Design Of Experiments Approach Research Articles

Impact of probiotics, prebiotics, and synbiotics on digestive enzymes, oxidative stress, and antioxidant defense in fish farming: current insights and future perspectives

There has been a surge of research in the aquaculture industry investigating probiotic, prebiotic, and synbiotic interventions on the physiological mechanisms of fish, specifically digestive enzymes, oxidative stress, and antioxidant defense. In fish, probiotics have been shown to improve nutrient utilization and growth performance by stimulating digestive enzymes. Meanwhile, probiotics, prebiotics and synbiotics have also been studied for their ability to modulate oxidative stress and antioxidant defense mechanisms in fish, highlighting their multifaceted health benefits. This review identified current trends, research gaps, and future considerations in this evolving field. Although promising findings have been made, a significant research gap exists in understanding the specific role of probiotics prebiotics, and synbiotics in modulating digestive enzymes, oxidative stress, and antioxidant defense systems in a variety of fish species. As this study investigate into the existing body of literature, it becomes evident that while certain aspects of these interactions have been elucidated, a nuanced and comprehensive understanding still needs to be discovered. The variations in experimental design, species-specific responses, and the lack of standardized methodologies contribute to the complexity of the field. Digestive physiology and antioxidant defense mechanisms vary among different fish species, so future research should focus on species-specific responses to probiotic, prebiotic, and synbiotic formulations. It will also be possible to establish robust correlations between dietary interventions and observed effects through a systematic experimental design and methodology approach. Accordingly, further research is needed to understand the interactions between probiotics, prebiotics, and synbiotics in fish and digestive enzymes, oxidative stress, and antioxidant defense. Identifying research gaps and adopting standardized methodologies can help develop tailored strategies to optimize aquaculture fish health and growth performance.

An Investigation into the Effects of Processing Factors on the Properties and Scaling-Up Potential of Propranolol-Loaded Chitosan Nanogels.

Chitosan-triphosphate (TPP) nanogels are widely studied drug delivery carrier systems, typically prepared via a simple mixing process. However, the effects of the processing factors on nanogel production have not been extensively explored, despite the importance of understanding and standardising such factors to allow upscaling and commercial usage. This study aims to systematically evaluate the effects of various fabrication and processing factors on the properties of nanogels using a Design of Experiment approach. Hydrodynamic size, polydispersity index (PDI), zeta potential, and encapsulation efficiency were determined as the dependent factors. The temperature, stirring rate, chitosan grade, crosslinker choice, and the interaction term between temperature and chitosan grade were found to have a significant effect on the particle size, whereas the effect of temperature and the addition rate of crosslinker on the PDI was also noteworthy. Moreover, the addition rate of the crosslinker and the volume of the reaction vessel were found to impact the encapsulation efficiency. The zeta potential of the nanogels was found to be governed by the chitosan grade. The optimal fabrication conditions for the development of medium molecular weight chitosan and TPP nanogels included the following: the addition rate for TPP solution was set at 2 mL/min, while the solution was then stirred at a temperature of 50 °C and a stirring speed of 600 rpm. The volume of the glass vial used was 28 mL, while the stirrer size was 20 mm. The second aim of the study was to evaluate the potential for scaling up the nanogels. Size and PDI were found to increase from 128 nm to 151 nm and from 0.232 to 0.267, respectively, when the volume of the reaction mixture was increased from 4 to 20 mL and other processing factors were kept unchanged. These results indicate that caution is required when scaling up as the nanogel properties may be significantly altered with an increasing production scale.

Solubilization Improvement using Non-Ionic Surfactants and Phospholipids Optimized by An Experimental Design: Case of Albendazole

The solubilization of insoluble or poorly water-soluble drugs is one of the major factors taken into account in the formulation of oral solutions and parenteral forms. The goal of this study is to evaluate the utility of mixture design in enhancement water solubility of Albendazole by the use of phospholipids and nonionic surfactants that are well tolerated by the parenteral route.in this work, a design of experiments approach was tested using a mixture design of nonionic surfactants (tween® 80 and Solutol® HS 15), A phospholipid (Lipoid® S75) and Albendazole. Solubility determination was based on the analysis of samples absorbance at 308nm. A particles size measurement was conducted using a dynamic light scattering at the point showing the maximum of solubility. these experiments show an improving of solubility, up to 90% with runs 1, 3 and 6 compared to the solubility of albendazole in water, without excipients, examined by run 7. Indeed, the analysis of the design space showed that the solubility of albendazole is affected by the association of tween® 80, Solutol® HS 15 and Lipoid® S75. Run 3 containing 1% of albendazole, 1,5% of tween® 80, 1, 5% of Solutol® HS 15 and 0,38% of Lipoid® S75 and water q.s. for 100% w/w improved albendazole solubility by about 90%, and showed an average particles size of 11nm.

