Showing papers on "Factorial experiment published in 2012"

BOX- BEHNKEN experimental design in factorial experiments: The importance of bread for nutrition and health

Abstract: BACKROUND: In this study, a BOX-BEHNKEN design (BBD) of response surface methodology was used to investigate the effects of the amount of bran, the amount of yeast and the fermentation time on the amount of phytic acid in bread. The goal of first order factorial experiment is to identify the optimum levels of independent variables for the dependent variable. In this study, the implementation of first order response surface model and interpretation of the results were based on a 3 k Box- Benhnken (BBD) experimental design with one replicate. RESULTS: The calculation of the data for the first-order response surface model revealed that R 2 was 99,5% and that the model described most of the variance in the dependent variable (phytic acid). CONCLUSION: According to the results of the Box-Behnken experimental design (BBD), it was found that the amount of bran and the fermentation time had highest effect on phytic acid and that the amount of yeast, either alone or in any interaction, had no effect on the amount of phytic acid. Thus, it was concluded that optimal use of the amount of bran and fermentation time in the production of high-quality bread could prevent several diseases in future.

Quality by design approach in the optimization of the spray-drying process

Abstract: Context/objective: The aim of this study was to illustrate the influence of the processing parameters, inlet temperature, atomization air flow rate and feed flow rate, on critical quality attributes of spray-dried powders using design of experiments (DoE).Methods: Spray-dried powders were characterized by laser diffraction, X-ray powder diffraction (XRPD) and near-infrared spectroscopy (NIR). Multivariate analysis of two different experimental designs was performed to elucidate the optimal process conditions.Results and discussion: XRPD revealed that the spray-dried powders consisted of crystalline β-mannitol and amorphous trehalose. Non-invasive NIR measurement was successfully used for correlating the critical quality attribute particle size with size determined by laser diffraction. The full factorial design proved to be unsuitable due to the non-linear influence of factors. The composite face-centered design improved the quality of the models and showed both linear and non-linear influence of the para...

1 Experimental Design

Abstract: The chapter discusses the three principles of good experimental design-random assignment, replication, and local control-that were championed by Ronald A. Fisher. The chapter also shows how complex experimental designs are constructed from and understood in terms of three simple building block designs: the completely randomized design, randomized block design, and Latin square design. Missing observations and missing cells are common in the behavioral sciences and education. Procedures for dealing with unequal cell n's in randomized block designs and completely randomized factorial designs are illustrated using a regression model and a cell means model. The merits of the two models and the classical model are examined. Researchers sometimes use confounding to reduce the size of blocks or to reduce the number of treatment combinations in an experiment. The advantages and disadvantages of group-treatment confounding in split-plot factorial designs, group-interaction confounding in confounded factorial designs, and treatment-interaction confounding in fractional designs are examined. The chapter discusses a variety of analysis of covariance designs. These designs enable a researcher to (a) remove that portion of the dependent-variable error variance that is predictable from a knowledge of the concomitant variable thereby increasing power and (b) reduce bias by adjusting the dependent variable so that it is free of effects attributable to the concomitant variable. Keywords: experimental design; analysis of variance; design of experiments; cell means model

Simultaneous analysis of irbesartan and hydrochlorothiazide: an improved HPLC method with the aid of a chemometric protocol.

Abstract: Experimental design method was used for HPLC determination of irbesartan and hydrochlorothiazide in combined dosage forms. The traditional approach for optimization of experiments is time-consuming, involves a large number of runs and does not allow establishing the multiple interacting parameters. The main advantages of the experimental design method include the simultaneous screening of a larger number of factors affecting response and the estimation of possible interactions. On the basis of preliminary experiments, three factors-independent variables were selected as inputs (methanol content, pH of the mobile phase and temperature) and as dependent variables, five responses (resolution, symmetry of irbesartan peak, symmetry of hydrochlorothiazide peak, retention factor of irbesartan and retention factor of hydrochlorothiazide) were chosen. A full 23 factorial design, where factors were examined at two different levels ("low" and "high") was used to determine which factors had an effect on the studied response. Afterwards, experimental design was used to optimize these influent parameters in the previously selected experimental domain. The novelty of our method lies in the optimization step accomplished by Derringer's desirability function. After optimizing the experimental conditions a separation was conducted on a Supelcosil C(18) (150 mm × 4.6 mm, 5 mm particle size) column with a mobile phase consisting of methanol-tetrahydrofuran-acetate buffer 47:10:43 v/v/v, pH 6.5 and a column temperature of 25 °C. The developed method was successfully applied to the simultaneous separation of these drug-active compounds in their commercial pharmaceutical dosage forms.

