Multiple Response Surface Methodology Research Articles

Optimisation of Lutein Extraction From Gac (Momordica cochinchinensis) Fruit Peel and Evaluation of In Vitro Eye Protective Effects of the Extracts

Lutein is a macular biopigment that can protect the retina from oxidative stress induced by chronic exposure to light and oxidants. The peel of gac fruit (Momordica cochinchinensis) contains high levels of lutein, but the extraction optimisation parameters are complex, requiring saponification, and the effects on ocular bioactivity are not well understood. This study is aimed at optimising parameters for lutein extraction validated using HPLC from gac peel to determine their protective effects on oxidatively stressed retinal cells. The factors investigated for optimisation were KOH concentration, time, and temperature using (1) single factor, (2) multiple factor response surface methodology, and (3) predictive modelling. The optimum conditions for lutein extraction using single factors were 9% KOH for 3 h at 60°C, multiple factors were 9% KOH for 3 h at 45°C, and predictive modelling was 18% KOH for 1.9 h at 39.9°C, with the maximum extraction of lutein at 12.37, 15.64, and 17.4 mg/g DW, respectively. The extracted lutein purity and chemical composition were compared using HPLC and FTIR and indicated high purity and similarities in all extracts including the commercially purified lutein. Lutein extracted exhibited benefits on oxidatively stressed retinal pigment epithelium (RPE) cells that reversed damage to baseline unstressed levels exceeding 80% viability, similar to purified lutein standards. The feasibility of gac lutein and extraction optimisation were discussed for commercial translational opportunities as a medicinal bioproduct with a focus on eye benefits.

Process Optimization for Syngas Production from the Dry Reforming of Methane over 5Ni+3Sr/10Zr+Al Catalyst Using Multiple Response Surface Methodology

Abstract5Ni+3Sr/10Zr+Al catalyst is synthesized using the impregnation method, characterized, and tested for dry reforming of methane. The influence of reaction temperature, feed ratio (CO2/CH4), and gas hour space velocity are examined using multiple response surface methodology through three factors in, a four‐level central composite design. Second and higher‐order regression models are applied to evaluate the interaction between the process parameters and responses. The results indicate that the reaction temperature is the most influential followed by the space velocity, while the feed ratio has a weak effect. The optimum values that maximize each of the response variables are found to be the reaction temperature at 746 °C, the space velocity of 12 000 ccg−1h−1, and the feed ratio of 0.958. Under these conditions, the predicted CH4 and CO2 conversions are 86.83% and 92.27%, respectively. While the H2/CO ratio is 1.02. On the other hand, the experimental results match the predicted ones when the optimum predicted operating conditions are used for the process. A weight loss of <16% is obtained on the spent catalyst after 86 h on stream. This is attributed to the highly basic and oxidative nature of the ZrO2 co‐supported catalyst and indicates the suitability of the developed catalyst.

Interpretable artificial intelligence for advanced oxidation systems: Principle, operations and performance

Advanced oxidation processes have been widely studied and employed due to their potent mineralization capacity for pollutants. However, the intricate reaction mechanisms of these processes pose limitations for fitting and predicting performance. In this study, we comprehensively derived and assessed neural networks for three advanced oxidation methods: catalytic oxidation, catalytic wet air oxidation, and electrochemical oxidation. Our analysis encompassed multilayer perceptron principles, forward and back propagation process, strategies for handling overfitting, and performance evaluation matrices. Additionally, we utilized Bayesian optimization to probe the impact of network architecture on outcomes. Two conventional methods, multiple linear regression and response surface methodology, are employed for comparison. Our results demonstrate that neural networks exhibit more robust performance in fitting and predicting advanced oxidation processes as indicated by statistical indicators. Importantly, we tackle the "black box" issue of neural networks by incorporating the shapley additive explanations interpretable model of game theory to elucidate the impact of advanced oxidation features on outcomes. The superior performance of explainable artificial intelligence techniques implies their vast potential for broad applications in environmental science and technology.

