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The application of Design of Experiments (DoE) significantly accelerates the optimization of reaction conditions for previously unreported transformations. DoE achieves this by focusing experimental efforts, enabling efficient exploration of the so‐called experimental space. In this study, two DoE paradigms are used to determine the optimal parameters for converting the model substrate, N‐methylindole, into its C‐3 phenylselenylated analogue. The DoE approaches include the widely recognized face‐centered central composite design and the less familiar D‐optimal design, implemented using the freeware Chemometric Agile Tool (CAT) for experimental plan development and data analysis. A comparison of the two designs reveals that the D‐optimal design, requiring 25% fewer reactions, provided equivalent results in terms of experimental space exploration and information retrieved. This highlights its efficiency and potential advantages. One aim of this report is to raise awareness within the organic chemistry community about the capabilities of the D‐optimal design. Reaction kinetics are studied using an online FlowNMR device, which allows real‐time reaction monitoring without the need for stopping the reaction or performing work‐up procedures that might introduce errors or yield misleading results. Finally, the scope of the reaction is briefly explored.

The production of trans‐fats and chemical changes during the process of frying are serious public health concerns and must be monitored efficiently. For this purpose, the canola oil was formulated with different ratio of extra virgin olive oil and palm olein using D‐optimal mixture design, and the best formulation (67:22:11) based on free fatty acid (FFA) content, peroxide value (PV), and iodine value (IV) as responses was selected for multiple frying process. The data on FFA, PV, and IV along with Fourier transform‐infrared (FT‐IR) spectra were taken after each frying up to ten frying. The spectral data were preprocessed with standard normal variate followed by principal component analysis which is clearly showing the differentiation for various frying. Similarly, partial least square regression was applied to predict the FFA (0.37%–1.63%), PV (4.47–13.85 meqO2/kg), and IV (111.51–51.39 I2/100 g) which demonstrated high coefficient of determination (R2) 0.84, 0.83, and 0.81, respectively. It can be summarized that FT‐IR can be used as a novel tool for fast and noninvasive quality determination of frying oils. The current study was designed to make canola oil blends with different concentrations of olive oil and palm olein. These blends were optimized by D‐optimal Design using the oil stability parameters such as free fatty acid (FFA), peroxide (PV), and iodine values (IV). The optimized canola oil blend was further used for multiple frying, and FT‐IR spectral fingerprints were taken which is considered to be a rapid, non‐destructive, and environment‐friendly analytical tool for characterization of the chemical changes taken place due to the processing.

Dose–response modeling in areas such as toxicology is often conducted using a parametric approach. While estimation of parameters is usually one of the goals, often the main aim of the study is the estimation of quantities derived from the parameters, such as the ED50 dose. From the view of statistical optimal design theory such an objective corresponds to a c-optimal design criterion. Unfortunately, c-optimal designs often create practical problems, and furthermore commonly do not allow actual estimation of the parameters. It is therefore useful to consider alternative designs which show good c-performanee, while still being applicable in practice and allowing reasonably good general parameter estimation. In effect, using optimal design terminology this means that a reasonable performance regarding the D-criterion is expected as well. In this article, we propose several approaches to the task of combining c- and D-efficient designs, such as using mixed information functions or setting minimum requirements regarding either c- or D-efficiency, and show how to algorithmically determine optimal designs in each case. We apply all approaches to a standard situation from toxicology, and obtain a much better balance between c- and D-per for manee. Next, we investigate how to adapt the designs to different parameter values. Finally, we show that the methodology used here is not just limited to the combination of c- and D-designs, but can also be used to handle more general constraint situations such as limits on the cost of an experiment.

We consider the problem of designing additional experiments to update statistical models for latent day specific effects. The problem appears in thermal spraying, where particles are sprayed on surfaces to obtain a coating. The relationships between in-flight properties of the particles and the controllable variables are modelled by generalized linear models. However, there are also non-controllable variables, which may vary from day to day and are modelled by day-specific additive effects. Existing generalized linear models for properties of the particles in flight must be updated on a limited number of additional experiments on a different day. We develop robust D-optimal designs to collect additional data for an update of the day effects, which are efficient for the estimation of the parameters in all models under consideration. The results are applied to the thermal spraying process and a comparison of the statistical analysis based on a reference design as well as on a selected Bayesian D-optimal design is performed.

