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Experiment Design (experiment + design)
Selected AbstractsOptimisation of hard pretzel productionINTERNATIONAL JOURNAL OF FOOD SCIENCE & TECHNOLOGY, Issue 3 2007Ni Yao Summary Absence of a thorough understanding of the baking conditions on hard pretzel quality has resulted in considerable loss of production and on productivity. Baking time, oven zone 1 temperature, drying time and kiln drying temperature were identified as important factors affecting pretzel quality in modern pretzel production, from a total of eleven factors following a screening experiment design. A central composite circumscribe design was then employed to optimise these four variables. The test for lack of fit was not significant (P < 0.05) for the responses, except for colour a* values. The analysis of variance of the responses indicated that models explained 99%, 91% and 87% variability for moisture content, ,Eab and pasting time, respectively. The four variables were optimised when pretzel moisture content, ,Eab and pasting time were considered simultaneously using desirability function approach. Validation experiment results revealed that the two most important qualities of pretzel, moisture content and ,Eab could be reliably predicted. [source] OPTIMAL CONDITIONS FOR THE GROWTH AND POLYSACCHARIDE PRODUCTION BY HYPSIZIGUS MARMOREUS IN SUBMERGED CULTUREJOURNAL OF FOOD PROCESSING AND PRESERVATION, Issue 4 2009PING WANG ABSTRACTS In submerged cultivation, many nutrient variables and environmental conditions have great influence on the growth and polysaccharide production by Hypsizigus marmoreus. Plackett,Burman design was used to determine the important nutrient factors. A central composite experimental design and surface response methodology were employed to optimize the factor levels. Prediction models for dry cell weight (DCW), polysaccharide outside cells (EPS) and polysaccharide inside cells (IPS) under important nutrient conditions were developed by multiple regression analysis and verified. By solving the equations, the optimal nutrient conditions for highest EPS production (9.62 g/L) were obtained at 6.77 g cornstarch/L, 36.57 g glucose/L, 3.5 g MgSO4/L and 6.14 g bean cake powder/L, under which DCW and IPS were 16.2 g/L and 1.46 g/L, close to the highest value under their corresponding optimal conditions. Optimal environmental conditions were obtained at 10% inoculation dose, 45 mL medium in a 250 mL flask, pH 6.5, 25C and 200 rpm according to the results of single-factor experiment design. PRACTICAL APPLICATIONS Hypsizigus marmoreus polysaccharides have many functional properties, including antitumor, antifungal and antiproliferative activities, and free-radical scavenging. Liquid cultivation could produce a higher yield of polysaccharides and more flexible sequential processing methods of H. marmoreus, compared with traditional solid-state cultivation. However, the cell growth and production of polysaccharides would be influenced by many factors, including nutrient conditions and environmental conditions in the liquid cultivation of H. marmoreus. Keeping the conditions at optimal levels can maximize the yield of polysaccharides. The study not only found out the optimal nutrient conditions and environmental conditions for highest cell growth and yield of polysaccharides, but also developed prediction models for these parameters with important nutrient variables. Yield of polysaccharide inside of cells was also studied as well as polysaccharides outside of cells and cell growth. The results provide essential information for production of H. marmoreus polysaccharides by liquid culture. [source] IDENTIFICATION OF IMPORTANT PRODUCTION VARIABLES AFFECTING HARD PRETZEL QUALITYJOURNAL OF FOOD QUALITY, Issue 3 2005N. YAO ABSTRACT The objective of this study was to determine the importance of raw material and processing variables that influence pretzel quality by utilizing a screening experiment design. Eleven variables were selected based on preliminary experiments, and a two-level-11-factor (211) fractional factorial experimental design was used to screen the variables. Several responses were measured for dough before and after extrusion, for half-baked and fully baked pretzels. These responses are important indicators of consistency and quality during pretzel processing. Results indicated that flour protein content, the amount of water added to make dough and dough mixing time were important variables influencing dough behavior. Caustic concentration affected brightness of half-baked pretzels but did not influence the color of the final product. Baking time was the most important factor for both half-baked product and final product qualities. The hardness of fully baked pretzels was influenced by baking time, temperature in baking oven zone 1, drying time and drying temperature. The color of final products was significantly influenced by baking time, while both baking time and drying temperature affected the moisture content of the final product. A key observation was that none of the raw material or dough processing parameters, within the range tested, influenced final pretzel quality as defined by pretzel moisture content, hardness or color. [source] A backoff strategy for model-based experiment design under parametric uncertaintyAICHE JOURNAL, Issue 8 2010Federico Galvanin Abstract Model-based experiment design techniques are an effective tool for the rapid development and assessment of dynamic deterministic models, yielding the most informative process data to be used for the estimation of the process model parameters. A particular advantage of the model-based approach is that it permits the definition of a set of constraints on the experiment design variables and on the predicted responses. However, uncertainty in the model parameters can lead the constrained design procedure to predict experiments that turn out to be, in practice, suboptimal, thus decreasing the effectiveness of the experiment design