics are the major challenges faced by the manufacturing industry when materials are machined with electrical discharge machining. Magnetic Field assisted powder mixed electrical discharge machining (MFAPM-EDM) is hybrid machining process which has shown the good potential of improving the surface characteristics. In this present paper, machining of aluminium 6061 alloy was carried out with aluminium powder mixed in EDM oil and in presence of magnetic field using EDM process Magnetic field, spark on duration, spark off duration, pulse current and powder concentration were selected as variable machining parameters and surface roughness as a response. Box-Behnken design approach based on response surface methodology was used for performing the experiments. In the present analysis, current was observed as the most significant parameter followed by spark on duration, powder concentration and magnetic field on surface roughness. Improvement in surface roughness with lesser voids and craters were observed under the influence of magnetic field and aluminium powder mixed in EDM oil.

Adsorptive removal of surfactant using dolochar: A kinetic and statistical modeling approach.

Disturbingly high rates of consumption of surfactants in household and industries have led to mark them as emerging contaminants in the environment. In the present work, removal of sodium dodecyl sulfate (SDS), an anionic surfactant, using an industrial waste (dolochar) was explored. The adsorbent material was characterized with the help of Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). Kinetic evaluation was performed using first, pseudo-first, second, and pseudo-second order models. Adsorption of SDS over dolochar was expressed best by pseudo-second order kinetic model with regression coefficient (R2 ) of .99. Three input parameters including adsorbent dose (20-10g/L), initial concentration (30-100mg/L) of the surfactant, and contact time (2-60min) were chosen for optimization using response surface methodology based on Box-Behnken design (BBD) approach. A total of 15 experiments were run to examine the effect of these variables on removal of SDS by dolochar in a multivariate system. A regression analysis indicated the experimental data fitted well to a quadratic polynomial model with coefficient of regression (R2 ) as .99. ANOVA and lack-of-fit test depicted the precision and efficiency of the model. The optimized conditions for SDS removal were found to be adsorbent dose 16.62g/L, contact time 40min, and initial concentration 47mg/L with removal efficiency as 98.91%. PRACTITIONER POINTS: Daily ablutions and use of personal care products introduce a number of surfactants and recalcitrant compounds into the environment. Adsorption is a handy and easy to operate treatment technique to remove graywater pollutants. Kinetic and statistical modeling may be recommended as one of the most prominent tools to understand the removal mechanism. Decentralized treatment of graywater using industrial wastes is recommended as sustainable solution in the developing nations.

Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm

Machining of shape memory alloys (SMAs) without losing the shape memory effect could immensely extend their applications. Herein, the wire electric discharge machining process was used to machine NiTi—a shape memory alloy. The experimental methodology was designed using a Box-Behnken design approach of the response surface methodology. The effects of input variables including pulse on time, pulse off time, and current were investigated on the material removal rate, surface roughness, and microhardness. ANOVA tests were performed to check the robustness of the generated empirical models. Optimization of the process parameters was performed using a newly formulated, highly efficient heat transfer search algorithm. Validation tests were conducted and extended for analyzing the retention of the shape memory effect of the machined surface by differential scanning calorimetry. In addition, 2D and 3D Pareto curves were generated that indicated the trade-offs between the selected output variables during the simultaneous output variables using the multi-objective heat transfer search algorithm. The optimization route yielded encouraging results. Single objective optimization yielded a maximum material removal rate of 1.49 mm3/s, maximum microhardness 462.52 HVN, and minimum surface roughness 0.11 µm. The Pareto curves showed conflicting effects during the wire electric discharge machining of the shape memory alloy and presented a set of optimal non-dominant solutions. The shape memory alloy machined using the optimized process parameters even indicated a shape memory effect similar to that of the starting base material.

Separation of Diclofenac using pseudo-emulsion hollow fiber membrane: Optimization by Box-Behnken response surface design

