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Cereals possess functional and nutritional properties already consolidated in the literature; they are also an excellent source of health-promoting bioactive compounds. This work aimed to determine the best solvents to extract phenolic compounds from black oat, rye, and wheat through a simplex centroid design, using pure solvents and binary and ternary mixtures. The response variables were the total phenolic compounds (TPC) and the antioxidants (DPPH, ABTS, FRAP) were quantified. For optimized extract, the phenolic compounds were identified by UHPLC. An optimization study for the recovery of antioxidant compounds from these cereals to obtain extracts with better antioxidant properties is reported. The water and acetone binary mixture extracted 18, 49, and 110% more than water and 3.2, 4.0, and 5.5 times more than TPC acetone for black oat, rye, and wheat, respectively. Chromatography identified that rye has the highest number of phenolic compounds, including vanillic acid (3008.64 μg g-1), ellagic acid 352.05 μg g-1, hesperetin 24.33 μg g-1, and formononetin. To conclude, the binary mixture of water and acetone was the best condition to obtain a maximized extract for the analyses in the optimized proportions of solvents for the extract are as follows: 0.52/0.48 for oats, 0.46/0.54 rye and 0.33/0.67 for wheat, respectively. This study optimizes the time and improves quality in measuring antioxidants, both for cereals and derivatives, and for evaluating the potential of new products.

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.

Limestone-calcined clay (LC3) cement has emerged as a promising low-carbon alternative to ordinary Portland cement (OPC), offering significant potential to reduce carbon emissions while maintaining comparable mechanical performance. However, the absence of a prediction model for the formulation of the LC3 system presents challenges for optimisation within the evolving concrete industry. This study introduces a multi-objective optimisation (MOO) framework to design the optimal LC3 system, aiming to maximise compressive strength while minimising environmental and economic costs, simultaneously. The MOO framework integrates a regularised multivariate polynomial regression (MPR) model, achieving an R2 of 0.927 and MSE of 3.445 for mechanical performance prediction. Additionally, life cycle assessment quantifies the environmental impact, and collected market prices contribute to financial considerations of the LC3 system. Utilising a dataset of 366 LC3 mortar mixtures, the optimisation challenges the conventional 2:1 calcined clay-to-limestone ratio (CC:LS). For high strength (≥65 MPa), target a CC:LS ratio of 1:1 to 1.6:1; for lower strength (<65 MPa), increase calcined clay content, resulting in a CC:LS ratio of 1.6:1 to 2:1. The proposed framework serves as a valuable starting point to enhance the efficiency of LC3 system design and help decision-making to achieve desired mechanical, economic, and environmental objectives.

Among the freshwater benthic macroalgae found in Brazilian rivers and streams, species of Oedogonium (Chlorophyta) have shown great ability to produce high biomass in several environmental conditions. Their resilience to eutrophic habitats provides an opportunity to assess the use of Oedogonium biomass for applications in different fields, especially those related to the extraction of biologically active compounds of interest for health, food, and cosmetic applications. Thus, this study assessed the effects of different organic solvents (acetone, ethanol, and methanol) and their mixtures when combined with water (20% v/v), on the extraction efficiency of total phenolic compounds (TPC), and their antioxidant capacity (AC), from Oedogonium sp. biomass, using simplex-centroid mixture design. TPC ranged from 90 to 150 mg of gallic acid equivalent per 100 g dry weight (DW), while AC ranged from 1 to 8 µM of Trolox equivalent per gram (DW). The highest TPC and AC were found for extracts using an ethanol/acetone (50:50) binary aqueous solvent mixture, while the lowest values for both TPC and AC were found for the methanolic aqueous solvent. Considering the relatively low-cost and less adverse environmental impacts of the ethanol and acetone aqueous mixture, our results suggest that the use of this specific mixture of organic solvents may have significant advantages in an eventual industrial process for the extraction of phenolic compounds from Oedogonium species.

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 low-cost production of cellulolytic complexes that present high action at mild conditions is one of the major bottlenecks for the economic viability of the production of cellulosic ethanol. The influence of agro-industrial residues was assessed to enhance endoglucanase production by Aspergillus niger 426 grown in solid state fermentation. The highest percentage of lignin degradation was found on soybean hulls (56%) followed by sugarcane bagasse (36%) and rice straw (8.5%). The cellulose degradation, around 90%, was observed on soybean hulls and sugarcane bagasse, but only 50% on rice straw, and maximum production of endoglucanase (112.34 ± 0.984 U mL-1) was observed for soybean hulls. The best Experimental Mixture Design condition was under cultivation of 2.5 g of sugarcane bagasse, 2.3 g of rice straw and 5.2 g of soybean hulls, leading to a maximum activity of 138.92 ± 0.02 U mL-1. The statistical methodology enabled an increase of over 20% in the production of endoglucanase using agro-industrial waste. These data demonstrate that A. niger 426 is a potential source of cellulases which can be obtained by solid state fermentation using agro-industrial waste.

