Cuminaldehyde potentiates antimicrobial efficacy of gentamicin and ciprofloxacin against Escherichia coli: a response surface methodology (RSM) based study.

The online version contains supplementary material available at 10.1007/s13197-021-05116-2.

This study focused on optimizing the fermentation conditions of Shatavari plant-based roots using an artificial neural network and response surface methodology. The aim was to identify the optimal independent variables and corresponding responses by comparing experimental and predicted responses. The experimentation was validated using a genetic algorithm, determining the best temperature, pH, and inoculum parameters. In this study, we used the Shatavari (Asparagus racemosus Willd.) plant's root as their primary raw material and subjected it to treatment with α amylase and gluco-amylase enzyme (EC 232-885-6) which exhibited a remarkable activity level ranging from 8000 to 12,000 U/mg The resulting hydrolysate was fermented using Saccharomyces cerevisiae (NCIM 2428) culture. To determine the optimal combination of input variables a Central Composite Rotatable Design was implemented, facilitated by the Design Expert software (Version 11.0.3.0 by Stat-Ease Inc.),. The optimal conditions for the experiment were found to be a temperature of 32°C, pH of 4.0, and inoculum concentration of 10% (v/v). The Artificial Neural Network (ANN) model was able to successfully predict the response variables with a marginal relative error rate of 8.722% and 24.312% for ethanol production and antioxidant activity, respectively. The fermented Shatavari-based low-alcohol Nutra beverage contained only fructose. The validation of Shatavari juice using the ANN model showed an enhanced ethanol yield of 3.21% and 421.47μg/L antioxidant activity during fermentation. The experimental and predicted outcomes from the Artificial Neural Network-Genetic Algorithm (ANN-GA) model matched, proving its predictive precision. The online version contains supplementary material available at 10.1007/s13197-025-06275-2.

The online version contains supplementary material available at 10.1007/s13197-024-06167-x.

Environmental contamination with various pesticides accompanied by uncontrolled use contributes to severe ecological and health problems. Although extensive research was conducted on pesticides degradation, very few reports have demonstrated the degradation of mixed pesticides. Consequently, this study aimed to evaluate the removal efficacy of highly potent bacterial isolate for pesticide mixture under optimal culture conditions, followed by their application in milk. Isolation and selection of bacterial isolates were performed from 40 milk samples by enrichment culture technique and were screened to obtain highly potent bacterial strain identified by 16 S rDNA analysis. The statistics-based experimental designs were applied to optimize the culture conditions towards the best degradation of pesticides mixture, followed by subsequent utilization in milk. The degradation ratio of pesticides was analyzed using gas chromatography-mass spectrometry. In this study, a bacterial strain S6A identified as Bacillus subtilis-mw1 efficiently eliminated environmental contaminants from different groups of pesticide residues. The statistical optimization showcased optimum settings that accomplished the highest pesticide mixture degradation (61.59 %). The application experiment manifested that degradation of pesticide mixtures of sterile milk (STM) was relatively faster than non-sterile milk (NSTM). The obtained results assist in eliminating environmental contamination with various groups of pesticide residues. Furthermore, it can be employed in reducing pesticide residues that cause milk contamination to increase safety and quality.Graphical abstract. The online version contains supplementary material available at 10.1007/s40201-021-00683-0.

The online version contains supplementary material available at 10.1007/s13205-021-03030-0.

The online version contains supplementary material available at 10.1007/s13197-024-06105-x.

In this study MgAl- layered double hydroxides (MgAl-LDH) nanoparticles were prepared by a simple and fast co-precipitation method and used as a catalyst in the ozonation process to degrade diazinon from aqueous solutions. The structure of the synthesized MgAl-LDH was investigated by X-ray diffraction pattern (XRD) and field emission scanning electron microscope-energy dispersive spectroscopy (FESEM-EDX). The response surface methodology (RSM) was used to investigate the effects of different parameters including of reaction time, initial diazinon concentration, pH, and LDH dose on the removal of diazinon by MgAl-LDH catalytic ozonation process. Central Composite Design (CCD) was employed for the optimization and modeling of the process. Dispersive liquid-liquid microextraction (DLLME) method was used to extract diazinon from aqueous samples. The GC-Mass analysis was performed to determine intermediate compounds during diazinon degradation reactions. To evaluate the process performance, TOC and COD removal were measured under optimum conditions. The highest removal efficiency of 92% was observed in optimum conditions as follow; initial diazinon concentration: 120mg/L, pH: 8.25, LDH dose: 750mg/L, and reaction time: 70min. The quadratic model was obtained with a good fit. The removal of COD and TOC were 80% and 74%, respectively. This process can be suggested and used in the treatment of various industrial wastewaters. The online version contains supplementary material available at 10.1007/s40201-021-00687-w.

The online version contains supplementary material available at 10.1007/s13197-023-05876-z.

The online version contains supplementary material available at 10.1007/s13197-021-05006-7.

With the continuous spread of COVID-19, the water pollution problems caused by the abuse of chloroquine phosphate (CQP) as an antiviral drug have attracted wide attention. The cubic Fm-3m spinel high entropy oxide (HEO)—(MgCuMnCoFe)Ox was prepared by coprecipitation method as the catalytic wet air oxidation (CWAO) catalyst to treat CQP simulated wastewater. Through electron spin resonance (ESR) analysis, HEO will stimulate the production of superoxide radical (·O2−) and hydroxyl radical (·OH) in the wet air oxidation (WAO) process, which accelerates the degradation and mineralization of CQP. Through response surface method (RSM) optimization, the optimal degradation conditions of CQP in CWAO were proposed: initial oxygen pressure of 15 bar, catalyst dosage of 1.4 g/L and temperature of 230 °C. The advantages of HEO in CWAO were analyzed by principal component analysis (PCA). The degradation mechanism of CQP in CWAO by (MgCuMnCoFe)Ox were explored. This work provides a new idea for the rapid development of HEO in the field of environmental catalysis.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10853-022-07271-z.