Diclofenac (DCF) is an anti-inflammatory drug detected in various aquatic sources and found harmful to aquatic life. In spite of adopting various treatment techniques, complete removal is not observed and remaining DCF can interact with other existing contaminants leading to the generation of more toxic contaminants in wastewater. In the present work, investigations on removal of DCF from aqueous solutions were carried out through Pseudo-emulsion hollow fiber strip dispersion (PEHFSD) technique with pseudo-emulsion consisting of Di-2-ethylhexyl phosphoric acid (D2EHPA) as carrier, n-heptane as diluent and HCl as stripping phase. Morphological features of hollow fiber membrane module were revealed through Field emission scanning electron microscopy which shows pore size and size distribution ranging from 95 nm to 120 nm. Energy-dispersive X-ray spectroscopy and Fourier transform infrared spectroscopy of membrane phase before and after extraction of DCF confirms interaction between D2EHPA and DCF. Contact angle measurement shows that due to hydrophobicity and strong capillary force, membrane phase retains within the micropores of the hollow fiber and helps to diffuse DCF forming DCF-D2EHPA complex. Uniform droplet sizes and size distribution of pseudo-emulsion provides high interfacial contact area and higher extraction efficiency. Studies on permeability and investigations regarding the effect of several operational parameters such as concentration of feed phase (F), carrier (C) and stripping phase (S) for the extraction of DCF as well as multivariable optimization were carried out by Response surface methodology using Box-Behnken design approach. 97.50% extraction of DCF was achieved with PEHFSD technique at optimum conditions.

Experimental Study on Material Removal Rate of Al6061 Machined with Magnetic Field Assisted Powder Mixed Electrical Discharge Machining

This paper describes the research work involved in experimental study on magnetic field-assisted powder mixed electrical discharge machining of Al6061 alloy. In this study, five process parameters namely, peak current, powder concentration, pulse on time, magnetic field and pulse off time are considered. Box-Behnken design approach based on response surface methodology was used for performing the experiments and for investigation of influence of process parameters on material removal rate (MRR). Based on analysis quadratic model was developed to predict the MRR. Optimization was performed using desirability function approach for determining the optimum set of process parameters for maximum MRR. The experimental validation was performed at optimum process parameters.

Investigation of erosion behaviour of an iron-mud filled glass-fibre epoxy hybrid composite

Iron-mining and ore processing (mainly laterite) are faced with the major challenge of generated overburden and topsoil aggregates (iron-mud) as a major solid waste. Storage and reclamation are two severe problems with harmful environmental concerns. The present paper investigates the possible uses of iron mine waste for developing a new-class hybrid polymer composite and its tribological characteristics. The polymer composites are fabricated through a hand-layup process by reinforcement of woven glass fibres in an epoxy polymer filled with different weight proportions of iron-mud. Erosion wear experiments were conducted on the fabricated composites according to a Box–Behnken design approach using an air jet-type erosion tester. The artificial neural network predicted values which exhibited close relationship with the experimental erosion values. Filler addition resulted in an improvement in the erosion resistance. A meta-heuristic approach like particle swarm optimization reveals the minimum erosion value at an optimal parametric combination. Finally, the morphology of eroded surfaces was critically examined by scanning electron microscopy and possible erosion mechanisms were presented.

Oil extraction from butter fruit (<i>Dacryodes edulis</i>) seeds and its optimization via response surface and artificial neural network

In this study, oil was extracted from butter fruit ( Dacrydes edulis ). To model and optimize the process conditions of oil extraction, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were used. Physicochemical analysis of the oil was carried out in order to determine the suitable of oil for industrial applications. Dacryodes edulis seeds were collected from Ikot Abasi Village in Eket Local Government of Akwa Ibom State, Nigeria. The seed was washed with clean water to remove dirt, and open with a sharp stainless knife to remove the seed from the pulp. The seeds were cut into small pieces and sundried for 5 days and were grinded into powder. Oil extraction from the powder seed was carried out using Sohxlet extraction method. The experiment was designed using Box-Behnken Design approach on three levels, three factors which generated 17 experimental runs. Independent factors considered were extraction time (X 1 ), solvent volume (X 2 ) and sample weight (X 3 ). The accuracy of the regression model obtained from the optimization software was determined using the co-efficient of determination (R 2 ). Results showed the highest oil yield of 17.878% (w/w) was obtained at solvent volume of 200 ml, sample weight of 50 g and extraction time of 55 min, respectively. However, response surface methodology predicted an oil yield of 17.826% (w/w), while the artificial neural network predicted 17.875% (w/w) at the same variables condition. The predicted values were validated in triplicate, and an average of 17.46% (w/w) and 17.72% (w/w) were obtained for RSM and ANN, respectively. The predicted values obtained were well within the range predicted. The coefficient of determination, which determines the model accuracy, was obtained to be 0.8454 for RSM and 0.8712 for ANN. Physicochemical analysis of the oil showed the oil is highly unsaturated with high saponification value and high iodine value. The study concluded that Dacryodes edulis seeds are found to be rich in oil and the oil can be applicable in industries as raw materials for products formation. Keywords: Dacryodes edulis seeds, response surface, artificial neural network, optimization, extraction, physicochemical properties