An optimization-free Fisher information driven approach for online design of experiments

Developing mathematical models used to elucidate reaction kinetics plays a crucial role in the design, control, and optimization of chemical processes. One of the most challenging tasks in kinetic model identification is the precise estimation of unknown kinetic model parameters. This challenge can be effectively addressed through the application of Model-Based Design of Experiments (MBDoE) techniques, which enable the design of experiments facilitating precise model parameter estimation with minimal runs and analytical resources. Nevertheless, MBDoE techniques rely on an optimization procedure that is susceptible to parametric uncertainty, making the design procedure computationally intensive and prone to issues of local optimality. MBDoE techniques are also employed in online procedures to expedite the identification of kinetic models in autonomous platforms. As a result, ensuring rapid convergence becomes imperative to mitigate numerical issues during operational processes. In this paper a Fisher Information Matrix Driven (FIMD) approach is introduced to tackle these challenges. The methodology integrates a sampling-based experimental design approach with experiment ranking based on FIM to select the most informative experiment at each iteration. The effectiveness of the proposed design methodology is examined and discussed via two different case studies of increasing complexity: a fed-batch reactor in which the fermentation of baker’s yeast is carried out and a nucleophilic aromatic substitution in a flow reactor system.

Experimental design approach for the quantitative analysis of multicomponents by single marker and HPLC fingerprinting of Thunbergia laurifolia aqueous extract.

Thunbergia laurifolia is used in traditional Thai medicine to reduce fever and treat mouth ulcers. However, the quantitative analysis of chemical markers has not yet been officially defined. The objective of this study is to develop a high-performance liquid chromatography (HPLC) method using a design of experiment (DoE) for the quantitative analysis of multicomponents by single marker (QAMS) and fingerprinting of the T. laurifolia aqueous extract. Critical variables were screened using a two-level fractional factorial design, followed by the optimization of the selected variables using a central composite design. The validated method was applied for quality assessment based on QAMS and fingerprinting of the extract. Optimum conditions of DoE for the analysis of caffeic acid, vicenin-2, and rosmarinic acid were determined. The relative correction factors for caffeic acid and vicenin-2 were calculated using rosmarinic acid as an internal reference standard, and their contents in 30 samples were determined. The differences between the external standard method (ESM) and QAMS were compared. No significant difference was observed in the quantitative determination, proving the consistency QAMS and ESM. HPLC fingerprints of T. laurifolia were established with 8 of 12 characteristic peaks that were structurally characterized using HPLC-diode array detection-electrospray ionization/tandem mass spectrometry. The similarity of the fingerprints in all samples was ≥0.74, and the pattern recognition of the characteristic peaks was satisfied. The proposed method efficiently detected multiple components of the T. laurifolia extract. Thus, the method is beneficial in providing references for enhancing the quality control of other herbal medicines.

Evolving Improved Sampling Protocols for Dose–Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3–20), and it reduced the parameter variance metric by 33–37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

Integrated Approach of Life Cycle Assessment and Experimental Design in the Study of a Model Organic Reaction: New Perspectives in Renewable Vanillin-Derived Chemicals.

This work is focused on performing a quantitative assessment of the environmental impacts associated with an organic synthesis reaction, optimized using an experimental design approach. A nucleophilic substitution reaction was selected, employing vanillin as the substrate, a phenolic compound widely used in the food industry and of pharmaceutical interest, considering its antioxidant and antitumoral potential. To carry out the reaction, three different solvents have been chosen, namely acetonitrile (ACN), acetone (Ace), and dimethylformamide (DMF). The syntheses were planned with the aid of a multivariate experimental design to estimate the best reaction conditions, which simultaneously allow a high product yield and a reduced environmental impact as computed by Life Cycle Assessment (LCA) methodology. The experimental results highlighted that the reactions carried out in DMF resulted in higher yields with respect to ACN and Ace; these reactions were also the ones with lower environmental impacts. The multilinear regression models allowed us to identify the optimal experimental conditions able to guarantee the highest reaction yields and lowest environmental impacts for the studied reaction. The identified optimal experimental conditions were also validated by experimentally conducting the reaction in those conditions, which indeed led to the highest yield (i.e., 93%) and the lowest environmental impacts among the performed experiments. This work proposes, for the first time, an integrated approach of DoE and LCA applied to an organic reaction with the aim of considering both conventional metrics, such as reaction yield, and unconventional ones, such as environmental impacts, during its lab-scale optimization.