An intelligent cooling system directly influences the thermal load of high‐temperature components, heat distribution, and fuel economy of a diesel engine. An optimal coolant pump rotational speed map is a key factor in intelligent cooling control strategies. In this study, we designed an experimental variable coolant flow system for a maritime diesel engine. Experiment design and D‐optimal designs were used to optimize the parameters of the diesel engine cooling system. The diesel engine speed, load, and freshwater rotational pump speed were selected as variables. The temperature of the high‐thermal‐load zone of the combustion chamber components, fuel consumption rate, effective power, and peak cylinder pressure were selected as response variables, and the D‐optimal method was used to sample the experimental points. Polynomial response surface models were obtained using a stepwise algorithm. A multiobjective optimization problem was converted into a simple‐objective optimization problem using the ideal point method. A genetic algorithm was used to optimize the single‐objective function globally to obtain the optimal freshwater pump speed map for a diesel engine under all conditions. On average, the optimized cooling system decreased the fuel consumption by 1.901%. Six typical propulsive conditions were selected to confirm the validity of the optimization results. The experimental results indicate that the fuel consumption decreased by 2.35%, the effective power increased by 2.26%, and the power consumption of the water pump decreased by 17.83%. The combination of experiment design and D‐optimal designs offers the advantages of low cost, high efficiency, and high precision in solving multiobjective optimization problems involving strong coupling and nonlinear systems. The results of this research provide data support and a theoretical basis for intelligent cooling control strategies. A multiobjective optimization problem was converted into a simple‐objective optimisation problem using the ideal point method. A genetic algorithm was used to obtain the optimal freshwater pump speed map for a diesel engine under all conditions. The combination of experiment design and D‐optimal designs offers the advantages of low cost, high efficiency, and high precision in solving multiobjective optimization problems involving strong coupling and nonlinear systems.

ABSTRACT This study investigates the optimization of four gluten free flours namely sorghum, rice, teff flours, and 3% flaxseed flour blends to enhance the quality injera, which was traditionally baked with only pure teff. Utilizing a D‐optimal mixture design, ratios were varied (sorghum 43%–50%, rice 20%–27%, teff 23%–30%). Methods followed AOAC and AACC standards, analyzed using Minitab 19.2 software with one‐way and two‐way ANOVA. Results show flaxseed supplementation significantly improves sorghum‐based injera's texture and sensory attributes, approaching teff injera quality. Hedonic ratings (color, rollability, softness, taste, eye distribution, mouthfeel, and overall acceptability) were favorable. Physical texture remained stable during storage, with variable titratable acidity among blends. This research supports integrating flaxseed flour in grain blends to enhance injera's nutritional and sensory qualities, proposing applications in both household and industrial settings. The purpose of this study is to investigate the supplementation of flaxseed flour in a blend of sorghum, rice, and teff flours, for its performance to improve the overall acceptability, and sensory appeals of the produced injera. This study finds that adding 3% flaxseed flour to blends of sorghum, rice, and teff enhances the texture and sensory qualities of injera, making it comparable to traditional teff injera. The D‐optimal mixture design yielded favorable ratings in color, taste, and overall acceptability, while maintaining stable texture during storage. These results support the integration of flaxseed flour to improve injera's nutritional and sensory attributes for household and industrial applications.

The separation of two isomers of conjugated linoleic acid is highly significant since each exhibits different biochemical properties. The aim of this study was to investigate and optimize several factors affecting the esterification of L-menthol with the c9,t11-CLA isomer in an organic solvent-free system using lipase from Candida rugosa (Lipase AY-30). D-optimal design with 5 factors and 3 levels were employed to evaluate the effects of synthesis parameters; reaction time (8–24 h), temperature (30–50 °C), enzyme content (2–20 U/ml), substrate molar ratio of conjugated linoleic acid oil to L-menthol (2:1–1:2) and pH (6–8) on esterification of c9,t11-CLA with L-menthol. Based on the analysis of the residual amount of c9,t11-CLA in the free fatty acid fraction after just one-step esterification, the optimum synthesis conditions were as follows: reaction time 23.12 h, temperature 32.65 °C, enzyme amount 135.40 U, molar ratio of CLA oil to L-menthol at 1:1.7 and pH at 7.7; the lowest purity of c9,t11-CLA in free fatty acid fraction based on the total content of c9,t11 and t10,c12-CLA isomers was 8.6 %.

