Multifactor Experimental Design Research Articles

Influence of Mesh Design and Surface Treatments on Particle Transport and Fate in a Vibration-Enhanced Flooded Bed Dust Scrubber

Respirable coal mine dust (RCMD) is one of the biggest occupational health hazards for underground coal miners. Dusty mining environments can cause long-term health problems, including pneumoconiosis and progressive massive fibrosis. The Mine Safety and Health Administration (MSHA) has recently revised regulations promoting enhanced dust mitigation technologies, which have sparked renewed interest in the development of dust mitigation technologies. The flooded bed dust scrubber (FBS) is one of the most widely used technologies; however, it is limited by technical challenges, the most notable being the potential to clog. Recent studies have shown that applying vibration to filter mesh can improve the overall efficiency of the scrubber and that the system can be readily integrated to existing continuous mining equipment using an energy harvesting approach. In this follow-up study, the impact of mesh design and surface modification on system efficiency was examined using different vibrating liquid-coated stainless-steel mesh panels in a laboratory-scale FBS. Based on the two-way interaction data from a multi-factor experimental design, the results show that the performance of the system can be optimized by using hydrophilic 20- or 30-layer filters and by excitation frequencies between 67 and 134 Hz. This laboratory study suggests that a 20-layer mesh screen with hydrophilic surface applications and optimized vibration parameters can perform similar to that of a 30-layer static mesh, which is typically used in industrial units.

The 1986 Annaburroo experimental grassland fires: data

Background In 1986, CSIRO conducted a large program of experimental fires in grassland at Annaburroo Station, Northern Territory, Australia, with the objective of quantifying the effect of fuel condition (load and height) on fire behaviour. Aims This paper provides the data collected during this program, representing a unique set of observations and measurements of large, free-burning experimental fires conducted in a multi-factor experimental design. Methods Data are collated by experimental burn plot, providing detailed measurements of weather (wind speed, air temperature, relative humidity), fuel state (load, height, moisture content, curing) and fire behaviour (rate of spread, flame depth, flame height, head fire width), as well as processed information (e.g. steady-state rate of spread). Data availability The data are made available for free download on the CSIRO Data Access Portal (https://data.csiro.au/collection/csiro:58746) and include detailed metadata descriptions of the data and their structure, also provided in this article. Conclusions We have made the data available for fire behaviour researchers around the world to use in their research under the Creative Commons Attributions licence. It is hoped they will analyse these data and extract new and innovative insights to help improve our understanding of wildland fires burning in grass fuels.

Four chemometric models enhanced by Latin hypercube sampling design for quantification of anti-COVID drugs: sustainability profiling through multiple greenness, carbon footprint, blueness, and whiteness metrics

Montelukast sodium (MLK) and Levocetirizine dihydrochloride (LCZ) are widely prescribed medications with promising therapeutic potential against COVID-19. However, existing analytical methods for their quantification are unsustainable, relying on toxic solvents and expensive instrumentation. Herein, we pioneer a green, cost-effective chemometrics approach for MLK and LCZ analysis using UV spectroscopy and intelligent multivariate calibration. Following a multilevel multifactor experimental design, UV spectral data was acquired for 25 synthetic mixtures and modeled via classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm-PLS (GA-PLS) techniques. Latin hypercube sampling (LHS) strategically constructed an optimal validation set of 13 mixtures for unbiased predictive performance assessment. Following optimization of the models regarding latent variables (LVs) and wavelength region, the optimum root mean square error of cross-validation (RMSECV) was attained at 2 LVs for the 210–400 nm spectral range (191 data points). The GA-PLS model demonstrated superb accuracy, with recovery percentages (R%) from 98 to 102% for both analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.0943, 0.1872) and (0.1926, 0.1779) for MLK and LCZ, respectively, as well bias-corrected mean square error of prediction (BCMSEP) of -0.0029 and 0.0176, relative root mean square error of prediction (RRMSEP) reaching 0.7516 and 0.6585, and limits of detection (LOD) reaching 0.0813 and 0.2273 for MLK and LCZ respectively. Practical pharmaceutical sample analysis was successfully confirmed via standard additions. We further conducted pioneering multidimensional sustainability evaluations using state-of-the-art greenness, blueness, and whiteness tools. The method demonstrated favorable environmental metrics across all assessment tools. The obtained Green National Environmental Method Index (NEMI), and Complementary Green Analytical Procedure Index (ComplexGAPI) quadrants affirmed green analytical principles. Additionally, the method had a high Analytical Greenness Metric (AGREE) score (0.90) and a low carbon footprint (0.021), indicating environmental friendliness. We also applied blueness and whiteness assessments using the high Blue Applicability Grade Index (BAGI) and Red–Green–Blue 12 (RGB 12) algorithms. The high BAGI (90) and RGB 12 (90.8) scores confirmed the method's strong applicability, cost-effectiveness, and sustainability. This work puts forward an optimal, economically viable green chemistry paradigm for pharmaceutical quality control aligned with sustainable development goals.