Analysis of the Reliability of Subsea Clamp Connector Based on Multiple Response Surface Methodology

Analysis of the Reliability of Subsea Clamp Connector Based on Multiple Response Surface Methodology

Multiple response optimizations on the leached-spray-dried bancha green tea towards healthy ageing

Bancha is a popular type of green tea in Japan, rich in tea polyphenols (TPs) and has a more astringent aroma with a less aromatic and strong character that complements functional foods. The blanching process is used to extract TPs and remove unwanted microorganisms, as well as inhibit phenolic oxidation. This study proposed a green tea blanching process followed by spray drying the extracts with maltodextrin. Furthermore, it is focused on maximizing the major chemical components of green tea (i.e., catechins, caffeine, and phenolic contents) based on powder particle size obtained through Multiple Response Surface Methodology optimizations. The results show that the proposed model accurately predicts leached-spray dried green tea's total catechin and caffeine content, with a coefficient of 0.9475 and 0.8692, respectively. This process yielded composite desirability of 0.9751, while individual desirability yielded excellent results of 1.0000, 0.9188, 1.0000, and 0.9839 for catechin, caffeine, phenol content, and powder. The settings appear to yield functional results for entire responses. Due to the concerns in tropical skin nutrition applications, smaller particle size green tea can promote better adsorption than larger sizes.

Optimization of Ethanol-Based Extraction Process for Duliang Formula by Central Composite Design and Response Surface Methodology

Background: Compound medicines (traditional Chinese medicines [TCM] formulae) are the main means of treating diseases in TCM. Now, for the convenience of clinical use, there are a lot of Chinese patent medicines (proprietary Chinese medicine) on the market. However, the preparation of Chinese patent medicine needs to solve 2 key problems. One is as far as possible to keep crude drug's active ingredients, and the second is the cost control of industrialization; in this article, the extraction process for the proprietary Chinese medicine of the classic TCM formula, Duliang formula, has been studied effectively. Objective: To investigate the optimal ethanol-based extraction process for Duliang formula by central composite design and response surface methodology. Materials and methods: Central composite design was used to carry out a 3-factor 5-level study for a comprehensive evaluation of the extraction process of Duliang formula after normalization processing, with the ethanol concentration, solvent volume, duration, and times of extraction as investigation factors to consider; also, the contents of imperatorin, ferulic acid, and extract yield were determined as Quality Control indicators. Multiple linear regression, binomial fitting, and response surface methodology were used to analyze and determine the optimal ethanol-based extraction process for Duliang formula and predict its extraction parameters. Results: The determined optimal ethanol-based extraction process was thermal reflux extraction twice with 11 times of 90% ethanol for 2.5 h each time. The overall desirability for the “Normalization Value” evaluation was 0.7681, with a deviation of −0.99% (<5%) compared with the predicted value (0.7758). Conclusions: The determined ethanol-based extraction process for Duliang formula was easy and convenient, with stable, accurate, reliable parameters and reasonable predictability, and could serve as a reference for the industrial production of Duliang formula.

Estimating Rubber Covered Conveyor Belting Cure Times Using Multiple Simultaneous Optimizations Ensemble

Multiple response surface methodology (MRSM) has been the favorite method for optimizing multiple response processes though it has two weaknesses which challenge the credibility of its solutions. The first weakness is the use of experimentally generated small sample size datasets, and the second is the selection, using classical model selection criteria, of single best models for each response for use in simultaneous optimization to obtain the optimum or desired solution. Classical model selection criteria do not always agree on the best model resulting in model uncertainty. The selection of single best models for each response for simultaneous optimization loses information in rejected models. This work proposes the use of multiple simultaneous optimizations to estimate multiple solutions that are ensembled in solving a conveyor belting cure time problem. The solution is compared with one obtained by simultaneous optimization of single best models for each response. The two results were different. However, results show that it is possible to obtain a more credible solution through ensembling of solutions from multiple simultaneous optimizations.

Using criterion-based model averaging in two-input multiple response surface methodology problems

Using criterion-based model averaging in two-input multiple response surface methodology problems

The multiple-response modeling of heat-assisted, microwave-assisted and subcritical water extraction on selected phenolics from traditional plant species Teucrium montanum

The growing popularity of integration of functional compounds into food and pharmaceutical products has greatly influenced the intense investigation of aromatic plants as its notable sources. One of the insufficiently explored plant species—Mountain Germander (Teucrium montanum)—may represent a prominent source of biologically active components—polyphenols. Hence, to maximally enhance their recovery, conventional (HAE) and non-thermal extraction methods—microwave-assisted extraction (MAE) and subcritical water extraction (SWE) were employed by using multiple response surface methodology (RSM). Optimization of temperature, solid-to-solvent ratio, and extraction time was evaluated by measuring total phenolic content (TPC) and antioxidant capacity (DPPH and ABTS assays), with the further estimation of the extraction efficiency of two selected phenylethanoid glycosides present in final extracts—verbascoside and echinacoside determined by HPLC analysis. The optimum HAE, MAE, and SWE extraction conditions with the highest measured dependent variables were, as follows: 100 °C, 0.5 g/50 mL and 30 min, 90 °C, 0.5 g/50 mL and 9 min, 200 °C, 0.5 g/50 mL and 15 min, respectively. Regarding examined phenylethanoid glycosides, HAE extract showed to be its richest source (16.64 mg/g dw) while SWE was estimated as at least suitable extraction technique (5.42 mg/g dw). In conclusion, this study outlined the most appropriate extraction technique and conditions for the recovery of selected phenolic compounds using RSM and gave closely insight into the antioxidative potential of valuable ethnomedicinal plant from the Balkan peninsula—Teucrium montanum