Abrasive waterjet cutting of r‐GO infused magnesium fiber metal laminates: Experimental investigations and optimization through gorilla troops algorithm

AbstractLaminated metal‐composite structures, known as fiber metal laminates (FMLs), are modern lightweight materials extensively utilized in diverse industries such as aerospace and automotive manufacturing. These materials offer enhanced impact and fatigue resistance, making them invaluable for various applications. However, machining FMLs poses a challenge due to the occurrence of delamination and heterogenous in nature during conventional methods. Therefore, this study aims to investigate the quality characteristics such as kerf width, surface roughness and kerf taper of Magnesium based FMLs processed with an unconventional machining process namely abrasive water jet cutting (AWJC). These FMLs comprise alternately stacked kevlar/carbon fibers adhesively bonded with epoxy resin matrix filled with varying wt% of reduced graphene oxide (r‐GO), combined with AZ31B alloy sheet as the skin material. AWJC experiments were performed by varying the process parameters including waterjet pressure (275–325 MPa), stand‐off distance (2.5–3.5 mm), and cutting speed (600–800 mm/min) based on the box–behnken experimental design. The findings from the statistical analysis underscore the significant influences of stand‐off distance and waterjet pressure on kerf characteristics, while the inclusion of r‐GO is observed to notably affect the surface quality. In pursuit of enhancing cut quality, a multi‐response optimization was conducted employing the Gorilla Troops Optimization (GTO) algorithm. Comparative analysis with established metaheuristics like the gray wolf algorithm, dragonfly algorithm, and harmony search algorithm reveals GTO's superior performance across various metrics, including rapid convergence, diversity, and spacing values. Further, the cutting‐induced damages such as matrix plowing, fiber‐metal debonding and composite delamination were observed through microstructure analysis.Highlights Magnesium (AZ31B) fiber metal laminate comprising with various wt% of reduced graphene oxide was fabricated. Abrasive waterjet cutting of FMLs were performed based on box–behnken design experimental approach. Statistical analysis and influence of process parameters on the kerf width, surface roughness and kerf taper were investigated. Multi‐response optimization was carried out using metaheuristic‐based gorilla troops algorithm.

Controlled Enzymatic Synthesis of Polyesters Based on a Cellulose-Derived Triol Monomer: A Design of Experiment Approach.

Regioselective enzymatic polycondensation of the bio-based cellulose derived polyol, Triol-citro, and dimethyl adipate using Candida antarctica Lipase B (CaLB) was investigated. A Design of Experiment approach with MODDE® Pro 13 was used to determine important factors in the branching behavior of this polymer, and reactant ratio, temperature, reaction time and enzyme wt % were the studied factors. Multifunctional polyesters with pendant hydroxy groups were synthesized and fully characterized using 2D NMR techniques to determine degree of branching. Branching was minimal, with a maximum of 16 % observed, and monomer ratio, temperature and reaction time were all determined to be significant factors. In this work, Mn of up to 13 kDa were achieved, while maintaining degree of branching below 15 %, resulting in a linear polyester with the potential to be further functionalized.