session. Additionally, in the presence of parametric mismatch, the feasibility constraints may well turn out to be violated when that optimally designed experiment is performed, leading in the best case to less informative data sets or, in the worst case, to an infeasible or unsafe experiment. In this article, a general methodology is proposed to formulate and solve the experiment design problem by explicitly taking into account the presence of parametric uncertainty, so as to ensure both feasibility and optimality of the planned experiment. A prediction of the system responses for the given parameter distribution is used to evaluate and update suitable backoffs from the nominal constraints, which are used in the design session to keep the system within a feasible region with specified probability. This approach is particularly useful when designing optimal experiments starting from limited preliminary knowledge of the parameter set, with great improvement in terms of design efficiency and flexibility of the overall iterative model development scheme. The effectiveness of the proposed methodology is demonstrated and discussed by simulation through two illustrative case studies concerning the parameter identification of physiological models related to diabetes and cancer care. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source] Forecasting the Bayes factor of a future observationMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 3 2007Roberto Trotta ABSTRACT I present a new procedure to forecast the Bayes factor of a future observation by computing the predictive posterior odds distribution. This can assess the power of future experiments to answer model selection questions and the probability of the outcome, and can be helpful in the context of experiment design. As an illustration, I consider a central quantity for our understanding of the cosmological concordance model, namely, the scalar spectral index of primordial perturbations, nS. I show that the Planck satellite has over 90 per cent probability of gathering strong evidence against nS= 1, thus conclusively disproving a scale-invariant spectrum. This result is robust with respect to a wide range of choices for the prior on nS. [source] Hierarchical analysis of large-scale two-dimensional gel electrophoresis experimentsPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 10 2003Amit Rubinfeld Abstract Large-scale two-dimensional gel experiments have the potential to identify proteins that play an important role in elucidating cell mechanisms and in various stages of drug discovery. Such experiments, typically including hundreds or even thousands of related gels, are notoriously difficult to perform, and analysis of the gel images has until recently been virtually impossible. In this paper we describe a scalable computational model that permits the organization and analysis of a large gel collection. The model is implemented in Compugen's Z4000Ô system. Gels are organized in a hierarchical, multidimensional data structure that allow the user to view a large-scale experiment as a tree of numerous simpler experiments, and carry out the analysis one step at a time. Analyzed sets of gels form processing units that can be combined into higher level units in an iterative framework. The different conditions at the core of the experiment design, termed the dimensions of the experiment, are transformed from a multidimensional structure to a single hierarchy. The higher level comparison is performed with the aid of a synthetic "adaptor" gel image, called a Raw Master Gel (RMG). The RMG allows the inclusion of data from an entire set of gels to be presented as a gel image, thereby enabling the iterative process. Our model includes a flexible experimental design approach that allows the researcher to choose the condition to be analyzed a posteriori. It also enables data reuse, the performing of several different analysis designs on the same experimental data. The stability and reproducibility of a protein can be analyzed by tracking it up or down the hierarchical dimensions of the experiment. [source] A Genetic Algorithm Hybrid for Constructing Optimal Response Surface DesignsQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 7 2004David Drain Abstract Hybrid heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective, and simple heuristics like simulated annealing fail to find good solutions. One such heuristic hybrid is GASA (genetic algorithm,simulated annealing), developed to take advantage of the exploratory power of the genetic algorithm, while utilizing the local optimum exploitive properties of simulated annealing. The successful application of this method is demonstrated in a difficult design problem with multiple optimization criteria in an irregularly shaped design region. Copyright © 2004 John Wiley & Sons, Ltd. [source] Choice experiment adaptive design benefits: a case study,AUSTRALIAN JOURNAL OF AGRICULTURAL & RESOURCE ECONOMICS, Issue 4 2010Geoffrey N. Kerr Efficient experimental designs offer the potential to reduce required sample sizes, or to reduce confidence intervals for parameters of interest, in choice experiments. Choice experiment designs have typically addressed efficiency of utility function parameter estimates. The recently developed concept of C -efficiency recognises the salience of willingness to pay estimates rather than utility function parameters in studies that seek to put money values on attributes. C -efficiency design benefits have been illustrated in a theoretical context, but have not been tested in applied settings. This study reports a choice experiment field application that used initial responses to update statistical designs to maximise C -efficiency. Consistent with theoretical predictions, the revised design delivered significant reductions in the variance of willingness to pay estimates, illustrating that C -efficient designs can indeed decrease costs of choice experiments by reducing required sample sizes. [source] | ||||||||||