The selection of suitable solvents for selective extraction is a challenging task due to expensive experimental studies and heavy reliance on heuristics which introduce errors and biased judgements. The development of computer-aided molecular design (CAMD) has facilitated the search for novel solvents by providing an efficient and systematic computational approach. In this work, a novel CAMD framework for the selection of most suitable solvents or solvent mixtures by quantifying thermodynamics and sustainability aspects has been developed. The overall solvent design methodology can be described via a multi-level approach. The first level involves the prescreening of molecular blocks by introducing a solubility model to identify the promising functional groups. The second level includes the implementation of a rigorous model to determine potential solvents based on phase equilibrium information such as selectivity and capacity. Finally, the third level incorporates safety and health parameters in the formulation of a multi-objective optimization problem. Here, a modified fuzzy algorithm is developed to address the trade-off between thermodynamic and safety and health properties. In cases where the selectivity and capacity show antagonistic behaviour, solvent mixture design is introduced to improve the extraction performance. The developed CAMD framework is illustrated through a case study on the extraction of 1,2-dichloroethane from cyclohexane.

Nanoemulsion is one of the potential drug delivery strategies used in topical ocular therapy. The purpose of this study was to design and optimize a nanoemulsion-based system to improve therapeutic efficacy of moxifloxacin in ophthalmic delivery. Moxifloxacin nanoemulsions were prepared by testing their solubility in oil, surfactants, and cosurfactants. A pseudoternary phase diagram was constructed by titration technique and nanoemulsions were obtained with four component mixtures of Tween 80, Soluphor® P, ethyl oleate and water. An experiment with simplex lattice design was conducted to assess the influence of formulation parameters in seven nanoemulsion formulations (MM1–MM7) containing moxifloxacin. Physicochemical characteristics and in vitro release of MM1–MM7 were examined and optimized formulation (MM3) was further evaluated for ex vivo permeation, antimicrobial activity, ocular irritation and stability. Drug pharmacokinetics in rabbit aqueous humor was assessed for MM3 and compared with conventional commercial eye drop formulation (control). MM3 exhibited complete drug release in 3 h by Higuchi diffusion controlled mechanism. Corneal steady state flux of MM3 (~32.01 µg/cm2/h) and control (~31.53 µg/cm2/h) were comparable. Ocular irritation study indicated good tolerance of MM3 and its safety for ophthalmic use. No significant changes were observed in the physicochemical properties of MM3 when stored in the refrigerator for 3 months. The greater aqueous humor concentration (Cmax; 555.73 ± 133.34 ng/mL) and delayed Tmax value (2 h) observed in MM3 suggest a reduced dosing frequency and increased therapeutic efficacy relative to control. The area under the aqueous humor concentration versus time curve (AUC0–8 h) of MM3 (1859.76 ± 424.51 ng·h/mL) was ~2 fold higher (p < 0.0005) than the control, suggesting a significant improvement in aqueous humor bioavailability. Our findings suggest that optimized nanoemulsion (MM3) enhanced the therapeutic effect of moxifloxacin and can therefore be used as a safe and effective delivery vehicle for ophthalmic therapy.

Ultra-high-performance concrete (UHPC) is a recent class of concrete with improved durability, rheological and mechanical and durability properties compared to traditional concrete. The production cost of UHPC is considerably high due to a large amount of cement used, and also the high price of other required constituents such as quartz powder, silica fume, fibres and superplasticisers. To achieve specific requirements such as desired production cost, strength and flowability, the proportions of UHPC’s constituents must be well adjusted. The traditional mixture design of concrete requires cumbersome, costly and extensive experimental program. Therefore, mathematical optimisation, design of experiments (DOE) and statistical mixture design (SMD) methods have been used in recent years, particularly for meeting multiple objectives. In traditional methods, simple regression models such as multiple linear regression models are used as objective functions according to the requirements. Once the model is constructed, mathematical programming and simplex algorithms are usually used to find optimal solutions. However, a more flexible procedure enabling the use of high accuracy nonlinear models and defining different scenarios for multi-objective mixture design is required, particularly when it comes to data which are not well structured to fit simple regression models such as multiple linear regression. This paper aims to demonstrate a procedure integrating machine learning (ML) algorithms such as Artificial Neural Networks (ANNs) and Gaussian Process Regression (GPR) to develop high-accuracy models, and a metaheuristic optimisation algorithm called Particle Swarm Optimisation (PSO) algorithm for multi-objective mixture design and optimisation of UHPC reinforced with steel fibers. A reliable experimental dataset is used to develop the models and to justify the final results. The comparison of the obtained results with the experimental results validates the capability of the proposed procedure for multi-objective mixture design and optimisation of steel fiber reinforced UHPC. The proposed procedure not only reduces the efforts in the experimental design of UHPC but also leads to the optimal mixtures when the designer faces strength-flowability-cost paradoxes.

Three-dimensional (3D) printing with foam concrete, which is known for its distinct physical and mechanical properties, has not yet been purposefully investigated. The article at hand presents a methodological approach for the mixture design of 3D-printable foam concretes and a systematic investigation of the potential application of this type of material in digital construction. Three different foam concrete compositions with water-to-binder ratios between 0.33–0.36 and densities of 1100 to 1580 kg/m3 in the fresh state were produced with a prefoaming technique using a protein-based foaming agent. Based on the fresh-state tests, including 3D printing as such, an optimum composition was identified, and its compressive and flexural strengths were characterized. The printable foam concrete showed low thermal conductivity and relatively high compressive strengths of above 10 MPa; therefore, it fulfilled the requirements for building materials used for load-bearing wall elements in multi-story houses. Thus, it is suitable for 3D-printing applications, while fulfilling both load-carrying and insulating functions.