Mixture Design Research Articles

Mixture Design for Lightweight Geopolymer Concrete

Mixture Design for Lightweight Geopolymer Concrete

Utilising natural pigments as a filler replacement on the mix design, performance and colorimetric characteristics of the microsurfacing mixture: laboratory evaluation

Coloured pavements enhance road safety and urban aesthetics. This study examined the use of bitumen emulsion and natural pigments as replacements for natural filler in coloured microsurfacing mixtures (CMMs). Coloured powders and natural fillers were analyzed, and seven colour combinations of red, yellow, and blue pigments with titanium dioxide (TiO2) were used to create CMMs. Performance was evaluated according to ASTM D6372, while colour evaluation was assessed using a colorimetric system and a spectrophotometer device. The results revealed that the mixture containing the red pigment exhibited the most appropriate performance, which was based on its particle size and properties among the CMMs. Compared to the control sample, the red combination showed improved cohesion (9.3-9.5% increase), reduced moisture sensitivity (40.1% decrease), and less vertical displacement under traffic (14.5% reduction). TiO2 was beneficial for blue and red compounds at 1-3% content, while 1% TiO2 yielded optimal results for yellow compounds.

Design of experiment (DoE) of mucoadhesive valproic acid-loaded nanostructured lipid carriers (NLC) for potential nose-to-brain application

Epilepsy is a highly prevalent neurological disease and valproic acid (VPA) is used as a first-line chronic treatment. However, this drug has poor oral bioavailability, which requires the administration of high doses, resulting in adverse effects. Alternative routes of VPA administration have therefore been investigated, such as the nose-to-brain route, which allows the drug to be transported directly from the nasal cavity to the brain. Here, the use of nanostructured lipid carriers (NLC) to encapsulate drugs administered in the nasal cavity has proved advantageous. The aim of this work was to optimise a mucoadhesive formulation of VPA-loaded NLC for intranasal administration to improve the treatment of epilepsy. The Design of Experiment (DoE) was used to optimise the formulation, starting with component optimisation using Mixture Design (MD), followed by optimisation of the manufacturing process parameters using Central Composite Design (CCD).The optimised VPA-loaded NLC had a particle size of 76.1 ± 2.8 nm, a polydispersity index of 0.190 ± 0.027, a zeta potential of 28.1 ± 2.0 mV and an encapsulation efficiency of 85.4 ± 0.8%. The in vitro release study showed VPA release from the NLC of 50 % after 6 h and 100 % after 24 h. The in vitro biocompatibility experiments in various cell lines have shown that the optimised VPA-loaded NLC formulation is safe up to 75 µg/mL, in neuronal (SH-SY5Y), nasal (RPMI 2650) and hepatic (HepG2) cells. Finally, the interaction of the optimised VPA-loaded NLC formulation with nasal mucus was investigated and mucoadhesive properties were observed. The results of this study suggest that the use of intranasal VPA-loaded NLC may be a promising alternative to promote VPA targeting to the brain, thereby improving bioavailability and minimising adverse effects.

Factorial analysis and modeling of chemical admixtures’ influence on Portland cement paste rheology

The influence of the amount of superplasticizer (SPA) and air-entraining (AEA) admixtures on the rheological properties of a Portland cement paste was investigated based on a statistical approach. The factorial design had the yield stress (YS) and flow as response variables, while the factors of 0.5 ml/kg-cem < SPA < 1.5 ml/kg-cem and 1.2 ml/kg-cem < AEA < 3.6 ml/kg-cem were evaluated, considering main effects, factor interactions, correlation analysis and modelling. The main effects analyses showed that the flow decreased as the AEA or the SPA increased, while the opposite was observed for the YS. The interactions analyses showed that the AEA promoted a reduction trend using the lowest SPA for the flow, but the highest SPA nullified the effect of AEA on flow, which maintained the highest value of 113.7%. Similarly, the AEA favored an increasing trend for the YS due to the interaction with the low SPA content, but a high SPA changed this trend, which decreased to the lowest value of 2652.6 Pa. The flow model (R2 = 84.9%) indicated that the linear effect of the SPA was the most significant, but the linear effect of the AEA and the SPA*AEA interaction were similar in terms of their significance (p-value < 0.001), while the YS model (R2 = 99.6%) showed that the AEA’s linear effect and the AEA*SPA interaction were similar and the most significant (p-value < 0.001). This research provides insights that could facilitate modeling, prediction, and optimization of efficient mixture designs while also gaining clarity considering potential inhibitory effects or synergies, which promotes the responsible consumption and production of construction materials.Graphical