Evaluation of Cr(VI) Reducing Capability of Bacillus licheniformis DAS1 Using a Multifactor Experimental Approach

The current study is about detoxifying soil and water contaminated with toxic Cr(VI). To ensure that DAS1 could develop as well as possible, the pH was changed between 4 and 10. DAS1 showed its highest growth at pH 8, and at the same pH, it had an 85% potential to remediate by converting Cr(VI) to Cr(III). Immobilized bacteria increased the reduction of Cr(VI) to Cr(III) from the culture medium to 90.4%. The impact of glucose concentrations between 0.5 and 2.5 g.L-1 was examined. The greatest development was seen at pH 8 and 2 g.L-1 glucose concentration. The remediation potential was improved by up to 96% when the growing medium contained 200 mg.L-1 Cr(VI). The value of ks (0.434 g.L-1) demonstrated the substrate’s affinity for bacteria in accordance with the Monod equation, while μ max (0.090 h) demonstrated that DAS1 required 11.11 h for maximal growth. The multifactor experimental design was used to analyze mixed cultures of DAS1 and DAS2 in a 1:1 ratio, and it was determined that the X3Y2Z1 experiment design was best for completely removing Cr(VI) from the growing medium. By making pores using Na2EDTA, it was determined that the cell membrane’s impermeability did not cause Cr(VI) resistance in DAS1. The delayed lag phase indicated that the enzyme activity was inductive rather than constitutive.

Greenness and whiteness evaluated triple divisor-continuous wavelet transform spectrophotometric and advanced chemometric methods for the simultaneous determination of four photoinitiators in saliva and intravenous injection solution

Migration of photoinitiators from packaging materials and dental products in saliva presents an extensive real damage, particularly that emerges from packed food and dental products in saliva or from polyethylene packaging of the intravenous solutions which is extremely dangerous to human health and to the environment. In line with this, environmentally evaluated, advanced and reliable triple divisor combined with continuous wavelet transform (CWT) spectrophotometric method and nine chemometric methods were established and validated for the concurrent determination of four photoinitiators: Benzophenone, 4-Hydroxyacetophenone, Benzoin, and Diphenyliodonium hexafluorophosphate in saliva and in intravenous injection solutions. To our knowledge, this is the first spectrophotometric method for the analysis of the cited photoinitiators as well as the primary white method for the examination of those photoinitiators. Also, in this paper, the triple divisor method is combined with continuous wavelet transform for the first time. Triple divisor–CWT peak amplitudes were defined at 260.0, 275.8, 262.2 and 231.2 nm over a linear dynamic range of 0.50–13.00, 0.80–10.00, 1.5–30.00 and 1.00–28.00 μg/mL for Benzophenone, 4-Hydroxyacetophenone, Benzoin and Diphenyliodonium hexafluorophosphate, respectively. The nine chemometric methods incorporate the application of principle component regression (PCR), orthogonal partial least square (OPLS), ridge regression (RR) and artificial neural networks (ANN) methods along with different variable selections including genetic algorithm (GA-PCR, GA-OPLS and GA-RR) and interval selections (Interval-PCR and Interval-OPLS). Regarding the adopted chemometrics models, a multivariate multifactor experimental design based on the calibration curve range of the studied photoinitiators was considered to construct a calibration set consisting of eighteen mixtures while using seven mixtures for the validation set. The proposed methods were demonstrated to be sensitive, accurate, precise and reproducible throughout the validation process. The acquired results revealed that the proposed spectrophotometric and chemometric approaches could be effectively applied for the regular analysis of the cited photoinitiators in biological fluids as saliva and in intravenous solutions. The greenness of the proposed methods was assessed using two green metric tools enclosing Analytical Eco-scale & Analytical Greenness Assessment (AGREE), and a White Analytical Chemistry tool (WAC).