Optimization of operating parameters using response surface methodology for adsorption of crystal violet by activated carbon prepared from mango kernel

Abstract: An adsorbent prepared from mango seed was used to study its sorption potential on removing Crystal Violet dye from aqueous solution. The influences of operating parameters like pH, temperature, initial concentration and adsorbent dosage on dye adsorption were studied. A maximum dye -1 removal of 95% was achieved with an initial concentration of 10 mg L . The percentage removal was mathematically described as a function of experimental parameters and was modeled through response surface methodology. The results show that the responses of color removal in adsorption of dyes were significantly affected by the synergistic effect of linear term of time and dosage and the 4 quadratic term of temperature and time. A 2 full factorial design of experiments was adopted and statistical analysis was performed in the form of the analysis of variance and Student s t-test, which gave good interpretation in terms of interaction of experimental parameters. The interaction of the parameters temperature, pH, dye concentration and adsorbent dosage on Crystal Violet removal is also highly significant with confidential level > 99%.

Influence of temperature, light and nutrients on the growth rates of the macroalga Gracilaria domingensis in synthetic seawater using experimental design

Abstract: In the present study, the daily relative growth rates (DRGR, in percent per day) of the red macroalga Gracilaria domingensis in synthetic seawater was investigated for the combined influence of five factors, i.e., light (L), temperature (T), nitrate (N), phosphate (P), and molybdate (M), using a statistical design method. The ranges of the experimental cultivation conditions were T, 18–26°C; L, 74–162 μmol photons m−2 s−1; N, 40–80 μmol L−1; P, 8–16 μmol L−1; and M, 1–5 nmol L−1. The optimal conditions, which resulted in a maximum growth rate of ≥6.4% d−1 from 7 to 10 days of cultivation, were determined by analysis of variance (ANOVA) multivariate factorial analysis (with a 25 full factorial design) to be L, 74 μmol photons m−2 s−1; T, 26°C; N, 80 μmol L−1; P, 8 μmol L−1; and M, 1 nmol L−1. In additional, these growth rate values are close to the growth rate values in natural medium (von Stosch medium), i.e., 6.5–7.0% d−1. The results analyzed by the ANOVA indicate that the factors N and T are highly significant linear terms, X L, (α = 0.05). On the other hand, the only significant quadratic term (X Q) was that for L. Statistically significant interactions between two different factors were found between T vs. L and N vs. T. Finally, a two-way (linear/quadratic interaction) model provided a quite reasonable correlation between the experimental and predicted DRGR values (R adjusted 2 = 0.9540).

Optimal Designs for Two-Level Factorial Experiments with Binary Response

Abstract: We consider the problem of obtaining locally D-optimal designs for factorial experiments with qualitative factors at two levels each and with binary response. For the 2 factorial experiment with main-effects model, we obtain optimal designs analytically in special cases and demonstrate how to obtain a solution in the general case using cylindrical algebraic decomposition. The optimal designs are shown to be robust to the choice of the assumed values of the prior, and when there is no basis to make an informed choice of the assumed values we recommend the use of the uniform design that assigns equal number of observations to each of the four points. For the general 2 case we show that the uniform design has a maximin property.

Polymeric nanoparticles loaded with the 3,5,3'-triiodothyroacetic acid (Triac), a thyroid hormone: factorial design, characterization, and release kinetics.

Abstract: This present investigation deals with the development and optimization of polymeric nanoparticle systems loaded with 3,5,3′-triiodothyroacetic acid (Triac). A 211–6 fractional factorial design and another 22 factorial design were used to study the contrasts on particle size distribution, morphology, surface charge, drug content, entrapment efficiency, and in vitro drug release profiles. The independent variables were the concentration of Triac, type and quantity of both polymer and oil, quantity of Span™ 60 and Tween® 80, volume of solvent and water, and velocity of both magnetic stirring and the transfer of the organic phase into the aqueous solution. The results of optimized formulations showed a narrow size distribution with a polydispersity index lower than 0.200. The particle sizes were on average 159.6 nm and 285.6 nm for nanospheres and nanocapsules, respectively. The zeta potential was higher than 20 mV (in module) and the entrapment efficiency was nearly 100%. A high-performance liquid chromatography method was developed, validated, and efficiently applied to Triac quantification in colloidal suspension. The main independent variables were the type and quantity of the polymer and oil. In vitro drug release profile depicted several features to sustain Triac release. Different formulations showed various release rates indicating an interaction between Triac and other formulation compounds such as polymer and/or oil quantity. Two different models were identified (biexponential and monoexponential) that allowed the control of both the release rate and Triac concentration. Thus, the prepared nanoparticles described here may be of clinical importance in delivering Triac for thyroid treatment.

