Mixture Experimental Design Research Articles

Applying Numerical Optimization Technique in the Development of Valueadded Powdered Custard

Custard powder is a relatively shelf-stable, fine textured, dry-formulated, carbohydrate-rich breakfast food product primarily made from corn starch with the addition of salt, flavoring, and coloring agents and with or without the inclusion of egg yolk solids, vitamins, and minerals. The aim of this study was to formulate, characterize, and optimize value-added custard from millet, soybean, and tigernut. A three-component constrained optimal (custom) mixture experimental design, with 69 randomized experimental runs, was employed. The formulation design constraints were: millet (10% - 70%), soybean (10% - 70%), and tigernut (10% - 70%). Other constant components of the formulation were sweet potato extract (3%), date (2 %), clove (1%), cinnamon (1%), turmeric (1%), and ginger (2%). The formulated samples were analyzed and evaluated for quality and sensory characteristics using standard procedures. The result of the optimization gave optimized custard with an overall desirability index of 0.535, based on the set optimization goals and individual quality desirability indices. The optimal formulated value-added custard was obtained from 10.0 % millet flour, 49.3 % soybean meal, and 30.7 % tigernut. The quality properties of this optimal custard are 3.78 % ash content, 28.5 % crude protein, 10.1 % moisture content, 20.1 % fat content, 4.43 % crude fibre, 33.9 % carbohydrate, and 5.79 overall acceptability. The result of the study showed that the formulated value-added custard was found to be of higher quality than the traditional custard which is produced from monocereals. Improving the nutritional quality of food and tackling nutrient deficiencies, particularly protein energy malnutrition in populations, is possible through the application of numerical optimization technique in development new, dietary-based food products.

Effect of Silica/M-BNT/BNT Ternary Filler Loading on the Curing Properties of Natural Rubber Vulcanizates

The goal of this study was to investigate the effect of varied proportions of silica/M-BNT/BNT on the curing properties of natural rubber (NR) composites. Thirteen ternary-filled NR composites and a control sample were prepared in this study based on a third degree – simplex lattice mixture design of experiment. It was observed that high loading of silica has a retarding effect on vulcanization rate due to the presence of silanol groups on its surface. Replacement of silica with BNT led to a big reduction in scorch time and increase in vulcanization rate due to the presence of metallic oxides acting as co-activators. The addition of the organoclay M-BNT to silica and BNT resulted to shorter scorch time, an increase in vulcanization rate, and a decrease in minimum and maximum torques improving the processability of the rubber composite. The kinetic model was able to demonstrate the vulcanization behavior of the rubber composites as supported by the very high coefficient of determination (R2) values for all samples. The generated contour plots for maximum torque (MH), minimum torque (ML), scorch time (tS1), cure rate index (CRI), induction time (ti) and rate constant (k1) were able to display the trends observed in the experimental values.

Optimization of medium with perfusion microbioreactors for high density CHO cell cultures at very low renewal rate aided by design of experiments.

A novel approach of design of experiment (DoE) is developed for the optimization of key substrates of the culture medium, amino acids, and sugars, by utilizing perfusion microbioreactors with 2 mL working volume, operated in high cell density continuous mode, to explore the design space. A mixture DoE based on a simplex-centroid is proposed to test multiple medium blends in parallel perfusion runs, where the amino acids concentrations are selected based on the culture behavior in presence of different amino acid mixtures, and using targeted specific consumption rates. An optimized medium is identified with models predicting the culture parameters and product quality attributes (G0 and G1 level N-glycans) as a functionof the medium composition. It is then validated in runs performed in perfusion microbioreactor in comparison with stirred-tank bioreactors equipped with alternating tangential flow filtration (ATF) or with tangential flow filtration (TFF) for cell separation, showing overall a similar process performance and N-glycosylation profile of the produced antibody. These results demonstrate that the present development strategy generates a perfusion medium with optimized performance for stable Chinese hamster ovary(CHO) cell cultures operated with very high cell densities of 60 × 106 and 120 × 106 cells/mL and a low cell-specific perfusion rate of 17 pL/cell/day, which is among the lowest reported and is in line with the framework recently published by the industry.