Analysis of the Factors Influencing the Trailing Infrared Characteristics of Underwater Vehicles under Surge Conditions Using the Orthogonal Method

With its benefits of efficiency and speed, the orthogonal experimental design method is currently often employed in many domains for multi-factor experimental design. In this study, the wind–wave–flow multifunctional experimental flume was used to replicate surge circumstances using the orthogonal technique in order to investigate the contributing factors and their interactions on the sea surface IR characteristics of underwater vehicles’ wakes. According to the correlation analysis, the height of the swell and the surface temperature difference of the wake had only a weak negative correlation, while the dive depth of the underwater vehicle and the surface temperature difference of the wake had a significant negative correlation (p = −0.833). It was tentatively concluded that the surface temperature differential of the wake was found to be more sensitive to dive depth than to swell height. An investigation of the impact of trailing IR features can use this finding as a pertinent reference.

The role of inorganic-phosphate, potassium and magnesium in yeast-flavour formation

Inorganic-phosphate, potassium, and magnesium are key-minerals required for yeast growth, metabolism, and survival, the present work investigated its impact in yeast-flavour formation using a multi-factor experimental design, which was used to generate a range of phosphorous-potassium-magnesium resulting in a 28-point D-optimal design. Samples were evaluated using HPLC (ethanol), GC–MS (aroma), and CountStar (total yeast cell). Results revealed that yeast requires a minimal amount of inorganic-phosphate, potassium, and magnesium (250, 500, and 70 mg/L, respectively) to support yeast-growth and ethanol/flavour formation. Inorganic-phosphate was important for fatty acid esters formation/short chain fatty acid (SCFA) reduction. Potassium was important in the formation of acetate esters/higher alcohols. Magnesium was the most important inorganic element for ester formation/SCFA reduction; furthermore, ethanol production is magnesium-dependent. In conclusion, inorganic phosphate, potassium and magnesium play an important role in yeast-growth, esters and higher alcohols formation; and SCFA reduction. Ethanol formation is Mg-dependent.

Optimization of Direct Blue 71 sorption by organic rich-compost following multilevel multifactor experimental design