Quality by Design-Steered Development of Stealth Liposomal Formulation of Everolimus: A Systematic Optimization and Evaluation.

Everolimus is a drug approved for the treatment of breast cancer with HR+ and advanced breast cancer reoccurring in postmenopausal women. The oral administration of EVE has been observed to have low oral bioavailability and severe epithelial cutaneous events that include rashes and lip ulceration followed by mouth ulceration after oral administration. The present research aimed to enhance the bioavailability by loading the EVE into a stealth liposomal formulation (S-EVE-LIPO) intended for intravenous administration. The surface of the liposomes was modified with vitamin E TPGS, which prolongs the systemic circulation of the drug and provides additional benefits like inhibition of the P-gp efflux pump and acting synergistically with EVE. The formulation was prepared using the thin film hydration method and optimized using a D-optimal mixture design. ANOVA suggested the significance of the proposed mathematic model, and the optimized formulation was generated by design expert software. The optimized formulation (S-EVE-LIPO) was observed with nanometric size (99.5 ± 3.70 nm) with higher encapsulation efficacy (81.5 ± 2.86 %). The S-EVELIPO formulation indicated a sustained release profile as 90.22% drug release was observed in 48 h, whereas the formulation without vitamin E TPGS (EVE-LIPO) released only 74.15 drugs in 24 hours. In vitro cytotoxicity study suggested that the presence of vitamin E TPGS lowers the IC50 value (54.2 ± 1.69), increases the cellular uptake of the formulation, also increases the generation of ROS, and shows better hemocompatibility. Vitamin E TPGS could be set as a vital additive to improve therapeutic efficacy and reduce offsite toxicity and dosing frequency.

Modeling and design optimization of the performance of stone matrix asphalt mixtures containing low-density polyethylene and waste engine oil using the response surface methodology

In recent years, the use of waste plastic materials such as low-density polyethylene (LDPE) to modify asphalt binders and enhance mixture performance has garnered significant attention. One major concern with using such materials is the higher production temperature required, which necessitates the use of a bitumen extender agent, such as waste engine oil (WEO), to reduce the viscosity and mixing temperature. Therefore, using these stabilizing additives can reduce the consumption of virgin binder, especially for Stone Matrix Asphalt (SMA) mixtures, which require higher asphalt content. This study aimed to optimize the design of SMA modified by LDPE and WEO to minimize the optimum asphalt content (OAC) and mixing temperature while maximizing SMA performance. To achieve this, Response Surface Methodology (RSM) was utilized to develop the necessary models for optimizing design and predicting performance. The selected independent variables (factors) for the design include LDPE content, WEO content, OAC, and mixing temperature. Meanwhile, the responses (dependent variables) consist of Marshall stability, rut depth, tensile strength ratio, and resilient modulus, which were used to determine the best mix design. The findings demonstrated that the suggested models for the output variables can predict performance with a higher level of confidence. The Analysis of variance (ANOVA) demonstrated that predictive models were significant and well-fitted, with a coefficient of determination (R2) of higher than 0.80, an adequate precision value of greater than 4, and a low p-value (less than 0.05). The error percentage between the RSM-predicted and actual values was less than 5 %, indicating that RSM-established models can accurately and efficiently predict the SMA performance. The best mix design of the SMA mixture modified by LDPE and WEO was found to be 10 % LDPE, 3.91 % WEO, 5.92 % OAC, and mixing temperature of 152.24°C with a combined desirability of 82.8 %.