Design and optimization of a dividing wall column by factorial design

Abstract: A factorial design methodology was applied to the design of a dividing wall column, solving the complex multivariable problems and simultaneously optimizing the interacting variables to achieve the best design with respect to total annual cost. Column structure was practically optimized with a minimum of simulation runs. The proposed design method was tested in the design and optimization of an NGL recovery system; it allowed interactions between variables to be identified and quantified. The column system designed by the proposed method reduced reboiler energy consumption and total annual cost by 28.23% and 25.49%, respectively, in case 1, and those by 25.63% and 18.85%, respectively, over conventional distillation in case 2.

Full factorial design analysis of carbon nanotube polymer-cement composites

Abstract: The work described in this paper is related to the effect of adding carbon nanotubes (CNT) on the mechanical properties of polymer-cement composites. A full factorial design has been performed on 160 samples to identify the contribution provided by the following factors: polymeric phase addition, CNT weight addition and water/cement ratio. The response parameters of the full factorial design were the bulk density, apparent porosity, compressive strength and elastic modulus of the polymer-cement-based nanocomposites. All the factors considered in this analysis affected significantly the bulk density and apparent porosity of the composites. The compressive strength and elastic modulus were affected primarily by the cross-interactions between polymeric phase and CNT additions, and the water/cement ratio with polymeric phase factors.

Optimization of LDL targeted nanostructured lipid carriers of 5-FU by a full factorial design.

Abstract: Background: Nanostructured lipid carriers (NLC) are a mixture of solid and liquid lipids or oils as colloidal carrier systems that lead to an imperfect matrix structure with high ability for loading water soluble drugs. The aim of this study was to find the best proportion of liquid and solid lipids of different types for optimization of the production of LDL targeted NLCs used in carrying 5-Fu by the emulsification-solvent evaporation method. Materials and Methods: The influence of the lipid type, cholesterol or cholesteryl stearate for targeting LDL receptors, oil type (oleic acid or octanol), lipid and oil% on particle size, surface charge, drug loading efficiency, and drug released percent from the NLCs were studied by a full factorial design. Results: The NLCs prepared by 54.5% cholesterol and 25% of oleic acid, showed optimum results with particle size of 105.8 nm, relatively high zeta potential of −25 mV, drug loading efficiency of 38% and release efficiency of about 40%. Scanning electron microscopy of nanoparticles confirmed the results of dynamic light scattering method used in measuring the particle size of NLCs. Conclusions: The optimization method by a full factorial statistical design is a useful optimization method for production of nanostructured lipid carriers.

Real-time monitoring of photocytotoxicity in nanoparticles-based photodynamic therapy: a model-based approach

Abstract: Nanoparticles are widely suggested as targeted drug-delivery systems. In photodynamic therapy (PDT), the use of multifunctional nanoparticles as photoactivatable drug carriers is a promising approach for improving treatment efficiency and selectivity. However, the conventional cytotoxicity assays are not well adapted to characterize nanoparticles cytotoxic effects and to discriminate early and late cell responses. In this work, we evaluated a real-time label-free cell analysis system as a tool to investigate in vitro cyto- and photocyto-toxicity of nanoparticles-based photosensitizers compared with classical metabolic assays. To do so, we introduced a dynamic approach based on real-time cell impedance monitoring and a mathematical model-based analysis to characterize the measured dynamic cell response. Analysis of real-time cell responses requires indeed new modeling approaches able to describe suited use of dynamic models. In a first step, a multivariate analysis of variance associated with a canonical analysis of the obtained normalized cell index (NCI) values allowed us to identify different relevant time periods following nanoparticles exposure. After light irradiation, we evidenced discriminant profiles of cell index (CI) kinetics in a concentration- and light dose-dependent manner. In a second step, we proposed a full factorial design of experiments associated with a mixed effect kinetic model of the CI time responses. The estimated model parameters led to a new characterization of the dynamic cell responses such as the magnitude and the time constant of the transient phase in response to the photo-induced dynamic effects. These parameters allowed us to characterize totally the in vitro photodynamic response according to nanoparticle-grafted photosensitizer concentration and light dose. They also let us estimate the strength of the synergic photodynamic effect. This dynamic approach based on statistical modeling furnishes new insights for in vitro characterization of nanoparticles-mediated effects on cell proliferation with or without light irradiation.