Enhancing Dimensional Accuracy in Fused Filament Fabrication: A DOE Approach

3D printing is widely used in various fields and industries and is one of the most popular methods. Most popular advantage of this method is its flexibility in printing complex shapes and sizes. Quality is one of the important aspects when using this kind of technology which has a high advantage of printing complex shapes as this includes many factors like temperature, speed, material, infill types etc. In this paper one such analysis has been done by 3D printing parts under different factors. Analysis is done on how scaling factors affect the dimensions like height and diameter of a 3D printed part and check factors affecting these variations using Design of Experiments (DOE) analysis

Influence of defect geometry on putty performance in pipeline composite repair assessments

The effective repair of pipelines is crucial to ensuring the integrity of infrastructure. However, the effect of defect geometries on the efficiency of pipeline composite repair systems is a major concern in the industry. This study investigated the effects of geometric properties on the performance of composite repaired pipes and putty components in the context of the efficiency of composite repair systems using parametric analysis with various defect geometries as well as two putty formulations. The study involved the development of a finite element model and the analysis of numerical simulations based on a statistical experimental design matrix. Specifically, a design of experiments approach with a specific emphasis on response surface methodology utilizing the Box–Behnken design was employed to identify factor settings tailored to different defect geometries. The analysis revealed that defect depth, length, and width had a significant negative impact on the strength of putty. Defect depth had a greater impact on the putty performance and steel pipe burst pressure compared to defect length and width. However, defect length and width had mixed influences on putty performance, with different geometries resulting in different responses for both types of putty, indicating the existence of complex interactions between these two parameters. The strength capacity of Putty-A in the repair system was significantly influenced by the interaction between defect width, depth, and length, while Putty-B, the interaction was more significant when it came to length and width of the defect. Further statistical analysis confirmed the individual significance of defect depth, length, and width, as well as their interactions on putty strength capacity. The increased sensitivity of Putty-A to changes in defect geometry compared to Putty-B introduces further complexity to material considerations. These findings highlight the importance of selecting appropriate putty properties depending on the defect geometry for effective pipeline repair. This research provides valuable insights that will guide material selection and the development of new putty material, improving the resilience and reliability of future pipeline repair technologies.

Parameters design optimization of grading ring based on electric field analysis through response surface methodology

This research delves into the primary cause of the ageing of insulated cross-arms in the field, which is attributed to the high electric stress experienced at the end fittings. To address this issue, a novel technique is introduced to meticulously analyse the influence of Grading rings on the electric field (E-field) stress, with the goal of regulating it within the prescribed limits on the surface of the insulated cross-arm. By employing advanced methodologies such as finite element analysis and design of experiment approaches, the impact of various geometrical characteristics of Grading rings on the E-field is comprehensively investigated. Through the application of the analysis of variance, the primary and interaction effects of these factors are carefully examined. Additionally, leveraging the power of the response surface technique, an optimal link between the E-field and the geometrical parameters is modelled and optimized. The results of this comprehensive study unequivocally reveal the profound influence of the geometrical parameters of Grading rings on the E-field. Furthermore, diligent efforts are made to estimate the Grading ring parameters that will effectively regulate the E-field within the suggested maximum threshold based on the derived model. The proposed technique not only significantly reduces the computational analysis time but also serves as an efficient and invaluable tool for investigating the intricate geometry of Grading rings without imposing any additional computing burden.According to the findings, the E-field is most impacted by the factors H, D, and T to optimise the shape of grading rings used in insulated cross-arms. The main objective was achieved by reducing the maximum E-field value by 65% compared to the E-field without a grading ring, which has been accomplished. The suggested technique provides a quick and easy way to analyse and improve the intricate geometry of grading rings. As a result, it lowers the cost and time related to computational analysis without needing an excessive amount of computing work to do this.

Experiment design as a tool for hydrate phase equilibria prediction of CO2 + CH4 gas mixture in the presence of tera-butylammonium bromide in LW–H–V systems

In this study, we introduce experimental design as a novel approach to derive an equation that predicts the hydrate phase equilibrium temperature in a LW–H–V (liquid water, hydrate, vapor) system, based on pressure, gas composition, and the concentration of tetra-n-butylammonium bromide (TBAB). Our phase equilibria model is informed by experiments under nine different conditions, varying in gas compositions (CH4, CO2, and a 50/50 mole % CH4/CO2 mixture) and TBAB concentrations (15 and 30 wt.%). From these, we calculated temperature responses for 13 experiments, leading to the formulation of two models: a model without pressure transformation, which lacked predictive accuracy, and a more accurate model incorporating a natural logarithm pressure transformation (NLPT). The NLPT model demonstrated exceptional predictive power, with R2, adjusted R2, and predicted R2 values of 0.9975, 0.9958, and 0.9914, respectively. Additionally, it achieved an F-value of 565.90 in the analysis of variance (ANOVA). Validation diagrams, comparing predicted results to experimental data, confirmed the ability of the NLPT model to accurately predict hydrate phase equilibria, underscoring the efficacy of our experimental design approach.