Multi-objective optimization of engineered cementitious composite based on machine learning and generative adversarial network

This study aims to establish a novel framework for mixture design optimization of engineered cementitious composite (ECC) by first collecting two original datasets of ECC's tensile stress and strain from the extensive and credible literature. The datasets comprise a wide range of variables including cementitious ingredients, 9 types of fiber and characteristics, admixtures, and experimental conditions. The data augmentation is then performed using a tuned constraints-modified Conditional Tabular Generative Adversarial Network (Tuned-CTGAN) to increase the model accuracy and generalizability. The fitness functions of tensile stress and strain are established based on four machine learning models with the hyperparameters tuned by the Hunger Games search (HGS) algorithm. After the data augmentation, the values of R2 in their test sets are increased from 0.874 to 0.925 and from 0.772 to 0.889, respectively. Subsequently, the third objective function (cost) is computed by polynomials and four classes of constraints (Min-max, volume, ratio, and fiber) are set up to define the variable's search space. A non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) is introduced to optimize the mixture proportions of ECC by simultaneously optimizing tensile stress, tensile strain, and cost. This paper combines the results of data augmentation, model prediction, and multi-objective optimization for complex ECC design, which aims to provide a basis for practical application.

AI-assisted ultrasonic wave analysis for automated classification of steel corrosion-induced concrete damage

Early detection of cracks in reinforced concrete caused by chloride intrusion is crucial for effective maintenance. This paper develops and compares AI-assisted models that use ultrasonic pulse waves to automatically assess early-stage concrete damage, particularly cracks caused by steel corrosion. Data were collected from 108 concrete cubes with various mixture designs, cover depths, and steel corrosion levels induced by the impressed current technique. The AI models, particularly convolutional neural networks (CNNs), outperformed traditional regression models, achieving up to 84% classification accuracy. The systematic and automated features of the AI-assisted algorithm enable reliable, consistent and rapid ultrasonic wave data analysis, which is especially beneficial for the condition assessment of large infrastructure systems. This advancement inspires further research into integrating automated IoT and robotics-assisted systems for comprehensive infrastructure monitoring.

An innovative mixture sampling strategy with uniform design: Application to global sensitivity analysis of mixture toxicity

Global sensitivity analysis combined with quantitative high-throughput screening (GSA-qHTS) uses random starting points of the trajectories in mixture design, which may lead to potential contingency and a lack of representativeness. Moreover, a scenario in which all factor levels were at stimulatory effects was not considered, thereby hindering a comprehensive understanding of GSA-qHTS. Accordingly, this study innovatively introduced an optimised experimental design, uniform design (UD), to generate non-random and representative sample points with smaller uniformity deviation as starting points of multiple trajectories. By combining UD with the previously optimised one-factor-at-a-time (OAT) method, a novel mixture design method was developed (UD-OAT). The single toxicity tests showed that three pyridinium and five imidazolium ionic liquids (ILs) exerted stimulatory effects on Vibrio qinghaiensis sp.-Q67; thus, four stimulatory effective concentrations of each IL were selected as factor levels. The UD-OAT generated 108 mixture samples with equal frequency and without repetition. High-throughput microplate toxicity analysis revealed that all 108 mixtures exhibited inhibitory effects. Among these, type B mixtures exhibited increasing toxicities that subsequently decreased, unlike type C mixtures, which consistently increased over time. GSA successfully identified three of the eight ILs as important factors influencing the toxicities of the mixtures. When individual ILs produced stimulatory effects, mixtures containing two to three ILs exhibited either stimulatory effects or none. In contrast, mixtures containing five to eight ILs exhibited inhibitory effects, while those containing four ILs showed a transition from stimulatory to inhibitory effects. This study provides a novel mixture design method for studying mixture toxicity and fills the application gap of GSA-qHTS. The phenomenon of individuals being beneficial while mixtures can be harmful challenges traditional mixture risk assessments.