Optimization and validation of ultrasensitive GC–MS/MS method to measure oxidatively induced DNA damage products and role of antioxidants in oxidation mechanism

Oxidation of DNA due to exposure to reactive oxygen species (ROS) is a major source of DNA damage. ROS induced damage to DNA plays an important role in some diseases such as various cancers, aging and neurodegenerative diseases. The detection of DNA oxidation products plays a major role in assessing the mutagenicity potential of specific exposure. The GC–MS/MS method was developed for the ultrasensitive determination of individual DNA damage products. The validation results revealed that the proposed method was reliable and sensitive. Multiple response surface methodology (MRSM) was used to optimize derivatization conditions of oxidatively DNA base damage products before GC–MS/MS analysis. The optimum derivatization conditions were determined as 40 min for derivatization time, 120 °C for derivatization temperature and 1.4 for BSTFA/pyridine ratio under nitrogen atmosphere. The effects of thymol, carvacrol and thymoquinone as antioxidants were investigated on oxidative DNA damage. The determination of the oxidatively induced DNA damage products was performed after adding DNA and antioxidants with different concentrations under oxidative stress. Eighteen DNA base damage products were analyzed simultaneously using GC–MS/MS. This study showed a significant decrease in the amount of DNA base damage products when the antioxidants were present in the medium.

Fermentative molecular biohydrogen production from cheese whey: present prospects and future strategy.

Waste-dependent fermentative routes for biohydrogen production present a possible scenario to produce hydrogen gas on a large scale in a sustainable way. Cheese whey contains a high portion of organic carbohydrate and other organic acids, which makes it a feasible substrate for biohydrogen production. In the present review, recent research progress related to fermentative technologies, which explore the potentiality of cheese whey for biohydrogen production as an effective tool on a large scale, has been analyzed systematically. In addition, application of multiple response surface methodology tools such as full factorial design, Box-Behnken model, and central composite design during fermentative biohydrogen production to study the interactive effects of different bioprocess variables for higher biohydrogen yield in batch, fed-batch, and continuous mode is also discussed. The current paper also emphasizes computational fluid dynamics-based simulation designs, by which the substrate conversion efficiency of the cheese whey-based bioprocess and temperature distribution toward the turbulent flow of reaction liquid can be enhanced. The possible future developments toward higher process efficiency are outlined.

An effective method for extracting anthocyanins from blueberry based on freeze-ultrasonic thawing technology

The aim of this study was to develop an effective method for extracting anthocyanins from blueberry Vaccinium spp. (ABVS) using freeze-ultrasonic thawing technology (FUTE). Various parameters including freezing time, ultrasonic time, ultrasonic temperature and liquid–solid ratio were optimized by a single-factor design and multiple response surface methodology. The amounts of extracted anthocyanin and cyanidin-3-O-glucoside were measured by UV and HPLC respectively. The maximum yield of anthocyanins was achieved by freezing the samples for 5.43 min in liquid nitrogen at the liquid–solid ratio of 24.07:1 mL/g, followed by ultrasonic thawing at 41.64 °C for 23.56 min. The yield and antioxidant effects of ABVS extracted using FUTE, ultrasound-assisted extraction (UAE) and freeze-thawing extraction (FTE) were compared in order to determine the overall efficacy of FUTE. In addition to the higher content, FUTE extracted ABVS showed greater ability to scavenge DPPH·, ABTS+ and superoxide anions, and inhibit lipid peroxidation compared to the ABVS extracted by UAE or FTE. The reducing power of the FUTE-derived ABVS was intermediate between that of the UAE and FTE samples. Taken together, FUTE can rapidly and effectively extract ABVS and retain its antioxidant capacity.