Analysis of mechanical and sliding wear characteristics of steel industries’ solid wastes-filled epoxy composites using an experimental design approach

Analysis of mechanical and sliding wear characteristics of steel industries’ solid wastes-filled epoxy composites using an experimental design approach

Improving Observation, Experiment Planning, and Communication Skills Based on the STEM Approach

Physics learning needs to emphasize science process skills (KPS) in students. In fact, from the results of a case study conducted at SMAN 2 Banda Aceh, it was found that the LKPD used by teachers was still conventional and there were no aspects of KPS. The purpose of this study was to determine the results of the application of physics LKPD based on the STEM approach in improving the KPS of high school students. This study used a quantitative approach of experimental type Non-Equivalent Control Group Design. The samples used in this study were X4 and X6 class students totaling seventy people taken by purposive sampling. The instrument in this study was the KPS test. Data analysis of this study used the average test, N-gain test and t-test. The results showed the average value of the experimental class KPS N-gain of 0.70 was in the high category and the control class N-gain of 0.63 was in the medium category. Based on the results of the t-test, it can be concluded that there are differences in the improvement of students' KPS in physics learning taught with the STEM approach and those taught using the direct learning model.

Dispersive liquid–liquid microextraction based on deep eutectic solvent followed by high-performance liquid chromatography-ultraviolet for imidacloprid, hexythiazox, and fenpyroximate determination: Experimental design approach

Dispersive liquid–liquid microextraction (DLLME) of three insecticides, including imidacloprid (I), hexythiazox (H), and fenpyroximate (F), was performed as an efficient method for separation and preconcentration using a deep eutectic choline chloride-acetic acid solvent (DES) followed by determination by high-performance liquid chromatography-ultraviolet (HPLC-UV). The experimental design approach (central composite design) was used to optimize the effective parameters. According to the results, the addition of salt had no significant effect on extraction among the four parameters studied, including pH, DES volume (dispersive solvent), salt concentration, and chloroform volume. The optimized conditions in the DES-DLLME-HPLC-UV method were pH = 6.00, 100 µL DES, and 138 µL chloroform. Under optimized conditions, a wide linear range of 1.00 × 10−3-20.00 µg mL−1 was obtained for three insecticides. The enhancement factors were 11.22, 58.04, and 39.65 and the limit of detection of 3.00 × 10−4, 2.00 × 10−4, and 2.00 × 10−4 µg mL−1, and the limit of quantification of 9.00 × 10−4, 8.00 × 10−4, and 6.00 × 10−4 µg mL−1 were also determined for imidacloprid, hexythiazox, and fenpyroximate, respectively. The presence of other components including benomyl, dimethoate, mancozeb, captan, thiodicarb, carbendazim, and oxyfluorfen, showed no significant interference effect on the determination of three insecticides up to 50-fold. The method is eco-friendly benign, sensitive, precise, and straightforward as shown by the results.

Controlling Fluorescence Wavelength in the Synthesis of TGA-Capped CdTe Quantum Dots

Quantum dots (QDs) are semiconductor materials, with a size range between 1–10 nm, showcasing unique size-dependent physical and chemical properties. Such properties have potentiated their use in areas like medical imaging and biosensing. Herein, we present an open-air approach for synthesis of QDs, reducing the need for controllable atmospheric conditions. Furthermore, we present a predictive mathematical model for maximum emission wavelength (λmax) control. Through a straightforward microwave-based aqueous synthesis of TGA-CdTe QDs, we investigated the influence of time, temperature, and Te:Cd and TGA:Cd molar ratios on λmax, using a chemometric experimental design approach. CdTe-QDs were characterized by UV-Vis and fluorescence spectroscopies. Additionally, Fourier-Transform Infrared spectroscopy, X-ray photoelectron spectroscopy, Transmission Electron Microscopy, and Energy Dispersive X-ray were conducted. Stable QDs with fluorescence ranging from green to red (527.6 nm to 629.2 nm) were obtained. A statistical analysis of the results revealed that time and temperature were the most significant factors influencing λmax. After fine-tuning the variables, a mathematical model with 97.7% of prediction accurately forecasted experimental conditions for synthesizing TGA-CdTe QDs at predefined λmax. Stability tests demonstrated that the QDs retained their optical characteristics for over a month at 4 °C, facilitating diverse applications.