Mixing design and performance of porous asphalt mixtures containing solid waste

The process of urbanization generates substantial amounts of solid waste materials, including glass, red brick, concrete, and ceramic tile. Effective treatment methods for these waste materials are currently lacking, resulting in significant land wastage and environmental pollution. Utilizing these waste materials in the construction of asphalt pavements presents a viable solution for their high-quality and large-scale utilization. In this study, the properties of commonly produced urban solid waste materials, including glass, red brick, concrete, and ceramic tile, were investigated for their suitability as limestone porous asphalt mixtures. The physical and chemical properties of the coarse aggregate were examined using four groups of preliminary tests, including leakage analysis, scattering, rutting, and freeze-thaw splitting tests. The results revealed that the apparent relative density varied for different particle sizes within the range of 2.36–4.75 mm, 4.75–9.5 mm, 9.5–16 mm, and 13.2–16 mm. However, the dynamic stability of the glass specimen was only 740 times/mm, which falls significantly below the technical requirement of 1500 times/mm. Additionally, the high-temperature stability of the glass specimen was deemed unsatisfactory. The red brick test piece exhibited inadequate water stability due to its high-water absorption rate. Therefore, reducing the amount of waste glass and minimizing the incorporation of waste red brick are recommended. The findings of this study contribute to the expansion of application methods and techniques for urban solid waste, improving the utilization rate of solid waste resources. Furthermore, the study sheds light on the changes in the performance of solid waste aggregates mixed with porous asphalt mixtures, providing valuable insights for road engineering endeavors.

A comprehensive evaluation of crumb rubber modified asphalt-aggregate interfacial behavior and the effect of water: Molecular dynamics simulation and experimental investigation

The incorporation of crumb rubber (CR) makes the asphalt-aggregate interfacial properties highly complex and uncertain, while water is a key environmental factor causing interfacial failure. This paper aims to evaluate the characteristics of crumb rubber modified asphalt (CRMA)-aggregate interaction and the interfacial failure mechanism under the influence of different material properties and water at ambient temperature. Firstly, the CRMA-aggregate interfacial behavior before and after water intrusion is investigated using molecular dynamics simulations at the molecular scale. Next, the failure process and characteristics of the CRMA-aggregate interface were analyzed using the disk-shaped compact tension (DCT) test at the macro scale. Finally, the effect of water intrusion on the interface failure mode was analyzed through image processing. The results show that CR enhances binder cohesion and significantly affects its adhesion to aggregates with high CaO and MgO contents regardless of the presence of water. The asphalt concentration distribution on the CaO and MgO surfaces is more concentrated with higher peak concentration, leading to superior interfacial adhesion and water damage resistance with binder, while Al2O3 and SiO2 were inferior. The use of asphalt with higher creep stiffness, high-strength alkaline aggregates, and smaller CR particle size is beneficial for enhancing the CRMA binder-aggregate interfacial fracture performance. Water intrusion severely affected the interfacial adhesion, resulting in a significant increase in the proportion of adhesion failure, which is the main cause of the deterioration of the interface properties. The findings of this study provide insights into the factors affecting the CRMA-aggregate interfacial behavior, which facilitate the accurate design and application of high-strength durable CRMA mixtures.

Formulation of novel low-sodium salts using potassium salts and dietary polysaccharide

Potassium citrate (KC) and potassium lactate (KL) are considered as salt replacers due to their saltiness, processing advantages, and health benefits. However, the obvious bitter taste associated with these compounds has limited their use in salt substitutes. Despite this challenge, little attention has been paid to improving their sensory properties. This study provided evidence that dietary polysaccharide carrageenan can effectively mask the bitterness of KC and KL by specifically binding K+ and forming double helix chains. A highly accurate prediction model was then established for the saltiness and bitterness of low-sodium salts using mixture design principles. Three low-sodium salt formulas containing different potassium salts (KC, KL, KCl), NaCl, and carrageenan were created based on the prediction model. These formulas exhibited favorable saltiness potencies (>0.85) without any noticeable odor, preserving the sensory characteristics of high-sodium food products like seasoning powder while significantly reducing their sodium content. This research provides a promising approach for the food industry to formulate alternative low-sodium products with substantially reduced sodium content, potentially contributing to decreased salt intake.