Prediction of structural properties of activated carbons derived from lignocellulosic biomass components using mixture design of experiments

There is significant interest in using biomass to produce activated carbons for applications such as environmental remediation, energy storage, and construction. To date, the research has primarily focused on exploring the potential of individual biomass sources. There is little research on how interactions between the fundamental components of lignocellulosic biomass affect activated carbons' structural properties, and, therefore, their suitability for various applications. This investigation sought to address this knowledge gap by exploring how the relative compositions of cellulose nanofibers, cellulose nanocrystals, and lignin affected the final properties of activated carbons produced through a two-step KOH activation process. A simplex-lattice mixture design was used to understand how the composition affected activated carbon yield, specific surface area, and micropore fraction. Mixture regression models showed that the specific surface area was highest when the activated carbon was composed of 16.7% cellulose nanofibers, 16.7% cellulose nanocrystals, and 66.7% lignin by mass. In addition, the micropore fraction was largest at 76.3% while also maintaining a high surface area, with a precursor mixture of 50% cellulose nanocrystals and 50% lignin by mass. The results of this investigation provide a foundation for future efforts to be able to predict, and tune, activated carbon properties based on biomass composition.

Application of mixture design of experiments to the development of alkali-activated composites based on chamotte and waste glass

Alkali-activated binders have been indicated as possible replacement for Portland Cement with environmental benefits. However, an adequate dosage of raw materials and alkalis content is mandatory to obtain alkali-activated materials with suitable physical and mechanical characteristics. The aim of this work is the application of mixture design of experiments to determine optimal dosages for pressed alkali-activated composites based on two precursors: chamotte (C) and waste glass (WG). Fine sand (S) was used as aggregate. Ten mixtures were obtained from the experimental design, and cylindrical samples were cast by uniaxial pressing. The alkalis/precursor ratio was kept constant for all the mixtures. After curing, the physical, mechanical and microstructural characteristics of the specimens were assessed. The application of desirability function revealed that the optimal composition was the one with higher content of sand (50%), 30% chamotte and 20% waste glass. The addition of sand contributed to reduce porosity and increase the particle packing, promoting the increase of mechanical strength. Regarding compressive strength, almost all the mixtures met the requirement for solid bricks. This study demonstrates the potential of using mixture design of experiments as an efficient tool to determine the proper composition and predict the behavior of waste-based alkali-activated materials.

청양고추와 레몬을 첨가한 소금의 최적화를 위한 배합비 연구

This study was conducted to produce salt with the addition of Cheongyang hot pepper and a lemon by using a mixture experiment design. It conducted a general composition analysis and an antioxidation analysis on the Cheongyang hot pepper and the lemon, and then, it performed a sensory test and a physicochemical test on 14 kinds of samples which it produced by designing a mixing ratio with the Augmented Design. The general composition analysis showed significant differences in all compositions such as moisutre, crude ash, crude fat, crude protein, crude carbohydrate and Kcal, and that antioxidation was stronger in the lemon than in the Cheongyang hot pepper. Based on the results of the sensory test on 14 kinds of salt mixture, it analyzed the interactions among Cheongyang hot pepper, lemon and salt, to optimize them. The desirability of the optimization was 89.4% and the optimal mixing ratio of Cheongyang hot pepper ; lemon : salt was = 80% : 10% : 10% = 100%, and the physicochemical characteristics were prescribed as follows: salinity=1.27, pH=5.08, sugar content=1.58, brightness=52.09, redness=-0.59 and yellowness=4.43. Ultimately, this study was expected to contribute to the commercialization of mixed salt of high quality.

Loteprednol-Loaded Nanoformulations for Corneal Delivery by Quality-by-Design Concepts: Optimization, Characterization, and Anti-inflammatory Activity.