Removal of a troublesome textile dye, Direct Blue 71 (DB71) from water by a food waste compost was assessed in the current study. Since compost dye sorption is a multi-factor process influenced by mass, pH, concentration, temperature, contact time, and salinity, the cumulative influence of all parameters on DB71 removal was examined following an optimal multilevel multifactor experimental design. The process had to be presented using both linear and interaction terms, according to the variables analysis: Dye sorption = –0.050Mass + 0.122Conc–0.114pH + 0.132Time – 0.074Temp + 0.056Sal + 0.103Mass × Conc + 0.226 Mass × pH – 0.257Mass × Time – 0.112Mass × Temp – 0.041Mass × Sal + 0.008Conc × pH + 0.100Conc × Time + 0.089Conc × Temp + 0.167Conc × Sal – 0.245pH × Time – 0.231pH × Temp – 0.123pH × Sal + 0.358Tim × Temp + 0.355Tim × Sal – 0.045Temp × Sal (R2 = 0.9241) Salinity and pH were positively correlated with concentration, and contact time with temperature and salinity, to get better dye uptake. The optimal conditions for dye removal were the following: solid:liquid ratio 1:375, pH 3.0, initial dye concentration 400 mg L−1, contact time 240 min, salinity 0.6 M NaCl, temperature 50 °C. At the optimum combination of factors, equilibrium sorption isotherm and sorption kinetics were studied. Kinetic analysis indicated high sorption rate 4.0 mg g−1 min−1 while 28% of maximum capacity was reached within the first 10 min of interaction. Sorption isotherm has L2-shape which reflected surface saturation at high solute concentration with low competition with solvent molecules, with a maximum sorption capacity of 95.4 mg g−1. In column experiments performed at bed depth 5.1–12.8 cm, flow rate 1.0–2.0 mL min−1 and influent concentration 10–20 mg L−1, sorption capacity was 19.6 mg g−1, which represents 21% of the maximum capacity at equilibrium conditions. IR analysis of dye-loaded-compost confirmed the contribution of hydrophobic-hydrophobic forces in the sorption process.

An innovative spectroscopic approach for qualitative and quantitative evaluation of Mb-CO from myoglobin carbonylation reaction through chemometrics methods

In this work, an innovative approach using K-means and multivariate curve resolution-purity based algorithm (MCR-Purity) for the evaluation and quantification of carboxymyoglobin (Mb-CO) formation from Deoxy-Myoglobin (Deoxy-Mb) was presented. Through a multilevel multifactor experimental design, samples with different concentrations of Mb-CO were created. The UV–Vis spectra of these samples were submitted to K-means analysis, finding 3 clusters. The mean spectra of the clusters were extracted and it was possible to detect 2 totally differentiable groups through peaks 423 and 434 nm, which are wavelengths related to the Mb-CO and Deoxy-Mb components, respectively. The spectral data were subjected to MCR-Purity analysis. The MCR-Purity result successfully described the analyzed reaction, explaining more than 99.9% of the variance (R2) with a LOF of 1.43%. Then, a predictive model of MbCO was created through the linear relationship between MCR-Purity contributions and known concentrations of MbCO. The performance parameters of the created predictive model were R2CV = 0.98, RMSECV = 0.58 and RPDcv = 7.8 for the training set, and R2P = 0.98, RMSEP = 0.7 and RPDp = 6.8 for the test set. Thus, the predictive model presented an excellent performance considering that the Mb-CO variation is comprised between 0 and 21 µM. Therefore, these results demonstrate that the application of the proposed strategy to the analysis of spectral data presenting overlapping bands is feasible and robust.

Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

Combining rapid 2D NMR experiments with novel pre-processing workflows and MIC quality measures for metabolomics.

The use of 2D NMR data sources (COSY in this paper) allows to reach general metabolomics results which are at least as good as the results obtained with 1D NMR data, and this with a less advanced and less complex level of pre-processing. But a major issue still exists and can largely slow down a generalized use of 2D data sources in metabolomics: the experiment duration. The goal of this paper is to overcome the experiment duration issue in our recently published MIC strategy by considering faster 2D COSY acquisition techniques: a conventional COSY with a reduced number of transients and the use of the Non-Uniform Sampling (NUS) method. These faster alternatives are all submitted to novel 2D pre-processing workflows and to Metabolomic Informative Content analyses. Eventually, results are compared to those obtained with conventional COSY spectra. To pre-process the 2D data sources, the Global Peak List (GPL) workflow and the Vectorization workflow are used. To compare this data sources and to detect the more informative one(s), MIC (Metabolomic Informative Content) indexes are used, based on clustering and inertia measures of quality. Results are discussed according to a multi-factor experimental design (which is unsupervised and based on human urine samples). Descriptive PCA results and MIC indexes are shown, leading to the direct and objective comparison of the different data sets. In conclusion, it is demonstrated that conventional COSY spectra recorded with only one transient per increment and COSY spectra recorded with 50% of non-uniform sampling provide very similar MIC results as the initial COSY recorded with four transients, but in a much shorter time. Consequently, using techniques like the reduction of the number of transients or NUS can really open the door to a potential high-throughput use of 2D COSY spectra in metabolomics.