Application of activated carbon from banana stem waste for removal of heavy metal ions in greywater using a Box–Behnken design approach

ABSTRACT There is limited information on the optimal processes to remove heavy metals in greywater. A Response Surface Methodology (RSM) via the Box–Behnken Design (BBD) approach was applied in this study to investigate and optimise the process variables of activation time (1.5–2.5 h), impregnation ratio (0.25–0.75) and zinc chloride (ZnCl2) percentage (20–60%) for the removal of heavy metal ions (Cd, Cu, Pb and Ni) associated with greywater treatment. The quadratic model was chosen to describe the effects of the process variables (activation time, impregnation ratio, ZnCl2 percentage) on predicting the responses (heavy metal ions removal) with low p-values (<.0001), high-adjusted R 2 and predicted R 2 values. Second order polynomial equations, ANOVA and three-dimensional surface plots were developed to evaluate the effects of each independent process variable and determine the optimal condition of each factor for heavy metal ions removal. The optimal activation time for the activated carbon variables was 1.8 h, with 56% ZnCl2 and 0.60 impregnation ratio. This showed that the observed values for removing the heavy metal ions (Cu, Cd, Pb and Ni) were close to the predicted values. A RSM-based field test via the BBD approach, involving different types of greywater samples (bathtubs, showers, hand basins and laundry machines) showed that the percentages of heavy metal ions removal fit the experimental results.

Starch-coated green synthesized magnetite nanoparticles for removal of textile dye Optilan Blue from aqueous media

Starch-coated Fe3O4 magnetic nanoparticles were synthesized and tested as adsorbents for Optilan Blue dye removal from aqueous solutions. The four tested magnetite samples were prepared through an environmentally friendly synthetic route which uses water as solvent and biocompatible sodium bicarbonate as precipitating agent. In addition to the control sample, three of the Fe3O4 nanoparticle samples were synthesized using fruit waste extracts from avocado (Persea americana) and watermelon (Citrullus lanatus) as surfactants. The Box–Behnken design approach was applied in order to determine the optimum experimental conditions for dye removal from aqueous solutions, selecting as independent variables initial concentration of dye, pH, temperature and adsorbent dose. A significant removal of the dye was obtained with an initial concentration of 50 mg L−1 with 0.6 g L−1, pH 2, at 35°C. Three adsorption isotherms viz: the Langmuir, Freundlich and Temkin models were used to analyze the adsorption data, and kinetic and thermodynamic studies were also conducted.

Application of the response surface methodology to determine optimal conditions of castine calcination in a full-scale rotary kiln

ABSTRACT The optimisation of the calcination process in industrial lime kilns is of particular importance because it affects the final quality of the lime, coal consumption, and pollutant emissions. This work investigated the effects of different operating variables of a full-scale rotary kiln on the minimisation of unburned castine and reactivity of produced lime. The L8(27) Plackett-Burman's experimental plan was used for screening the significant variables on the calcination yield and the quality of the lime. Sequentially, Box-Behnken design approach, L15(33), under Response Surface Methodology (RSM) was used to determine the best combination of the variables optimising the calcination of castine. Quadratic models were developed to characterise the influence of selected significant variables, temperature of the smoke chamber (x 1 = 400, 500, and 550°C), kiln rotational speed (x 2 = 120, 90, and 70 s/revolution) and coal flow rate (x 4 = 0.5, 0.7, and 1.0 t/h), on the responses obtained. Under optimised conditions, the lime produced yielded 0.93% of unburned castine and a reactivity of 93.96°C, corresponding to the configuration: x 1 = 120, x 2 = 0.5, and x 3 = 550°C. Analysis of variance was conducted to assess the strength of evidence of the developed models. Good correlation coefficients were obtained for unburned castine (R 2 = 0.92, p < 0.031) and reactivity (R 2 = 0.96, p < 0.006), indicating that the models developed could be used to estimate castine calcination response.