Compressive strength is a fundamental property for evaluating cementitious materials, yet its prediction and optimization remain challenging due to the complex and nonlinear interactions of chemical composition, curing time, and processing conditions. This study presents a hybrid framework that integrates Response Surface Methodology (RSM) with the Genetic Algorithm (GA) to enhance predictive modeling and optimize cement mixtures. RSM was used to construct polynomial models—linear, linear + square, linear + interaction, and full quadratic—to capture the relationships between eight oxide compositions (Silo₂, Allow₃, Foo₃, Cano, Mg, SO₃, K₂O, NaoSiO2,Al2O3,Fe2O3,CaO,MgO,SO3 ,K2O,Na2O) and compressive strength at 2, 7, and 28 days. The Genetic Algorithm was employed to explore this high-dimensional solution space and identify optimal compositions. Experimental data from a regional cement producer /laboratory were used for model training and validation. Results indicate that the full Quadratic model consistently outperformed other RSM models, achieving the lowest RMSE and highest R² across all curing periods. GA optimization further improved performance, with maximum predicted compressive strengths increasing by 83% at 2 days, 48% at 7 days, and 43% at 28 days compared to RSM-only predictions. These findings highlight the robustness of the hybrid GA + RSM approach in capturing nonlinearities, improving accuracy, and achieving superior optimization outcomes. The framework offers a promising tool for developing eco-efficient and high-strength cementitious mixtures, contributing to innovation in sustainable construction materials.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-32836-8.

A recent article in the journal by Ilzarbe et al. In total, 77 case studies were presented from a variety of engineering fields that the authors classified as materials, mechanical engineering, industrial engineering, electrical/electronic engineering, energy and other. The authors surveyed several core journals to find their case studies; The Journal of Applied Statistics, The Journal of Quality Technology, Quality and Reliability Engineering International, Quality Engineering, and Technometrics. A search was also made using the Web of Knowledge.

To explore the flavonoids from Morus alba L. leaves (MLF), the process of extracting was optimized by a response surface methodology and the antimicrobial and antioxidant activities were evaluated in vitro. The yield of flavonoids reached 50.52 mg g−1 under the optimized extraction conditions (i.e., extraction temperature, 70.85 °C; solvent concentration, 39.30%; extraction time, 120.18 min; and liquid/solid ratio, 34.60:1). The total flavonoids were extracted in organic solvents with various polarities, including petroleum ether (MLFp), ethyl acetate (MLFe), and n-butanol (MLFb). In vitro, the four MLF samples exhibited good antioxidant activities for scavenging of 2, 2′-azinobis-(3-ethylbenz-thiazoline-6-sulphonate) radical, 1, 1-diphenyl-2-picrylhydrazyl radical, and total reducing power. Regarding antimicrobial efficacy, the MLF samples suppressed the development of Staphylococcus aureus, Bacillus subtilis, and Bacillus pumilus. The MLF samples inhibited α-amylase activity to a certain extent. The analytical hierarchy process (AHP) was used to evaluate comprehensively the bioactivities of the MLF samples. The AHP results revealed that the bioactivity comprehensive score (78.83 μg mL−1) of MLFe was optimal among the four MLF samples. Morus alba L. leaves also exhibited non-hemolytic properties. All bioactivities suggested the potential of MLFe as a candidate resource in the food and drug industries.

Inactivation of Salmonella, Listeria monocytogenes, Aspergillus and Penicillium on lemons using advanced oxidation process optimized through response surface methodology

The synthesis of chiral amines is crucial since they are a key component in nearly 40% of top-selling drugs. ω-transaminases are promising biocatalysts for producing these chiral amines. This study reports the isolation of a wild-type Bacillus strain (Bacillus inaquosorum AGSP2) from a contaminated site at the Amlakhadi river, Gujarat, India. We optimized ω-transaminase production using both One Factor at a Time and Response Surface Methodology- Central Composite Design (RSM-CCD) techniques, with further model validation via an Artificial Intelligence (AI) tool, Support Vector Machine. The optimal medium, called Modified Luria–Bertani, contains fructose (12 g/L), NaCl (7.5 g/L), yeast extract (7.5 g/L), peptone (12 g/L), and α-methylbenzylamine (5 mM). Optimization increased ω-transaminase production 2.8 times, reaching an activity of 6121.88 ± 42 U/ml at 37 °C, pH 7, 120 rpm, with 2% (v/v) inoculum. The RSM-CCD model had R2 = 0.95, predicted R2 = 0.78, RMSE = 0.2327, while SVM achieved R2 = 0.99, predicted R2 = 0.96, RMSE = 0.1327. AGSP2 catalyzed the biotransformation of acetophenone with (S)-α-methylbenzylamine, resulting in a 53.32% conversion rate. These findings demonstrate the potential of combining statistical and AI tools to improve biocatalyst production and applications, presenting a sustainable approach for chiral amine synthesis and highlighting ω-transaminase’s role in green biocatalysis.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-026-46062-3.