Optimization of Effervescent Tablets in Sensory Acceptance from the Active Ingredients of Fish Protein Hydrolysate and Microalgae Chlorellae sp. powder

Graphical Abstract Highlight Research 1. FPH and Chlorella sp. microalgae can be used as effervescent tablet ingredients received by panelists. 2. The mixture design method can determine the formula of effervescent tablets made from a combination of FPH and microalgae 3. The best percentage of FPH and microalgae in effervescent tablets was 5.873% and 9.127%. Abstract Effervescent tablets are a popular pharmaceutical dosage due to their ease of use and effectiveness in delivering active ingredients to consumers. In order to develop effervescent tablets that are more effective and highly nutritious, this research focuses on the use of Fish Protein Hydrolysate (FPH) and microalgae Chlorella sp. powder as the active ingredient. This research aims to optimize the effervescent tablet formulation in Sensory Acceptance. Formula optimization uses the Design Expert 13.0® program with the d-optimal mixture design method, which recommends 13 formula combinations with analysis of response: color, plant and fishy flavor, sweet taste, sour taste, aftertaste, hedonic, protein content, lipid content, water, and pH of effervescent tablets with a p-value &lt;0.05 indicates that the model results are significant. Evaluation of granules includes flow rate test, angle of repose test, compressibility test, and drying shrinkage test to meet the requirements. Evaluation of effervescent tablets includes size uniformity, weight uniformity, firmness, and dissolution time that meet the requirements. The proximate composition of FPH and Chlorella sp. powder consists of a protein content of 19.60% and 47.68%, a lipid content of 0.15% and 4.87%, a water content of 0.06% and 5.0%, and an ash content of 1.15% and 4.24%, respectively. In addition, Chlorella sp. powder consists of fiber (38.31%) and total flavonoids (2.69%). The optimal formula results obtained were with a composition of FPH 5.873% and Chlorella sp. powder 9.127%. The optimum formula of FPH and Chlorella sp. powder in effervescent tablets succeeded in increasing the acceptance of sensory attributes, including color response, plant aroma, fishy aroma, sweet taste, sour taste, aftertaste, hedonic, protein content, lipid content, and water content.

Development of healthier gummy candy by substituting glucose syrup with various fruit juice concentrates.

The main objective of this study was to evaluate the effects of various formulations of fruit juice concentrates (pomegranate, grape, and sour cherry) on the pH, water activity, density, color, texture, and microstructure characteristics of candies instead of glucose syrup. The experimental points of the study were examined by a D-optimal mixture design to optimize the concentration of fruits used in the formulation and achieve excellent physicochemical characteristics. Fruit juice concentrates, either singly or in combination, were used as a complete substitute for glucose syrup in the formulation. Total fruit juice concentration used in the formulation was 54.07% and each of the fruit juice concentrations changed between 0 and 54.07% in the formulation. By combining these three fruit juices, 14 gummy candy samples were produced, depending on the Special cubic, cubic, and quadratic models that were used for the effects on the physicochemical properties (pH, water activity, density, L*, a*, b*, and chroma), and the texture profile analysis (TPA) (hardness, cohesiveness, springiness, and resilience) parameters according to independent variables. Results showed that pH, water activity, and density values of the gummy candy samples were found to be in the range of 2.22-3.08, 0.46-0.52, and 1.10-1.53 g/mL, respectively, and were significantly affected by different fruit juice concentrates (p < .05). The texture profile analysis showed that except for springiness, fruit juice concentrations significantly affected the texture profile (p < .05). The texture values, such as hardness, springiness, cohesiveness, gumminess, and resilience of the gummy candy samples, were determined as 146.1-938.8 N, 0.63-0.99, 0.75-1.19, 136.02-947.94 g, and 0.12-0.51, respectively. In addition, various fruit juice concentrates significantly affected the color parameters of gummy candies, and using pomegranate juice and sour cherry concentrates increased the +a* value of the gummy candies. Therefore, fruit juice-based gummy candies can be developed as value-added gummy candies by using fruit juice concentrates.