Efficient designs: factorial randomized trials.

Abstract: As an alternative to conducting multiple parallel group randomized controlled trials, the factorial design is an efficient means of evaluating multiple interventions. This review highlights when it is appropriate to conduct a factorial trial, considers the structure and nomenclature of factorial designs, and discusses various methodological considerations, including sample size calculation, the role of statistical interactions between different interventions, and statistical power. Several pertinent examples in orthopaedics and medicine are used to highlight the key concepts.

Application of Taguchi and Full Factorial Experimental Design to Model the Color Yield of Cotton Fabric Dyed with Six Selected Direct Dyes

Abstract: This paper describes the modeling of the color yield (Fk) of 100% cotton fabric dyed with six selected direct dyes (two from each groups of A, B and C) using Taguchi and factorial experimental designs as well as a response surface regression method. The factors chosen were dye concentration, electrolyte (sodium chloride) concentration, temperature and time of dying. To conduct the tests using the Taguchi approach, two levels were chosen for each factor. After obtaining the data (Fk), the significant factors were determined by an analysis of variance (ANOVA). Then, the level of significant factors was increased from two to three and the supplementary tests were carried out using full factorial design. ANOVA was applied again and, finally, the initial response surface regression model was produced considering the significant factors. After verifying the validity of the initial models, the BOX-COX transformation was implemented until the models achieved validity.

Modeling and Analysis of Resin Bonded Sand Mould System Using Design of Experiments and Central Composite Design

Abstract: Abstract In this paper an attempt has been made for linear and non linear modeling of resin bonded sand mould system using full factorial design of experiments and response surface methodology, respectively. It is important to note that the quality of castings produced using the resin bonded sand mould system depends largely on properties of moulds, which are influenced by the characteristics of sand, like type of sand, grain fineness number, grain size distribution and quantity and type of resin, catalyst, curing time etc. In the present study, percentage of resin, percentage of hardener, number of strokes and curing time are considered as input parameters and the mould properties, such as compression strength, shear strength, tensile strength and permeability are treated as responses. In the present work, phenol formaldehyde is used as the resin whereas tetrahydrophthalic anhydride as the hardener. A two level full factorial and three level central composite designs are utilized to develop input-output relationships. Surface plots and main effects plots are used to study the effects of amount of resign, amount of hardener, number of strokes and curing time on the responses, namely, compression strength, tensile strength, shear strength and permeability. Moreover, the adequacies of the developed models have been tested using analysis of variance. The prediction accuracy of the developed models have been tested with the help of twenty test cases and found reasonably good accuracy.

Enhancing Biodegradation of Dibenzothiophene by Bacillus sphaericus HN1 Using Factorial Design and Response Surface Optimization of Medium Components

Abstract: Based on four levels of full factorial design, a statistical design of experiments is used to investigate two cases of dibenzothiophene biodegradation in batch processes using Bacillus sphaericus HN1, involving as factors, yeast extract and dimethylsulfoxide or magnesium sulfate for first and second cases, respectively. Predictive models have been correlated finding out how significant the effects of these variables (factors) and their interactions are in practice. Also, response surface methodology has been applied to visualize the effect of the studied factors and LINGO software was used to find out the optimum values of the variables for enhancing the process.

A comprehensive full factorial LC-MS/MS proteomics benchmark data set

Abstract: An important prerequisite for the development and benchmarking of novel analysis methods is a well-designed comprehensive LC-MS/MS data set. Here, we present our data set consisting of 59 LC-MS/MS analyses of 50 protein samples extracted individually from Escherichia coli K12 and spiked with different concentrations of bovine carbonic anhydrase II and/or chicken ovalbumin, according to a 2 × 3 full factorial design. Using the well-annotated and commonly used E. coli proteome as the sample background ensures that the complexity of the data is on a par with most current proteomic analyses. Data were acquired over a 2-month period using multiple reversed-phase columns and instrument calibrations to include real-life challenges faced when analyzing large proteomics data sets. Moreover, so-called “ground truth” data, comprised by LC-MS/MS measurements of the pure spikes are included in the data set. The current manuscript elaborates this comprehensive benchmark data set for future development and evaluation of analysis methods and software.