Corrosion inhibition behavior of 2-(4-methoxyphenyl)-benzothiazole on mild steel corrosion through design of experiments approach, quantum chemical calculations and molecular dynamics simulation

Corrosion inhibition behavior of 2-(4-methoxyphenyl)-benzothiazole on mild steel corrosion through design of experiments approach, quantum chemical calculations and molecular dynamics simulation

Optimization of process parameters in plasma arc cutting of commercial-grade aluminium plate

Abstract Plasma arc cutting (PAC) has emerged as a versatile and efficient method for the precision cutting of various materials, including commercial-grade aluminium plates. The optimization of process parameters is crucial for achieving high-quality cuts, minimizing material wastage, and enhancing overall productivity. This study aims to systematically investigate and optimize the key process parameters in PAC of commercial-grade aluminium plates. The experimental design involves the manipulation of parameters such as arc current, gas pressure, and workpiece thickness. A Design of Experiments approach, specifically Taguchi’s orthogonal array, is employed to efficiently explore the parameter space and identify the optimal combination of settings. The response variables considered for optimization include minimum surface roughness, minimum burr height, and maximum material removal rate (MRR). Analysis of variance is performed to get the percentage influence of each process parameter on the performance characteristic. The results obtained from the optimization process are expected to provide valuable insights into enhancing the efficiency and precision of PAC for commercial-grade aluminium plates. Arc current is found to be the most significant parameter in altering the surface roughness. The thickness of the material is the most significant parameter in altering burr height. None of the parameters is found to be significant in altering the MRR from Analysis of Variance analysis. From signal-to-noise ratio analysis and average performance graph, the optimum combination of processes in altering the bur height and MRR are found as arc current at 50 amp, the gas pressure at 5.4 bar, and the thickness of the workpiece at 6 mm.

Optimizing solid phase extraction and HPLC-MS/MS parameters for reliable quantification of COVID-19 pharmaceuticals in Mexico City’s wastewater: A design of experiments approach

Through the COVID-19 health crisis, a plethora of new medications, along with some discontinued pharmaceuticals, were directly tested on population to assess their efficacy in symptom management. Consequently, residues from these treatments were systematically disposed into sewage systems, presenting a novel contamination challenge in urban centers. To effectively address this issue by environmental authorities, the presence of these pollutants in wastewater must be accurately quantified. Herein, we conducted a meticulous study to validate an analytical method based on solid-phase extraction (SPE) and high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS) to detect six pharmaceuticals (metformin, famotidine, indomethacin, dexamethasone, azithromycin, and ivermectin) in wastewater. Using a Plackett-Burman design (PBD), the eight parameters significantly impacting on the HPLC-MS/MS technique were established based on the analytical signal of the six analytes. Subsequently, in conjunction with a Box-Behnken design (BBD), optimal analysis conditions were determined as follows: injection volume: 10 µL, column temperature: 20 °C, capillary voltage: 4100 V, drying gas temperature and flow rate of 350 °C and 7 L min−1 respectively, nebulizer pressure: 15 psi, concentration of formic acid and ammonium acetate in the mobile phase of 0.1 % and 10 mmol/L, respectively. SPE was optimized through a univariable experimental design. OASIS HLB cartridges exhibited the highest extraction efficiency compared to highly polar and non-polar adsorbents. The optimal sample loading conditions were set as 250.0 mL of water sample at pH = 9.0. The washing conditions were 5.0 mL of a 95:5 water:methanol solution, while elution conditions were optimal using 6.0 mL of a 98:2 methanol:formic acid solution. After evaporating the eluate to dryness and reconstituting in 1.0 mL of a 98:2 methanol:formic acid solution, a concentration factor of 250 was achieved. The analytical method was validated using for matrices, namely, raw and treated wastewater, wastewater percolated through soil and groundwater. Acceptable levels of precision and accuracy were achieved for all analytes in the tested matrices. Using the validated method, the six pharmaceuticals were quantified in environmental samples, revealing high levels of metformin, dexamethasone, and azithromycin in Mexico City’s sewage. Highly polar compounds displayed mobility through environment as they were present in effluent samples, percolated wastewater, and groundwater.