Loteprednol etabonate (LE) is a topical corticosteroid that uses inflammatory conditions of the eye. It has a low ocular bioavailability and side effects such as corneal disorder, eye discharge, and ocular discomfort. Therefore, it was decided to select the delivery systems, which are solid lipid nanoparticles (SLN), nanostructured lipid carriers (NLC), and nanoemulsion (NE). Design of experiments (DoE) of SLN, NLC, and NE formulations were formulated by using the quality by design (QbD) approach. Precirol® ATO 5 and oleic acid were used as solid and liquid lipids, respectively, in SLN, NLC, and NE formulations. Physiochemical characterization was performed on the formulations. The optimized formulations' inflammatory effects have been appraised on human corneal epithelial cells employing the ELISA test. Physicochemical characterization studies and inflammatory effects were appraised. The sizes of optimized formulations of SLN, NLC, and NE were 86.19nm, 82.38nm, and 126.35nm, respectively, with minimum polydispersity. The release behavior of the formulations is composed of both diffusion and erosion. ELISA test results proved that the formulations significantly reduced IL-1 and IL-6 levels (p < 0.05). D-optimal mixture experimental design allowed us to develop the most precise formulations of SLN, NLC, and NE. Furthermore, the optimized formulations could be promising candidates for treating an inflammation-based corneal disease of the eye.

“Application of the DoE approach to the fabrication of cast Al2O3-LM6 composite material and evaluation of its mechanical and microstructural properties”

This paper explain the impact of varying amount of reinforcement on mechanical and micro-structural properties during the fabrication of Al2O3-LM6 cast composite at self-pouring temperature. To decide the reinforcement proportion, use of mixture design of approach was used. Fine triturates of Al, Si, and xAl2O3 (x = 2%, 3%, and 4%) were mixed firstly by using ball milling. Then powder mix, which was pre-heated and became moisture free, condensed in sealed steel die. Compacted pre-forms were heated in a muffle furnace in the presence of Argon gas which creates an inert environment at self-poring temperature deprived of forming liquid slurry to pour under gravity in the cavity of Plaster of Paris (POP) die to synthesize the final cast composite. Blend combination was decided using mixture Design of Experiment (DOE) through Minitab Software and an Analysis of Variance (ANOVA) was performed to heighten the best mixture combination. Then fabricated modified cast composite then tested to show a combined outcome of reinforcement proportion on modified physical, mechanical and micro-structural properties.

Plastic behaviour of clay materials for the manufacture of fast-drying red ceramics

AbstractFast drying (~60 min) is useful for optimizing production processes by increasing productivity and reducing costs and environmental impacts, especially in red ceramic industries in Brazil. However, suitable clays are necessary and, currently, studies focused on the plastic behaviour of clays with compositions suitable for extrusion, especially for fast drying, are scarce. Therefore, in this study, three different clays from the same mineral deposit were studied for producing clay-based structural products via fast drying. The clays were characterized according to their chemical, mineralogical and thermal properties, particle size, cation-exchange capacity, specific surface area and open pore volume distribution. Ten formulations were developed using a simplex-centroid mixture design of experiments and their plasticity index (PI) values were determined. The response surfaces of the formulations were evaluated according to their PI, while the formation characteristics were determined according to their extrusion workability factor values. Formulations F5 (50.0 wt.% yellow clay and 50.0 wt.% green clay) and F8 (66.6 wt.% yellow clay, 16.7 wt.% grey clay and 16.7 wt.% green clay; PI = 15.5–16.6%) displayed optimal extrusion properties, followed by formulations F7 (33.3 wt.% yellow clay, 33.3 wt.% grey clay and 33.3 wt.% green clay) and F10 (16.7 wt.% yellow clay, 16.7 wt.% grey clay and 66.6 wt.% green clay; PI = 13.8–14.2%), which are within acceptable extrusion index values. Thus, the chosen formulations have significant potential for use in the manufacture of fast-drying red ceramics.

How the experimental design associated with objectivized sensory analysis can be used to predict odor quality of gaseous mixtures?

From environmental nuisances to perfumes and aromas, the need to understand odorous interactions is growing. In this context, sensory analysis is often used but faces an obstacle when dealing with odor mixtures. Indeed, sensory approaches provide data on the global nature and intensity of these mixtures, but these methods cannot reveal the various interactions taking place when perceiving an odor. To address this issue, modeling the variations in odor nature and intensity according to the composition of complex mixtures offers insight into sensory interactions as well as a tool to predict odor nature and intensity. While several models have been developed for odor intensity, models for odor nature are scarce. In this study, an experimental mixture design is combined with sensory analysis with the aim of developing new numerical models that transform chemical data into sensory data. Mixtures of four odorants, dimethyl disulfide (DMDS), toluene, furfuryl mercaptan, and cyclopentanone are composed of multiple values of the Odor Activity Value (OAV) for each odorant. The odor nature of each mixture is described using Langage des Nez® (LdN), an objectivized odor nature description method. The variation in odor nature with the composition of the mixture is modeled using the experimental mixture design. The yielded models are shown to be valid and of relatively good descriptive (adjusted R2) between 0.610 and 0.925) and predictive (predictive R2 between 0.390 and 0.886) qualities. The models provide insight into the individual contributions and binary and ternary interactions among the odorants impacting the global odor nature. Moreover, the models allow the odor nature of a mixture to be predicted by transforming the chemical concentration of each odorant into an odor nature description. These results may be an answer to the question raised by the odor mixture effect.