Process optimization of DBD plasma dry reforming of methane over Ni/La2O3[sbnd]MgAl2O4 using multiple response surface methodology

In this study, 10% Ni/La2O3MgAl2O4 nano-flake catalyst was synthesized, characterized and tested in a catalytic dielectric barrier discharge (DBD) plasma for dry reforming of methane (DRM). With design of experiment (DoE), the influence of process parameters namely (1) total feed flow rate (ml min−1), (2) feed ratio (CO2/CH4), (3) input power (W) and (4) catalyst loading (g) were examined using multiple response surface methodology (RSM) through a four-factor, five-level central composite design (CCD). Second-order regression models were applied for evaluating the interaction between the process parameters and responses. Input power (X3) and total feed flow rate (X1) were the two most influential process parameters followed by catalyst loading (X4) and feed ratio (X2). The experimental and predicted results from the optimum conditions fitted-well with less than ±5% margin of error. The possible dynamic interactions between the process variables were elucidated. The optimum values are feed flow rate = 18.8 ml min−1, feed ratio = 1.05, input power = 125.6 W and catalyst loading = 0.6 g. At these conditions, the predicted CH4 and CO2 conversions are 79.86% and 84.03%, respectively. The H2 and CO yields are predicted as 41.37% and 40.47%, respectively while H2/CO ratio is above unity. The calculated EE from the RSM model is predicted as 0.135 mmol kJ−1. Low carbon deposition observed on the spent catalyst is attributed to the highly basic and oxidative nature of the La2O3 co-supported catalyst.

A Practical Solution to the Small Sample Size Bias and Uncertainty Problems of Model Selection Criteria in Two-Input Process Multiple Response Surface Methodology Datasets

Multiple response surface methodology (MRSM) most often involves the analysis of small sample size datasets which have associated inherent statistical modeling problems. Firstly, classical model selection criteria in use are very inefficient with small sample size datasets. Secondly, classical model selection criteria have an acknowledged selection uncertainty problem. Finally, there is a credibility problem associated with modeling small sample sizes of the order of most MRSM datasets. This work focuses on determination of a solution to these identified problems. The small sample model selection uncertainty problem is analysed using sixteen model selection criteria and a typical two-input MRSM dataset. Selection of candidate models, for the responses in consideration, is done based on response surface conformity to expectation to deliberately avoid selection of models using the problematic classical model selection criteria. A set of permutations of combinations of response models with conforming response surfaces is determined. Each combination is optimised and results are obtained using overlaying of data matrices. The permutation of results is then averaged to obtain credible results. Thus, a transparent multiple model approach is used to obtain the solution which gives some credibility to the small sample size results of the typical MRSM dataset. The conclusion is that, for a two-input process MRSM problem, conformity of response surfaces can be effectively used to select candidate models and thus the use of the problematic model selection criteria is avoidable.

Predicting sugar content of candied watermelon rind during osmotic dehydration

Abstract Watermelon rind, comprising 35% of the fruit’s total mass, usually discarded due to its unappealing flavor and lack of sweetness. In this study, candied watermelon rind was prepared through osmotic dehydration at vacuum (VOD) and atmospheric pressure (OD). The effects of independent variables (sucrose concentration, immersion time and temperature) on the dependent variables, including solid gain (SG) and water loss (WL) of watermelon rind, were evaluated using multiple response surface methodology. The SG and WL increased with the increasing sucrose concentration, immersion time and temperature for both VOD and OD, but VOD greatly increased the mass transfer rate. The kinetic constants were obtained by fitting the data of SG and WL to the Fick’s diffusion model. The VOD had higher kinetic constants and dehydration efficiency indices than that of OD. Besides, the mass transfer model and RSM model were statistically compared by the coefficient of determination, root mean square error and absolute average deviation, based on the validation data set. The RSM and mass transfer models provide information about the osmotic dehydration for predicting the sugar content of candied product, and helping the designers and researchers find the best design parameters and the most effective process conditions.

Ultrasonic-Assisted Pelleting of Sorghum Stalk: Predictive Models for Pellet Density and Durability Using Multiple Response Surface Methodology

In the field of renewable energy, feedstock such as cellulosic biomass has been proposed as a renewable source of fuel to produce energy. However, the use of raw biomass as feedstock causes high costs in handling, transportation, and storage. Compressing raw cellulosic biomass into pellets significantly increases the density and durability of cellulosic biomass, reducing the transportation and handling costs of feedstock. To ensure high pellet quality, high pellet density and durability are desired during a compressing process. In this study, ultrasonic vibration-assisted (UV-A) pelleting, as a novel pelleting method, was applied to measure pellet density and durability during experiments. Response surface methodology (RSM) was employed to investigate the effects of pelleting time, ultrasonic power, and pelleting pressure on the pellet density and pellet durability. The model was validated by comparing the predictive results with experimental data and demonstrated a good predictive ability (R2 > 0.95). By employing a Derringer and Suich’s desirability function, our results suggest that the optimal pellet density and durability are 1239 kg/m3 and 93%, respectively, when the pelleting time was set to 44 s, the ultrasonic power was set to 50%, and pressure was set to 42 psi (289,580 Pa).