Enhancing nutritional and functional properties of gluten-free Riceberry rice pasta supplemented with cricket powder using D-optimal mixture design

The study aimed to develop gluten-free Riceberry rice pasta supplemented with cricket powder using a twin-screw extruder, contributing valuable knowledge to increasing the nutritional value of pasta through sustainable practices, nutritional enhancement, and novel ingredients. The D-optimal mixture design, an effective statistical tool for optimizing formulation, was used to determine the optimal proportions of Riceberry rice flour (RF, 75–90%), cricket powder (CP, 5–20%), and xanthan gum (XG, 1–5%). The results showed that all predictive models were statistically significant with satisfactory coefficients of determination (R2 ≥ 0.75), and exhibited a non-significant lack of fit. The contour plot demonstrated that pasta with high RF, XG, and low CP contents had the lowest L* and highest a* values. The incorporation of XG increased firmness, while an increase in CP led to higher protein and fat contents in the pasta. A decrease in RF resulted in reduced total phenolic content (TPC), anthocyanin content (ATC), and 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) scavenging activity (AOA). The sensory evaluation scores of the pasta, in terms of the texture attribute and overall acceptance, decreased as the proportion of XG increased. The optimization analysis showed that the highest desirability was achieved with RF, CP, and XG at 84.10, 14.45, and 1.45%, respectively. The optimally desirable product had the following properties: L* at 22.60, a* at 4.80, b* at 4.78, firmness at 3.41 N, protein content at 12.57%, fat content at 4.68%, TPC at 360.13 mg GAE/100 g dw, ATC at 476.24 mg C3G/100 g dw, AOA at 25.10 mg TE/100 g dw, texture rating at 7.40, and overall acceptance rating at 7.01. This study highlights the potential of cricket powder as a protein source in gluten-free Riceberry rice pasta. It offers insights into the effects of ingredients on pasta quality and facilitates the development of nutritious and appealing gluten-free pasta.

Synthesis of PVA–MWCNTs–UMCNOs–starch crosslinked nanocomposite biodegradable films for the removal of methylene blue and Congo red dyes from wastewater

AbstractPlastic waste and wastewater are indeed significant environmental issues that have wide‐ranging impacts on ecosystems, human health, and the economy. This study outlines the synthesis of biodegradable polyvinyl alcohol (PVA)–starch polymeric films incorporating multiwalled carbon nanotubes (MWCNTs) derived from plastic waste and unzipped carbon nanotubes oxides (UMCNOs) as co‐filled nanomaterials. These films were formulated based on data generated using a mixture design method in Design Expert 13 software, resulting in the synthesis of 19 films utilizing five types of PVA–starch polymer solutions (Types A–E). The synthesized films underwent characterization to study their surface morphology via scanning electron microscope and Fourier transform infrared spectroscopy analysis. Subsequently, the films were individually subjected to vacuum filtration, through which cationic dyes such as methylene blue (MB) and anionic dye Congo red (CR) were passed in batches. The highest removal efficiency of 96.43% for MB dye was achieved, accompanied by an exceptionally high flux of 3146.78 LMH. Similarly, the maximum removal of 88.08% for CR was observed with a flux of 1512.66 LMH. The renewable active sites mechanism resulted in increased dye removal with every cycle. Results indicated efficient reusability, particularly when washed with ethanol–water solution.

Predictive modelling of HHV and LHV for sugar industry by-products: a study on sugarcane trash leaves, bagasse, and filter cake

ABSTRACT This study aimed to enhance the efficiency of by-product raw materials from the sugar industry for use as fuel. The approach involved developing an equation to calculate the higher heating value (HHV) for each type of raw material using a regression method. Additionally, a simplex-centroid mixture design (SCMD) was employed to estimate the lower heating value (LHV) based on the mixing ratios by weight of sugarcane trash leaves (SCL), sugarcane bagasse (SCB), and filter cake (FTC). The results demonstrated that the developed model accurately estimated the HHV for each raw material. The ultimate analysis showed high statistical appropriateness, with an R2 of 0.83. The standard error of estimation was 0.74 MJ/kg, and the mean absolute error was 0.76%. Furthermore, the SCMD effectively estimated the LHV of the SCL, SCB, and FTC mixture ratios, achieving an R2 of 99.77%. The evaluation and validation of the prediction equation revealed a mean absolute error of 7.57% and a mean bias error of 6.31%. The findings of this study can be used to enhance the combustion efficiency of sugar industry by-products for use as fuel by selecting the optimal mixing ratio for each type of raw material.