Application of mixture experimental design for optimization of nickel catalyst promoted with Cu and Mg and supported by kieselguhr for soybean oil hydrogenation

Application of mixture experimental design for optimization of nickel catalyst promoted with Cu and Mg and supported by kieselguhr for soybean oil hydrogenation

D-OPTIMAL MIXTURE DESIGN APPROACH IN THE PRODUCTION OF COOKIES ENRICHED WITH DIETARY FIBER SOURCES SUCH AS LENTIL FLOUR, BANANA FRUIT AND BANANA PEEL POWDER

This study aimed to develop fiber-enriched cookies by partially replacing wheat flour (WF) with alternative fiber sources including yellow lentil flour (YLF), green lentil flour (GLF), banana powder (BP), and banana peel powder (BPP). The ratios of YLF, GLF, and WF were identified as 24.5, 10.7, and 64.8 % (flour basis), respectively, considering the mixture experimental design for an optimal breaking strength of cookies. Four types of cookies were studied: (i) wheat-based cookies (WC), (ii) lentil flour, BP and BPP-incorporated cookies (LBC), (iii) BP and BPP-incorporated cookies (BC), (iv) lentil flour-incorporated cookie (LC). LC showed a higher spread ratio and lightness than LBC and BC. Although alternative ingredients enhanced dietary fiber content compared to wheat-only cookies, the liking degree of sensory attributes was lower in LBC and BC. Overall, LC was superior to LBC and BC samples, indicating possible use of lentil flours for partial replacement of wheat flour for desired physical, chemical, and sensory attributes.

Multiresponse optimization of biodiesel obtained from a mixture of soybean oil, beef tallow, and waste cooking oil

Biodiesel is considered an alternative fuel that offers socio-environmental advantages over regular diesel from fossil origin. Several feedstocks can produce biodiesel, but animal fat and waste cooking oil (WCO) have become attractive due to their low cost and easy availability. These feedstocks can be mixed with traditional soybean oil to improve biodiesel’s properties, such as oxidation stability and cloud point, which would help comply with the quality parameters required by law. This work aimed to optimize pure biodiesel (B100) production from the mixture of soybean oil, beef tallow, and WCO by the methyl route and homogeneous (basic) catalysis. The simplex-centroid mixture experimental design was used to optimize yield, Induction Period (IP), and Cold Filter Plugging Point (CFPP). The results indicated that the optimum formulation was a mixture containing 50% soybean oil, 33% beef tallow, and 17% WCO. The quality parameters evaluated are within limits established by international legislation; IP of 7.11 h and CFPP of 0 °C. The real values of the R2, p-values, or t-values validate the obtained models.

Development, characterization, and modeling of aluminum chips-gabbro filler polystyrene hybrid composite using mixture design

A hybrid of aluminum chips and gabbro particulate filler reinforced polystyrene composite material was developed using a compounding machine and subsequent thermo-compression in mixture design experiment technique. Constituents’ materials used were confirmed using XRF and FTIR analyzers. Characterization results of the three-constituent and two-constituent analysis showed that an increase in aluminum and a decrease in gabbro compositions increase the flexural strength while both increases in aluminum and gabbro compositions increase the hardness value and density respectively. Mathematical models of the properties showed that the flexural strength fits best with the modified cubic model, hardness value with the special cubic model and the density with the cubic model. All the models were found to be statistically significant in predicting the respective properties with p-values less than 5%. The optimized formulation of maximization of the flexural strength and hardness value was determined at 4, 19, and 77% of aluminum chips (filler), gabbro rock particles (filler), and polystyrene (matrix) respectively with a corresponding flexural strength of 46.1 MPa and hardness value of 32.23Hv. Overall, the maximum flexural strength (sample 3) and hardness value (sample 9) as compared with the control sample (aluminum chips) were improved by 63 and 182% respectively.

Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting

Electric power systems have experienced the rapid insertion of distributed renewable generating sources and, as a result, are facing planning and operational challenges as new grid connections are made. The complexity of this management and the degree of uncertainty increase significantly and need to be better estimated. Considering the high volatility of photovoltaic generation and its impacts on agents in the electricity sector, this work proposes a multivariate strategy based on design of experiments (DOE), principal component analysis (PCA), artificial neural networks (ANN) that combines the resulting outputs using Mixture DOE (MDOE) for photovoltaic generation prediction a day ahead. The approach separates the data into seasons of the year and considers multiple climatic variables for each period. Here, the dimensionality reduction of climate variables is performed through PCA. Through DOE, the possibilities of combining prediction parameters, such as those of ANN, were reduced, without compromising the statistical reliability of the results. Thus, 17 generation plants distributed in the Brazilian territory were tested. The one-day-ahead PV generation forecast has been considered for each generation plant in each season of the year, reaching mean percentage errors of 10.45% for summer, 9.29% for autumn, 9.11% for winter and 6.75% for spring. The versatility of the proposed approach allows the choice of parameters in a systematic way and reduces the computational cost, since there is a reduction in dimensionality and in the number of experimental simulations.

Use of mixture experiments to design diets forTenebrio molitor L. (Coleoptera: Tenebrionidae) larvae

Insects, particularly the larvae of theTenebrio molitor beetle, are proposed as a food source since they have a high nutritional value and are easy to breed. Although researchers used different approaches to develop diets forT. molitor larvae growth, none used mixture design experiments. Therefore, this study aimed to evaluate the feasibility of using a mixture experiments design and three macronutrient sources to develop a diet resulting in the best growth forT. molitor larvae. We placed groups of fifty larvae in polycarbonate containers, fed themad libitum one of 19 diets containing different macronutrient proportions for five weeks, and recorded the larvae’s weight and mortality every seven days, and a model was constructed for the relative weight increase (growth rate). Larvae weight rises as whey protein concentrate (protein source) increases, and soybean oil decreases. Results of survival analysis showed that the feed composition did not influence larvae survival. Mixture experimental designs could help develop adequate diets forT. molitor larvae and other insects.

Methane production by anaerobic co‐digestion of dairy manure and cassava wastes for energy recovery

AbstractBACKGROUNDIn this study, the anaerobic co‐digestion of cassava wastewater (CW), cassava bagasse (CB), and dairy manure (DM) was investigated to produce biogas with energy recovery. Mixture experimental design was performed to establish the proportion of each substrate, on the basis of total weight (%wt). The digestion was carried out under mesophilic conditions (35 °C) using a batch test.RESULTSThe best methane yields were 150.33 NmLCH4 gVS−1 (114.46 NmLCH4 gCOD−1) for the condition with 100% CW (C/N ratio = 14.04) and 130.43 normal mililiters of methane per gram of volatile solid (NmLCH4 gVS−1) and 93.84 normal mililiters of methane per gram of chemical oxygen demand (NmLCH4 gCOD−1) obtained with 66.7% DM, 16.7% CB and 16.7% CW (C/N ratio = 34.84). Experimental conditions containing high proportions of CB (above 33%) yielded high C/N ratios (above 52), resulting in low pH and high volatile fatty acid production, mainly acetic and butyric acids. Kinetics of methane production under optimal conditions were best described by Groot's multi‐stage model.CONCLUSIONAn energy balance for the best conditions were performed, leading to 452.86 MJ tonsubstrate−1 for a high DM content mixture experiment (DM = 66.7%, CB = 16.7%, CW = 16.7%); thereby showing that anaerobic co‐digestion can replace 22.5% of the energy demanded by cassava processing industry. © 2022 Society of Chemical Industry (SCI).

