Clusterization in D-optimal Designs: The Case Against Linearization

Optimal mixture designs for four components in two orthogonal blocks

D-optimal minimax regression designs on discrete design space

D-optimal experimental designs for Freundlich and Langmuir adsorption isotherms

A new multiresponse optimization approach in combination with a D-Optimal experimental design for the determination of biogenic amines in fish by HPLC-FLD

Analysis of interferents by means a D-optimal screening design and calibration using partial least squares regression in the spectrophotometric determination of Cr(VI)

Paracetamol removal by photo-Fenton processes at near-neutral pH using a solar simulator: Optimization by D-optimal experimental design and toxicity evaluation

In this paper, a few experimental points were used to calibrate the throttle model, fuel injection model and semi-predictive combustion model of the engine system based on the D-optimal experimental design method. The results show that the number of calibration test points can be reduced by more than 90% on the premise of ensuring the accuracy of each sub-model. At the same time, a set of general calibration experiment design methods for each sub-model in the engine system is established by comparing the distribution of experimental points selected from each sub-model. Keywords: engine modelling, D-optimal, DOE, model calibration

For effective response surface modeling during sequential approximate optimization (SAO), thenormalized and the augmented D-optimality criteria are presented. Thenormalized D-optimality criterion uses the normalized Fisher information matrix by its diagonal terms in order to obtain a balance among the linear-order and higher-order terms. Then, it is augmented to directly include other experimental designs or the pre-sampled designs. This augmentation enables the trust region managed sequential approximate optimization to directly use the pre-sampled designs in the overlapped trust regions in constructing the new response surface models. In order to show the effectiveness of the normalized and theaugmented D-optimality criteria, following two comparisons are performed. First, the information surface of thenormalized D-optimal design is compared with those of the original D-optimal design. Second, a trust-region managed sequential approximate optimizer having three D-optimal designs is developed and three design problems are solved. These comparisons show that thenormalized D-optimal design gives more rotatable designs than the original D-optimal design, and theaugmented D-optimal design can reduce the number of analyses by 30 % –40 % than the original D-optimal design.

In this paper a numerical method for construction of optimal Bayesian block designs of size two is considered. The main focus is on implementing prior information on the unknown error variance and variance of random block effects to calculate the A- and D-optimal designs. It is noted from the numerical results that the A- and D-optimal Bayesian block designs are insensitive to the shape of the prior distributions.

Solid-state production of polygalacturonase by Aspergillus sojae ATCC 20235

This study is mainly undertaken to design and optimize an effervescent tablet formulation of Josapine pineapple by using the D-optimal experimental design methodology. Josapine pineapple powder, citric acid, sodium carbonate and stevia were used in the formulations as independent variables. Tablets were prepared by the direct compression method and evaluated for their disintegration time and sensory properties which were regarded as responses in a D-optimal design. Formulation V3 was selected as the optimum formulation with pineapple powder, citric acid, sodium carbonate and stevia at 49.59, 20.00, 11.96 and 18.45%, respectively. In addition, V3 has a very fast disintegration time and quite high overall acceptability which represents the consumer approval. The observed values of the responses obtained from the optimized formulation were very close to the predicted values where the euclidean distance calculated for V3 was equal to 0.26. In conclusion, this study reveals that the effervescent pineapple tablet has a wide potential for future development and can be enhanced for commercialization.

Injera, a fermented staple food traditionally made from Teff flour, is a key part of the Ethiopian diet, with nearly all Ethiopians eating it at least once daily. Compositing various grain-based, and oilseed flours with Teff flour improves the nutritional content, and cost-effectiveness of the produced Injera. In Ethiopia, very limited studies were conducted to investigate the use of sorghum, rice, and teff flour blends combined with flaxseed flour in Injera preparation. Thus, this study was conducted to evaluate the anti-nutritional factors, microbiological characteristics, and ideal blending proportions of sorghum, rice, teff, and flaxseed flours to create a superior quality Injera via D-optimal design. One-way and two-way ANOVA, alongside Tukey’s pairwise comparison method, was employed to assess mean differences. The aim was to enhance the nutritional quality of Injera compared to a control sample made from 100% sorghum flour. The optimal nutritional profile was achieved with a blend comprising 50% sorghum, 20% rice, 30% teff, and supplemented with 3% flaxseed flour. This blend resulted in Injera containing 3.02% fiber, 9.30% protein, 83.16% carbohydrate, 393.24 kcal/100 g energy, 13.99 mg/100 g iron, 4.06 mg/100 g calcium, and 3.78 mg/100 g zinc. The optimization process identified this blend as optimal for improving the nutritional quality of Injera. The findings underscore that blending teff with sorghum, rice, and flaxseed flour enhances the nutritive value of Injera.

Under study is the problem of a D-optimal experimental design for the problem of nonparametric kernel smoothing. Modification is proposed for the process of calculating the Fisher information matrix. D-optimal designs are constructed for one and several target points for the problems of nonparametric kernel smoothing using a uniform kernel, the Gauss and Epanechnikov kernels. Comparison is performed between Fedorov’s algorithm and direct optimization methods (such as the Nelder–Mead method and the method of differential evolution). The features of the application of the optimality criterion for the experimental design of the problems with several target points were specified for the cases of various kernels and bandwidths.

A- and D-optimal designs are investigated for a log contrast model suggested by Aitchison & Bacon-Shone for experiments with mixtures. It is proved that when the number of mixture components q is an even integer, A- and D-optimal designs are identical; and when q is an odd integer, A- and D-optimal designs are different, but they share some common support points and are very close to each other in efficiency. Optimal designs with a minimum number of support points are also constructed for 3, 4, 5 and 6 mixture components.

Factorial surveys use a population of vignettes to elicit respondents’ attitudes or beliefs about different hypothetical scenarios. However, the vignette population is frequently too large to be assessed by each respondent. Experimental designs such as randomized block confounded factorial (RBCF) designs, D-optimal designs, or random sampling designs can be used to construct small subsets of vignettes. In a simulation study, we compare the three vignette designs with respect to their biases in effect estimates and show how the biases arise from the designs’ confounding structure, nonorthogonality, and unbalancedness. We particularly focus on the designs’ sensitivity to context effects and misspecifications of the analytic model. We argue that RBCF designs and D-optimal designs are preferable to random sampling designs because they offer a stronger protection against undesirable confounding, context effects, and model misspecifications. We also discuss strategies for dealing with context and order effects since none of the basic vignette designs can satisfactorily handle them.