Comprehensive screening of 63 coloring agents in cosmetics using matrix solid-phase dispersion and ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap high-resolution mass spectrometry

An analytical methodology for comprehensive screening of 63 coloring agents of great concern for regulatory control in cosmetics has been established using ultra-high-performance liquid chromatography (UHPLC) coupled with quadrupole-Orbitrap high-resolution mass spectrometry (Q-Orbitrap HRMS). An effective, rapid, and simple sample pretreatment protocol with low sample and reagent consumption was developed based on matrix solid-phase dispersion (MSPD). The selection of the most suitable extraction conditions was made using statistical tools of factorial multifactor experimental design and analysis of variance (ANOVA). In the final conditions, 0.1 g of cosmetic sample was blended with 0.3 g of anhydrous sodium sulfate and 0.4 g of sand, and the MSPD column was eluted with 2 mL of methanol. The extract was analyzed by UHPLC-Q-Orbitrap HRMS under synchronous full-scan MS and data-dependent MS/MS (full-scan MS1/dd-MS2) acquisition mode. The mass resolution was set to 70,000 FWHM (full width at half maximum) for full-scan MS1 and 17,500 FWHM for dd-MS2 stage with the experimentally measured mass deviations of less than 3 ppm (parts per million) for quasi-molecular ions and 5 ppm for characteristic fragment ions for each individual analyte. An accurate-mass database and a mass spectral library were built in house for searching the 63 target compounds. Broad screening was conducted by comparing the experimentally measured exact mass of precursor and fragment ions, retention time, isotopic pattern, and ionic ratio with the accurate-mass database and by matching the acquired MS/MS spectra against the mass spectral library. Method performance was evaluated in terms of limits of detection (LODs), limits of quantitation (LOQs), linearity, precision, recovery, and matrix effect. The UHPLC-Q-Orbitrap HRMS approach was applied for the simultaneous analysis of 63 target coloring agents in 69 genuine cosmetic samples. Eleven legally prohibited coloring agents were detected in 26 cosmetic samples in total. The proposed method exhibited great potential for high-throughput, sensitive, and reliable screening of multi-class coloring agent substances in cosmetics.

ПРИМЕНЕНИЕ АЛГОРИТМА ПРЫГАЮЩИХ ЛЯГУШЕК ДЛЯ ОПТИМИЗАЦИИ ПО СТОИМОСТНЫМ (ВРЕМЕННЫМ) ЗАТРАТАМ ПЛАНОВ ПОЛНОГО ФАКТОРНОГО ЭКСПЕРИМЕНТА

The purpose of this article is to further develop the methodology for the optimal cost (time) costs of experiment planning, which includes a set of methods for optimizing experiment plans and software and hardware for their implementation. The object of study: the optimization processes for the cost-based plans of multivariate experiments. The subject of research: the cost-optimization method of experimental design plans, based on the use of shuffled frog-leaping method. Experimental research methods are increasingly used to optimize production processes. Planning an experiment allows you to get their mathematical models with minimal cost and time costs. At the same time, a method and a program in the C ++ programming language were developed for constructing optimal or close to optimal plans for a full factorial experiment applying the shuffled frog-leaping algorithm. This allows you to automate the process of solving the problem, reduce the time to develop optimal plans for the experiment, increase the reliability of the results, reduce the time and cost of the experiments. Its effectiveness is shown in comparison with other methods for optimizing multi-factor experimental designs. The efficiency and effectiveness are confirmed by the coincidence or approximation of the optimal plans obtained by this method and the method of complete enumeration. A number of technological objects are presented on which the operability of the developed method and software was tested, namely: fuel consumption in an internal combustion engine, welding of small thickness plates, production of parts by hot stamping, as well as the process of servicing numerically controlled machines. A comparative analysis of the methods for the synthesis of cost-optimal (time) expenditure plans for a full factorial experiment was carried out and the effectiveness of the shuffled frog-leaping method was shown. It is shown that the difficult task of reducing material and time costs when conducting experimental studies can be solved using the proposed method and the software implementing it.