Statistical Analysis and Mathematical Modeling of Dry Sliding Wear Parameters of 2024 Aluminium Hybrid Composites Reinforced with Fly Ash and SiC Particles

The present studies are focused to analyze mathematically the dry sliding wear of 2024 aluminium alloy reinforced with fly ash (FA) and silicon carbide (SiC) particles with weight percentages of 5, 10 and 15. Both FA and SiC reinforcements are combined equally in weight proportion. Dry sliding wear values are computed using the pin-on-disc wear testing machine. The process parameters or factors like applied load, the weight percentage of FA and SiC, sliding time are identified, which are going to affect the wear of the sample under investigation. The experiments are designed based on Taguchi L27 orthogonal array. Mathematical/statistical methods such as Taguchi’s signal-to-noise ratio and Analysis of Variance (ANOVA) are the best tools, which are used to find out the influence of factors/parameters on the wear of composite. The analysis of experimental data using such methods is done using MINITAB 18 software considering smaller is better as a quality characteristic. Multiple linear regression and response surface methodology (RSM) mathematical models are used to develop the relation between wear with process factors. The results obtained from multiple linear regression model and RSM are compared. 2D contour plots are drawn for evaluation of wear at different set of process conditions. The wear mechanisms are studied using SEM pictures.

Statistical analysis of surface roughness in turning based on cutting parameters and tool vibrations with response surface methodology (RSM)

In this paper, we present an experimental study to determine the effect of the cutting conditions and tool vibration on the surface roughness in finish turning of 32CrMoV12-28 steel, using carbide cutting tool YT15. For these purposes, a linear quadratic model in interaction of connecting surface roughness (Ra, Rz) with different combinations of cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration, in radial and in tangential cutting force directions (Vy) and (Vz) is elaborated. In order to express the degree of interaction of cutting parameters and tool vibration, a multiple linear regression and response surface methodology are adopted. The application of this statistical technique for predicting the surface roughness shows that the feed rate is the most dominant factor followed by the cutting speed. However, the depth of the cut and tool vibrations have secondary effect. The presented models have some interest since they are used in the cutting process optimization.

Improvement in the Design of Welded Joints of EN 235JR Low Carbon Steel by Multiple Response Surface Methodology

Gas metal arc welding (GMAW) is a joining process that is controlled by several inputs or welding parameters. However, speed, current and voltage are the parameters that are most frequently used in setting this process. Cord area, yield stress, tensile strength, residual stresses, hardness and roughness are considered to be outputs or welded joints parameters. They are widely used when the design requirements are based on the cost, manufacturing speed, strength and surface finish. This paper seeks to determine the relationship between the welding parameters and the welded joint parameters of speed, current and voltage in butt joints (X-groove) of EN 235JR by the response surface method (RSM). The optimal joints when considering the design requirements of cost, manufacturing speed, strength and surface finish were achieved by using the multi-response surface (MRS). The optimal welding parameters reached when considering the design requirements of cost were 140.593 amps, 8.192 mm/s and 29.999 volts, respectively, whereas the design requirements of manufacturing speed were 149.88 amps, 9.261 mm/s and 29.999 volts. Finally, the welding parameters for the design requirements of joint strength and surface finish were 149.086 amps, 7.139 mm/s and 28.541 volts and 150.372 amps, 8.561 mm/s and 29.877 volts, respectively.

DNA Yield and Concentration Modeling by Age, A260/280 and A260/230 Ratios Using Multiple Linear regression (MLR), Bootstrap and Response Surface Methodology (RSM).

30Jun 2016 DNA Yield and Concentration Modeling by Age, A260/280 and A260/230 Ratios Using Multiple Linear regression (MLR), Bootstrap and Response Surface Methodology (RSM). Siti Nazihahasma Hassan , Wan Suriana Wan Ab Rahman , Wan Muhamad Amir W Ahmad , Suharni Mohamad , Rosline Hassan and Selamah Ghazali. School of Dental Sciences. School of Medical Sciences.