This study was carried out to optimize the compositions of red teff flour with malted soybean flour and papaya fruit powders for better nutritional quality and sensory acceptability of porridge. Total eleven formulations of the composite flours were determined using D‐optimal mixture design with the help of Minitab Version 16 Statistical Software. The three ingredients were considered in the ranges of 55%–70%, 20%–30%, 5%–15% for red teff flour, malted soybean flour, and Papaya fruit powder, respectively. The prepared porridge samples from formulations were analyzed for nutritional composition, antinutritional factors, and sensory acceptability. Results of the study showed the significant difference (p < .05) in ash, fat, fiber, protein, carbohydrate, energy, iron, calcium, zinc, β‐carotene, phytates, tannin, appearance, taste, mouthfeel, and overall acceptability as the composition of ingredients were changed. The overall optimum point was found in a range of red teff flour (60%–70%), malted soybean flour (20%–27.5%), and papaya fruit powder (10%–12.5%). In conclusion, the present approach can help in improve infants dietary quality of complementary foods by developing nutritionally enhanced red teff‐based porridge used for intervention of malnutrition. Study conducted to optimize the composition of Red teff flour, malted soybean flour, and papaya fruit powder for porridge preparation. The overall optimum point was found in a range of RTF 60%–70%, MSF 20%–27.5%, and PFP 10%–12.5%. The formulation within the optimum may give porridge with fat 5.27%–7.44%, fiber 2.33%–3.6%, protein 12.55%–24.22%, carbohydrate 60.08%–69.68%, energy 376.3–385.56 kcal/100 g, iron 12.55–34.86 mg/100 g, calcium 201.49–293.57 mg/100 g, zinc 4.05–5.58 mg/100 g, β‐carotene 0.608–5.737 mg/g, and overall acceptance of 4.02–4.97 (on five‐point hedonic scale). So, this formulation can use as the complimentary food for the children under 2 years.

The purpose of this work was to optimize the pretreatment process of wheat straw by Polyporus brumalis_BRFM985 in order to improve carbohydrate accessibility for more efficient bioconversion. Indeed, there is growing demands to develop sustainable routes for lignocellulosic feedstocks valorization into value‐added products in energy, chemicals, materials, and animal feed fields. To be achieved, implementation of cheap and ecofriendly biomass pretreatment processes is necessary. In this frame, white rot basidiomycetes, well known for their ability to degrade lignin efficiently and selectively, are of great interest. The pretreatment of wheat straw by Polyporus brumalis_BRFM985 was performed in packed bed bioreactor and optimized using response surface methodology. The four pretreatment parameters optimized were metals addition (Cu, Mn, and Fe), time of culture, initial water content, and temperature. Multicriteria optimization highlighted that wheat straw pretreatment by Polyporus brumalis_BRFM985 in the presence of metals with high initial water content of 3.6 g H2O/g at 27°C for 15–16 days led to an improvement of carbohydrate accessibility with minimal matter loss. Polyporus brumalis selectively delignifies wheat straw during solid‐state pretreatment in packed bed bioreactor. Four pretreatment parameters were optimized. Multicriteria optimization highlighted the need for metals addition with high initial water content 3.6 g H2O/g for 15–16 days at 27°C to achieve a more efficient bioconversion.

Agomelatine (AGO) is a dual-functional drug. It uses as an antidepressant when orally administrated and antiglaucomic when topically applied to the eye. This study aimed to formulate AGO into bilosomal vesicles for glaucoma treatment, as modern studies pointed out the effect of topical AGO on intraocular pressure for the treatment of glaucoma. A modified ethanol injection technique was used for the fabrication of AGO bilosomes according to a D-optimal design. Phosphatidylcholine (PC) to edge activator (EA) ratio, Hyaluronic acid percentage (HA%), and EA type were utilized as independent variables. The measured responses were percent entrapment efficiency (EE%), particle size (PS), polydispersity index, zeta potential, percentage of drug released after 2 h (Q 2h% ), and 24 h (Q 24h% ). The optimal bilosomal formula (OB), with the desirability of 0.814 and the composition of 2:1 PC: EA ratio, 0.26% w/v HA and sodium cholate as EA, was subjected to further in vitro characterizations and in vivo evaluation studies. The OB formula had EE% of 81.81 ± 0.23%, PS of 432.45 ± 0.85 nm, Q 2h% of 42.65 ± 0.52%, and Q 24h% of 75.14 ± 0.39%. It demonstrated a higher elasticity than their corresponding niosomes with a typical spherical shape of niosomes by using transmission electron microscope. It exhibited acceptable stability over three months. pH and Refractive index measurements together with the histopathological study ensured that the OB formula is safe for the eye and causes no ocular irritation or blurred vision. The OB formula showed superiority in the in vivo pharmacodynamics parameters over the AGO solution, so AGO-loaded bilosome could improve ocular delivery and the bioavailability of agomelatine.