Hydrothermal kinetic modeling for microalgae biomass under subcritical condition cultivated in a close bubble tubular photobioreactor

In this study, the Spirulina platenesis growth and carbohydrates, proteins, lipids, and chlorophyll production were investigated by using synthetic wastewater via a mixture experimental design using carbon–nitrogen–phosphorus (C:N:P) relation according to the mixing obtained in the experimental space. In addition, a scale-up in the growth of microalgal biomass in a tubular column photobioreactor was carried out. The activation energy was determined by thermogravimetric analysis from the Spirulina platenesis biomass obtained from the experimental design. Also, kinetic growth analysis and thermal hydrolysis of Spirulina platensis biomass were performed for the experimental case where the carbohydrate concentrations were the highest. The stress induced by phosphorus limitation in the synthetic wastewater enhanced intracellular carbohydrates production. It achieves a maximum concentration of 859.59 ± 80.87 mg/L (60.11% w/w) on run 8. The results showed a relation between the activation energy and carbohydrates concentration in biomass. This indicates that the energy required to start the thermal degradation reaction increases as carbohydrate concentration increases. In terms of hydrothermal pretreatment, the temperature increment in the reactor raises the condition to extract carbohydrates from microalgae biomass. In contrast, the temperature increment decreases the reaction rate on the protein extraction. During the hydrothermal pretreatment, the best condition for the carbohydrates extraction in the Spirulina platensis biomass was 140oC for 45 min. At this condition, cell wall hydrolysis needs an energy supply of 18.65 kJ/mol. The development of this process will allow the fractionation of microalgal biomass and extraction of carbohydrates in terms of a biorefinery.

Neither too little nor too much: Finding the ideal proportion of excipients using confocal Raman and chemometrics

The applications of Raman imaging in pharmaceutical field are ever-increasing due its ability to obtain spatial and spectral information simultaneously, once it allows determine the chemical distribution of compounds. In this sense, it is used to study homogeneity, of paramount importance during the development of pharmaceutical formulations due to its relation to stability, safety and efficacy. Commonly, just surface is analyzed, but confocal Raman spectroscopy can also characterize the inner part of samples, allowing to determine phase separation in the early stages. In this sense, confocal 3D Raman microscopy was crucial to obtain the optimal proportion of Apifil®, Capryol® 90 and Transcutol® to promote controlled release of the local anesthetic butamben (BTB). 3D chemical maps were obtained by classical least squares (CLS) using pure compound spectra as S matrix, showing that chemical distribution throughout the material was different. Knowing that the composition of samples affects the homogeneity parameter, standard deviation and distributional homogeneity index (DHI) were used in mixture experimental design (DoE). From this analysis, it was revealed that a correct amount of Capryol® 90 enhances both miscibility and solubility. Furthermore, suitable miscibility was observed in two ratio proportions of excipients with a desirability of 0.783 and 0.742. These results unequivocally demonstrated that confocal Raman microscopy combined to DoE can bring pharmaceutical development to a higher level.

Multi-Response Modelling and Optimisation of Mechanical Properties of Al-Si Alloy Using Mixture Design of Experiment Approach

The research aims to produce, model, and optimise the mechanical properties of novel composite material through a structured multidisciplinary approach. The primary objective is to combine materials science, mechanical engineering, and statistical concepts to ensure Design for Manufacturability (DFM) from the industrial perspective. More specifically, the article is intended to determine the optimal mixture components and predictive model of Al-Si alloy with Al2O3 by accommodating multi-responses that enable DFM. The study adopted ASTM standards to prepare and test the novel composite material. Additionally, the Mixture Design of Experiment (DOE) approach was used to design the experimentation and subsequent analysis. In addition, microstructural images, Cox Response Trace plot, and Response Optimiser plot are effectively utilised to draw robust inferences. For multi-response modelling and optimisation, the composite material’s mechanical properties, like impact strength, hardness, density, and tensile strength, are considered. The study determines that innovative composite material will yield better results when Al-Alloy is 94.65 wt% and Al2O3 is 5.35 wt% from a multi-responses perspective. Further, it provides predictive models with a high level of predictability. Besides, the research shows that novel composite material has better mechanical properties from a practical perspective. The article not only provides the mechanical properties of a new class of material but also shows the effective utilisation of material science and statistical concepts to develop the novel material in a structured manner. This composite material can be used as a replacement for various parts of automobiles and aircraft. Additionally, researchers can use the article’s modelling and optimisation approach as a paradigm to create durable composite materials.