Mixture Experimentation in Pharmaceutical Formulations: A Tutorial.

Mixture experimentation is commonly seen in pharmaceutical formulation studies, where the relative proportions of the individual components are modeled for effects on product attributes. The requirement that the sum of the component proportions equals 1 has given rise to the class of designs, known as mixture designs. The first mixture designs were published by Quenouille in 1953 but it took nearly 40 years for the earliest mixture design applications to be published in the pharmaceutical sciences literature by Kettaneh-Wold in 1991 and Waaler in 1992. Since then, the advent of efficient computer algorithms to generate designs has made this class of designs easily accessible to pharmaceutical statisticians, although the use of these designs appears to be an underutilized experimental strategy even today. One goal of this tutorial is to draw the attention of experimental statisticians to this class of designs and their advantages in pursuing formulation studies such as excipient compatibility studies. We present sufficient materials to introduce the novice practitioner to this class of design, associated models, and analysis strategies. An example of a mixture-process variable design is given as a case study.

Key parameters and effects in image processing and aggregate–aggregate contact calculation of asphalt mixtures

At present, there is no standard way of acquiring images, addressing errors in various image processing steps, and analyzing skeleton contact parameters. To promote the application of image processing technique in the design, construction monitoring, and quality inspection of asphalt mixtures, this study investigates the key factors affecting the two-dimensional image processing accuracy of asphalt mixtures, and the impact of parameters on the contact structure of asphalt mixture skeleton during the contact analysis is studied. Finally, data accuracy requirements for image processing and contact analysis are proposed. The results indicate that the camera lens should be parallel to the slice with a tilt angle of less than 15°, the error of the passing rate of each sieve remains within 5.0 %. The smaller the aggregate particle size, the greater the impact of aggregate edge recognition error on the aggregate particle size, aggregates with a particle size greater than 2.36 mm are less affected by edge recognition error. For aggregate–aggregate contact analysis, it is appropriate to set the contact threshold to 0.23 times the minimum calculated particle size. To ensure the accuracy of image processing and contact analysis, at least 10 cross-sectional images should be prepared, and the average value can be taken as the representative value of the indices. The conclusions can improve the accuracy and reliability of image processing, providing more accurate guidance for the design and construction of asphalt mixture.

USE D-OPTIMAL MIXTURE DESIGN IN FORMULATION OF ONCHIDIID SLUG (ONCHIDIUM TYPHAE) INSTANT POWDER AS FUNCTIONAL FOODS

Onchidiid slug (Onchidium typhae) is an animal with bioactive compounds with high nutritional value and the potential to be used as a functional food product. The study aimed to optimize the onchidiid slug instant powder formula, analyze its proximate content, and determine its hedonic level. The formula of instant powder was made with a comparison of the composition of maltodextrin and dextrose based on the run in the D-Optimal Mixture Design (DMD) program, namely run 1 (20%:10%), run 2 (10%:20%), run 3 (13.33%: 16.67%), run 4 (16.67%:13.33%), run 5 (15%:15%), run 6 (12.5%:17.5%), run 7 (10%: 20%), run 8 (20%:10%). The powder is tested for water content and dissolution time, and the optimal formula is analyzed using One Sample T-Test in SPSS. The results showed that the composition of maltodextrin and dextrose significantly affected the characteristics of instant powder. The optimal instant powder formula combines 18.690% maltodextrin and 11.310% dextrose with a water content of 4.892% and a soluble time of 118.052 seconds. The results of statistical analysis in verifying the optimal formula show a p-value &gt; 0.05 (not significant). The results of the proximate content test were 8.21% water content, 0.42% ash content, 0.85% crude fiber, 3.54% protein, and 1.54%. They had a preference level in the like and acceptable in terms of color, taste, texture, and scent.