Selectivity optimization experimental design

In 1997, D.L. Massart suggested the following definition "Chemometrics is a chemical discipline that uses mathematics, statistics and formal logic (a) to design or select optimal experimental procedures (b) to provide maximum relevant chemical information by analyzing chemical data and (c) to obtain knowledge about chemical systems" (Massart et ah, 1997). 

Among these methods, Generating Optimal Linear PLS Estimations (GOLPE) is a variable selection method for selecting by -> experimental design a limited number of - interaction energy values, aimed at obtaining the best predictive PLS models. 

A D-optimal experimental design was chosen in order to minimize covariance between preparation parameters for a preselected number of preparation parameters and number of catalysts [6]. The design was then modified to include 3x3 experimental points, which reflect the influence of one preparation parameter while the other parameters are kept constant. Hence, three curves were obtained for each preparation parameter with different combinations of the other parameters, where the combinations were selected to achieve design with the highest D-efficiency (calculations were done by Matlab software [7]). Set-point values for the ten selected experimental parameters are shown in Table 1. 90 catalysts out of 59049 were selected for the experimental study. 

D-optimal design Given a limited number of experiments, or samples to collect, the algorithm for D-optimal experimental design are used to select a subset of exper-iments/samples representing the overall variability of the candidate samples/experi-ments as accurately as possible. Usually the algorithms used are based on maximisation of the determinant of the variance-covariance matrix of the subset, hence the term D-optimal. 

The low yields of covalent reaction dictate that one focus experimentally on the probe molecules that actually label receptor components. But at the same time, to resolve the heterogeneity problem, one must determine the functional status of the pertinent probe molecules. Hence, the basic feature of the optimal experimental design involves selective observation of the ligand analog molecules, which act both as affinity labels and as receptor substrates. 

In the development of a SE-HPLC method the variables that may be manipulated and optimized are the column (matrix type, particle and pore size, and physical dimension), buffer system (type and ionic strength), pH, and solubility additives (e.g., organic solvents, detergents). Once a column and mobile phase system have been selected the system parameters of protein load (amount of material and volume) and flow rate should also be optimized. A beneficial approach to the development of a SE-HPLC method is to optimize the multiple variables by the use of statistical experimental design. Also, information about the physical and chemical properties such as pH or ionic strength, solubility, and especially conditions that promote aggregation can be applied to the development of a SE-HPLC assay. Typical problems encountered during the development of a SE-HPLC assay are protein insolubility and column stationary phase... 

The MUF resin formulation is built up from combination of certain amount of formalin, melamine and urea (in initial and post refluxing stages) and also sorbitol. Variation on the formulation gives different resin properties. The optimum resin properties give the optimum MUF resin formulation. From the properties analysis data, the optimum formulation is determined by using Mixture Experimental Design D-optimal criterion. The selective criteria... 

The first stage of any experimental design is the problem formulation, a basic step in which the objectives and thus the response variable to be optimized should be defined. After that, it is essential to identify all the factors that might have an influence on the selected responses, and for each factor, variability levels that take into account eventual constraints. 

Optimal design theory provides an alternative approach to the selection of an experimental design. For a description of the theory of optimal design see, for example, Atkinson and Donev [22]. 

The Taguchi method uses a particular experimental design, the goal of which is to select those settings of the design variables, which give optimal results for the performance of a product. More over, those settings of the noise factors are selected, that have minimal effects on the performance of the product. 

In order to optimize the product it becomes important to select that particular set of formula variations which generate the appropriate sensory profile. The technique of experimental design, consumer evaluation, and product modelling play a key role in discovering the proper formulation balance among ingredients to generate the necessary sensory profile. 

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