Bayesian negative binomial regression for differential expression with confounding factors.

Rapid adoption of high-throughput sequencing technologies has enabled better understanding of genome-wide molecular profile changes associated with phenotypic differences in biomedical studies. Often, these changes are due to multiple interacting factors. Existing methods are mostly considering differential expression across two conditions studying one main factor without considering other confounding factors. In addition, they are often coupled with essential sophisticated ad-hoc pre-processing steps such as normalization, restricting their adaptability to general experimental setups. Complex multi-factor experimental design to accurately decipher genotype-phenotype relationships signifies the need for developing effective statistical tools for genome-scale sequencing data profiled under multi-factor conditions. We have developed a novel Bayesian negative binomial regression (BNB-R) method for the analysis of RNA sequencing (RNA-seq) count data. In particular, the natural model parameterization removes the needs for the normalization step, while the method is capable of tackling complex experimental design involving multi-variate dependence structures. Efficient Bayesian inference of model parameters is obtained by exploiting conditional conjugacy via novel data augmentation techniques. Comprehensive studies on both synthetic and real-world RNA-seq data demonstrate the superior performance of BNB-R in terms of the areas under both the receiver operating characteristic and precision-recall curves. BNB-R is implemented in R language and is available at https://github.com/siamakz/BNBR. Supplementary data are available at Bioinformatics online.

Applying the methodology of Design of Experiments to stability studies: a Partial Least Squares approach for evaluation of drug stability

The aim of the present research is to show that the methodology of Design of Experiments can be applied to stability data evaluation, as they can be seen as multi-factor and multi-level experimental designs. Linear regression analysis is usually an approach for analyzing stability data, but multivariate statistical methods could also be used to assess drug stability during the development phase. Data from a stability study for a pharmaceutical product with hydrochlorothiazide (HCTZ) as an unstable drug substance was used as a case example in this paper. The design space of the stability study was modeled using Umetrics MODDE 10.1 software. We showed that a Partial Least Squares model could be used for a multi-dimensional presentation of all data generated in a stability study and for determination of the relationship among factors that influence drug stability. It might also be used for stability predictions and potentially for the optimization of the extent of stability testing needed to determine shelf life and storage conditions, which would be time and cost-effective for the pharmaceutical industry.

Improved information pooling for hierarchical cognitive models through multiple and covaried regression

Cognitive process models are fit to observed data to infer how experimental manipulations modify the assumed underlying cognitive process. They are alternatives to descriptive models, which only capture differences on the observed data level, and do not make assumptions about the underlying cognitive process. Process models may require more observations than descriptive models however, and as a consequence, usually fewer conditions can be simultaneously modeled with them. Unfortunately, it is known that the predictive validity of a model may be compromised when fewer experimental conditions are jointly accounted for (e.g., overestimation of predictor effects, or their incorrect assignment). We develop a hierarchical and covaried multiple regression approach to address this problem. Specifically, we show how to map the recurrences of all conditions, participants, items, and/or traits across experimental design cells to the process model parameters. This systematic pooling of information can facilitate parameter estimation. The proposed approach is particularly relevant for multi-factor experimental designs, and for mixture models that parameterize per cell to assess predictor effects. This hierarchical framework provides the capacity to model more conditions jointly to improve parameter recovery at low observation numbers (e.g., using only 1/6 of trials, recovering as well as standard hierarchical Bayesian methods), and to directly model predictor and covariate effects on the process parameters, without the need for post hoc analyses (e.g., ANOVA). An example application to real data is also provided.

