Advanced chemically modified electrodes and platforms in food analysis and monitoring

Food Chemistry, Journal Year: 2024, Volume and Issue: 460, P. 140548 - 140548

Published: Aug. 3, 2024

Language: Английский

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Hydride-reduction-induced oxygen vacancies in CoV2O6 for machine learning-assisted enhanced electrochemical detection of homovanillic acid

Journal of Materials Science Materials in Electronics, Journal Year: 2025, Volume and Issue: 36(3)

Published: Jan. 1, 2025

Language: Английский

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Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms

Journal of Agricultural and Food Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

A large number of mycotoxins and related fungal metabolites have not been assessed in terms their toxicological impacts. Current methodologies often prioritize specific target families, neglecting the complexity presence co-occurring compounds. This work addresses a fundamental question: Can we assess molecular similarity predict toxicity silico using defined set descriptors? We propose rapid nontarget screening approach for multiple classes mycotoxins, integrating both unsupervised supervised machine learning models, alongside physicochemical descriptors to enhance understanding structural similarity, activity, toxicity. Clustering analyses identify natural clusters corresponding known mycotoxin indicating that belonging same cluster share similar properties. However, topological play significant role distinguishing between acutely toxic nonacutely Random forest (RF) neural networks (NN), combined with descriptors, contribute improved knowledge predictive capability regarding profiles. RF allows prediction data reflecting mainly features performs well biological activity. NN models prove be more sensitive activity than RF. The use encompassing diversity, chirality symmetry, connectivity, atomic charge, polarizability, together representing lipophilicity, absorption, permeation molecules, is crucial predicting toxicity, facilitating broader evaluations.

Language: Английский

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Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness these sensors, it crucial to find right balance among Researchers and engineers continually explore innovative approaches sensitivity, selectivity, reliability. Machine learning (ML) techniques facilitate analysis predictive modeling sensor establishing quantitative relationships between parameters their effects. This work presents a case study on developing molecularly imprinted polymer (MIP)-based for detecting doxorubicin (Dox), emphasizing use ML-based ensemble models improve Four ML models, including Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), are used evaluate effect each parameter prediction performance, using SHapley Additive exPlanations (SHAP) method determine feature importance. Based analysis, removing less influential introducing new significantly improved model's capabilities. By applying min-max scaling technique, ensured that all features contribute proportionally model process. Additionally, multiple models─Linear Regression (LR), KNN, DT, RF, Adaptive (AdaBoost), (GB), Support Vector (SVR), XGBoost, Bagging, Partial Least Squares (PLS), Ridge Regression─are applied data set in predicting output current compared. further novel proposed integrates GB, Bagging regressors, leveraging combined strengths offset individual weaknesses. main benefit this lies its ability MIP-based stacking regressor model, which improves methodology broadly applicable development other with different transducers sensing elements. Through extensive simulation results, demonstrated superior compared models. achieved an R-squared (R2) 0.993, reducing root-mean-square error (RMSE) 0.436 mean absolute (MAE) 0.244. These improvements enhanced sensitivity reliability sensor, demonstrating substantial gain over

Language: Английский

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Control of Microplastics and Nanoplastics Discharge via Biochar‐Based Filtration: Optimization Using Central Composite Design (CCD) and Identification of Column Fouling Mechanism

Environmental Quality Management, Journal Year: 2025, Volume and Issue: 34(4)

Published: May 6, 2025

ABSTRACT Microplastics (MPs) and nanoplastics (NPs) are emerging aquatic pollutants of significant environmental concern due to their pervasive hazards. Filtration using filter media is a common approach for mitigating MP NP contamination; however, the optimization process parameters underlying column fouling mechanisms remains insufficiently explored. This study investigates removal surface‐engineered biochar in continuous‐flow system via response surface methodology (RSM) employing central composite design (CCD). Four operating were evaluated: pH (3–11), concentration (0.01–0.09 g/L), flow rate (5–9 mL/min), bed depth (5–15 cm). Optimal efficiency was achieved at 7, 0.01 g/L, 7 mL/min rate, 10 cm depth, yielding efficiencies 93.75% (measured by turbidity method) 93.07% (estimated gravimetric method). Analysis variance (ANOVA) confirmed model's significance, with high coefficient determination ( R 2 ) observed between predicted actual data. All tested two interacting parameters, (i) concentration‐flow (ii) rate‐biochar significantly influenced efficiency. Prolonged operation under optimal conditions induced biochar‐packed bed, an evaluation Hermia's model, assuming uniform porosity filtration as main mechanism, indicated presence standard blocking, intermediate cake primary mechanisms. highlights potential promising efficient while providing insights into dynamics.

Language: Английский

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Determining the Quality of Imprinted Polymers Using Diverse Feature Selections Methods, Ada Boost and Gradient Boosting Algorithms

Results in Materials, Journal Year: 2025, Volume and Issue: unknown, P. 100722 - 100722

Published: May 1, 2025

Language: Английский

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Current trends of functional monomers and cross linkers used to produce molecularly imprinted polymers for food analysis

Critical Reviews in Food Science and Nutrition, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 21

Published: June 22, 2024

Molecularly imprinted polymers (MIPs) as artificial synthetic receptors are in high demand for food analysis due to their inherent molecular recognition abilities. It is common practice employ functional monomers with basic or acidic groups that can interact analyte molecules

Language: Английский

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Analytical and bioanalytical chemistry for digital diagnostics in digital healthcare

Analytical and Bioanalytical Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 31, 2024

Language: Английский

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Influence of Composition and Processing Methods on the Microstructure and Properties of Co-Cr-Fe-Mn-Ni High Entropy Alloys

Published: Jan. 1, 2024

This research was undertaken to evaluate potential use of high entropy alloys (HEAs) for high-temperature applications. Three HEAs – CoCrFeMnNi, Al0.5CoCrFeMnNi1.5, and Al0.5CoCrFeNi1.5 were examined assess the impact Mn Al additions heat treatments on microstructure mechanical properties. Additionally, preliminary development additive manufacturing process CoCrFeMnNi utilizing a laser powder direct energy deposition (LP-DED) system conducted. Results showed that addition resulted in hardness increase by forming γ'-Ni3Al β-NiAl phases, while enhanced but reduced phase stability solidus temperature. The LP-DED parameters (laser power, scanning speed, material feed rate) all demonstrated significant resulting sample dimensions. Comparative analyses revealed sintering hot isostatic pressing produced superior density defects compared casting LP-DED. study highlights influence alloy composition, techniques, performance HEAs.

Language: Английский

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