Performance of non-catalytic thermal hydrolysis and wet oxidation for sewage sludge degradation under moderate operating conditions

Land disposal of waste activated sludge pose environmental risks due to the presence of heavy metals, pathogens and organic pollutants. Anaerobic digestion (AD) is one of the preferred treatment methods for sludge treatment. However, sludge hydrolysis is often found the rate-limiting step thereby reducing the biogas generation potential. Therefore, an effort was made to determine the optimum conditions for sludge solubilization by means of hydrothermal pretreatment. In this study, response surface methodology using Box Behnken design approach was used to optimize four hydrothermal reaction variables (i.e., initial pH, time, temperature, and oxidation coefficient) for sludge solubilization and total chemical oxygen demand (tCOD) reduction. Temperature and pH were found to be the most significant parameters. The maximum tCOD reduction and volatile suspended solids solubilization of 58% and 52%, respectively, were obtained at the following optimum conditions: temperature = 180 °C, time = 5 h, pH = 3.3 and oxidation coefficient = 0.5. Under similar conditions and alkaline pH, Maillard's reaction occurs which may have adverse impact on the performance of downstream AD process. The highest NH4+N and volatile fatty acids (VFAs) concentrations were detected in the treated sludge at the optimum conditions. The future studies should be aimed at the recovery of proteins, VFAs and biogas using appropriate methods.

Optimization of arsenic removal with Fe3O4@Al2O3@Zn-Fe LDH as a new magnetic nano adsorbent using Box-Behnken design

This work is focused on arsenic removal using Fe3O4@Al2O3@Zn-Fe LDH as a new magnetic nanoadsorbent based on ultrasound-assisted dispersive magnetic solid phase extraction. The determination of Arsenic content of samples was performed by Atomic Fluorescence Spectroscopy. The synthesized nanoadsorbent was characterized by means of Fourier-Transform Infrared Spectroscopy (FT-IR), Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDX), X-Ray Diffraction (XRD) and Brunauer–Emmett–Teller (BET). An effecient Box-Behnken Design (BBD) approach was used to optimize the variables affecting the arsenic removal. The predicted maximum removal percentage was 97.3, yielded at pH, 4.0; adsorbent amount, 28.9 mg; removal time, 12.8 min and initial concentration, 65.6 μg L−1. The adsorption isotherms were studied by plotting Langmuir and Freundlich models. It was found that the obtained empirical adsorption data were well fitted to the Langmuir equation with a maximum adsorption capacity of 67.57 mg g−1. Moreover, detailed kinetics studies confirmed that the adsorption kinetic follows a pseudo-second-order model. The prepared sorbent proved to be enormously efficient for arsenic removal from tap water sample, with the repeatability in the range of 4.8–7.8%. Finally, the proposed analysis method was found to be a promising monitoring methodology for arsenic removal in various tap and waste water samples.

Application of Box–Behnken Design Approach for Removal of Acid Black 26 from Aqueous Solution Using Zeolite: Modeling, Optimization, and Study of Interactive Variables

This research is focused on the adsorption of Acid Black 26 dye from aqueous solutions using zeolite as an adsorbent. The experiments were designed by response surface methodology. Quadratic model was used to predict the variables. Analysis of variance was used for investigation of variables and interaction between them. The highest removal percent (87.98%) was attained, and the optimum parameters are achieved: zeolite amount (0.55 g), dye concentration (25.78 mg L−1), pH = 4, and time (59.99 min). F value (329.44), P value (< 0.0001), the adequate precision (66.04), non-significant lack of fit, and R2 = 0.9970 demonstrate a good correlation between experimental and predicted data.

Using Box-Behnken design approach to investigate benzo[a]anthracene formation in smoked cattle meat samples and its' risk assessment.

In present study, a novel approach was selected for benzo[a]anthracene determination from smoked cattle meat samples using solid phase extraction under various experimental conditions. To optimize experimental variables, a three factor, three-level Box-Behnken experimental design combining with response surface methodology and quadratic programming were employed to prevent excessive amount of benzo[a]anthracene formation in smoked meat based on different experimental parameters. The influence of some important process parameters including cooking time (10-30min), fat ratio (5-25%) and distance to the cooking source (5-25cm), which significantly affected the formation efficiency of benzo[a]anthracene were optimized. The analysis of variance was conducted for specifying the interactions of independent variables. The independent and the dependent variables interactions were investigated. The quadratic regression model and the response surface contour plots were used to determine optimum values for the selected variables. The results of study revealed that optimum cooking time of smoked cattle meat was determined as 24.9min, fat ratio 7.9% and the distance to the cooking source 21.8cm. Under optimum conditions, minimum benzo[a]anthracene was formed in meat and its amount was determined using high performance liquid chromatography-mass spectrophotometer as qualitative and quantitative. The limit of detection and limit of quantitation values were calculated as 0.4µgkg-1 and 1.1µgkg-1, respectively.