Application of multifactor experimental design for optimizing the conditions of atomic emission determination of noble metals using a double-jet arc plasmatron

Conditions for atomic emission analysis using a Grand spectrometer equipped with a double-jet arc plasmatron as the excitation source are optimized. The impact of the plasma-forming and carrier gas flow rate on the analytic signal of precious metals is studied. On the basis of the mathematical method of multifactor experimental design, the influence of various factors on the intensity of the spectral lines of analytes are ascertained, and optimally compromise conditions for the determination of noble metals are chosen. An evaluation of the analytical capabilities of the Grand spectrometer is performed, and the detection limits for precious metals (PM) under chosen optimum conditions are calculated.

Full spectrum and selected spectrum based multivariate calibration methods for simultaneous determination of betamethasone dipropionate, clotrimazole and benzyl alcohol: Development, validation and application on commercial dosage form

Five different chemometric methods were developed for the simultaneous determination of betamethasone dipropionate (BMD), clotrimazole (CT) and benzyl alcohol (BA) in their combined dosage form (Lotriderm® cream). The applied methods included three full spectrum based chemometric techniques; namely principal component regression (PCR), Partial Least Squares (PLS) and Artificial Neural Networks (ANN), while the other two methods were PLS and ANN preceded by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. A multilevel multifactor experimental design was adopted for proper construction of the models. A validation set composed of 12 mixtures containing different ratios of the three analytes was used to evaluate the predictive power of the suggested models. All the proposed methods except ANN, were successfully applied for the analysis of their pharmaceutical formulation (Lotriderm® cream). Results demonstrated the efficiency of the four methods as quantitative tool for analysis of the three analytes without prior separation procedures and without any interference from the co-formulated excipient. Additionally, the work highlighted the effect of GA on increasing the predictive power of PLS and ANN models.

Wear behaviour of DMD-generated high-strength steels using multi-factor experiment design on a pin-on-disc apparatus

Direct metal deposition (DMD) is a laser-based powder-fed-type additive manufacturing technology to create solid and porous structures from high-strength metallic alloys that can be used as coatings, foams and sandwiched structures, and as highly stressed components. This investigation evaluates the quality and capability of DMD-generated high-strength steel alloy parts to withstand severe sliding wear against mild steel counter face involving multi-factor experimental design. The specimens are produced by DMD as cylindrical pins and annular paddings coated on mild steel substrate. Experiments were conducted on a pin-on-disc wear tester under six different experimental regimes designed to take into account different material conditions of H13 tool steel and 316-L stainless steel. The wear characteristics were evaluated following ASTM standard G99 under dry and severe conditions involving direct metal to metal contact. The wear scars and tracks reveal oxidative flaking, plastic deformation and micro-ploughing of the surfaces depending on specific test condition. This study focuses on the evolution of co-efficient of friction and its variation while changing the material and operating conditions. The study also investigates the relationship of primary and secondary wear factors in terms of wear loss from the DMD specimen and the energy dissipation during sliding wear. The results show a strong and consistent behaviour of laser-generated specimen and exhibit very little signs of material degradation and flaking under high loading conditions.

NADH Oxidation in a Microreactor with an Oscillating Magnetic Field

In this study, magnetic nanoparticles (MNPs) of maghemite (γ-Fe2O3) were synthesized and characterized. The method of multifactor experimental design and evolutionary operation (EVOP) was used to optimize immobilization of the alcohol dehydrogenase (ADH) enzyme on MNPs. Optimal operating conditions for the immobilization process were determined (γADH = 0.08 mg/mL, 2% glutaraldehyde for surface activation, t = 28 h), and in such conditions, a specific activity of S.A. = 118 ± 6 U/mg and immobilization efficiency of η = 84.97 ± 3.67% were achieved. Compared to the native enzyme, ADH immobilized on MNPs retained 66.45 ± 3.66% of the initial activity. ADH immobilized on MNPs at optimal conditions was used as a biocatalyst for model reaction—NADH oxidation. NADH oxidation was performed in two different magnetic microreactor configurations: (1) microreactor equipped with permanent square magnets and (2) microreactor equipped with an electromagnet and an oscillating magnetic field that enables magnetic particles...