Research graph
References from Artificial intelligence in the design and optimization approach of microextraction techniques: A critical review and future perspectives. Local targets link to admitted publications; unresolved targets remain external evidence.
Recent advances in microextraction techniques using sustainable green solvents for mass spectrometry analysis
10.1016/j.trac.2023.117412 · 2024 · External reference
Transport phenomena in microchannels in liquid–liquid extraction (LLE) systems operating in a slug flow regime—a review
10.1002/cjce.25020 · 2024 · External reference
Miniaturized solid-phase extraction techniques in sample preparation applied to food matrices: a review
10.1016/j.microc.2025.113794 · 2025 · External reference
Various ecofriendly and green approaches in thin film microextraction techniques for sample analysis–a review
10.1016/j.trac.2025.118292 · 2025 · External reference
Solid phase microextraction for the bioanalysis of emerging organic pollutants
10.1016/j.trac.2024.117786 · 2024 · External reference
Recent advances in green solvents-based liquid-phase microextraction techniques for chromatographic analysis of active components in traditional Chinese medicine
10.1016/j.chroma.2024.465604 · 2025 · External reference
Dispersive liquid–liquid micro extraction: an analytical technique undergoing continuous evolution and development—a review of the last 5 years
10.3390/separations11070203 · 2024 · External reference
Critical role of single drop microextraction for drug isolation from complex matrix towards efficient pharmaceutical analysis: advances and challenges
10.1016/j.trac.2025.118163 · 2025 · External reference
Development of solid-phase microextraction methods for determination of non-steroidal anti-inflammatory drugs
10.1016/j.microc.2024.112340 · 2025 · External reference
Sample preparation of polar metabolites in biological samples: methodologies and technological insights
10.1016/j.trac.2025.118244 · 2025 · External reference
Headspace solid-phase microextraction: fundamentals and recent advances
2022 · External reference
Experimental design and optimization
10.1016/s0169-7439(98)00065-3 · 1998 · External reference
Response surface methodology (RSM) as a tool for optimization in analytical chemistry
10.1016/j.talanta.2008.05.019 · 2008 · External reference
Application of factorial and response surface methodology in modern experimental design and optimization
10.1080/10408340600969478 · 2006 · External reference
Machine learning advancements in organic synthesis: a focused exploration of artificial intelligence applications in chemistry
2024 · External reference
Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques
10.1016/j.chemosphere.2024.142477 · 2024 · External reference
A review of machine learning (ML) and explainable artificial intelligence (XAI) methods in additive manufacturing (3D Printing)
2024 · External reference
Artificial intelligence and machine learning techniques for suicide prediction: integrating dietary patterns and environmental contaminants
10.1016/j.heliyon.2024.e40925 · 2024 · External reference
AI vision and machine learning for enhanced automation in food industry: a systematic review
10.1016/j.foohum.2025.100587 · 2025 · External reference
Current trends in chromatographic prediction using artificial intelligence and machine learning
10.1039/d3ay00362k · 2023 · External reference
Smart Analytical chemistry: advances of artificial intelligence (AI) and machine learning (ML) in analytical and bioanalytical research-A review
2026 · External reference
Simulation for response surface in the HPLC optimization method development using artificial intelligence models: a data-driven approach
10.1016/j.chemolab.2020.104007 · 2020 · External reference
Accelerated chemical science with AI
10.1039/d3dd00213f · 2024 · External reference
Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost
10.1038/s41467-020-16201-z · 2020 · External reference
Understanding of machine learning with deep learning: architectures, workflow, applications and future directions
10.3390/computers12050091 · 2023 · External reference
Unresolved reference
2020 · External reference
Does machine learning really work?
1997 · External reference
Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence
10.1007/s10928-020-09685-1 · 2020 · External reference
Supervised machine learning algorithms: classification and comparison
10.14445/22312803/ijctt-v48p126 · 2017 · External reference
Unsupervised learning
10.1162/neco.1989.1.3.295 · 1989 · External reference
Artificial neural networks: fundamentals, computing, design, and application
10.1016/s0167-7012(00)00201-3 · 2000 · External reference
What is machine learning, artificial neural networks and deep learning?—examples of practical applications in medicine
10.3390/diagnostics13152582 · 2023 · External reference
Siri, Siri, in my hand: who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence
10.1016/j.bushor.2018.08.004 · 2019 · External reference
An ontol. Adapt. Pers. E-learning syst. assist. By Softw. agents cloud storage
2015 · External reference
Emerging trends in artificial intelligence
2019 · External reference
A brief history of artificial intelligence: on the past, present, and future of artificial intelligence
10.1177/0008125619864925 · 2019 · External reference
Unresolved reference
External reference
Applications of computational chemistry, artificial intelligence, and machine learning in aquatic chemistry research
10.1016/j.cej.2021.131810 · 2021 · External reference
Integrated omics: tools, advances and future approaches
10.1530/jme-18-0055 · 2019 · External reference
Using machine learning approaches for multi-omics data analysis: a review
10.1016/j.biotechadv.2021.107739 · 2021 · External reference
Artificial intelligence: machine learning for chemical sciences
10.1007/s12039-021-01995-2 · 2022 · External reference
Machine learning and artificial intelligence in pharmaceutical research and development: a review
10.1208/s12248-021-00644-3 · 2022 · External reference
Artificial intelligence and machine learning in drug discovery and development
10.1016/j.imed.2021.10.001 · 2022 · External reference
Mppredictor: an artificial intelligence-driven web tool for composition-based material property prediction
10.1021/acs.jcim.3c00307 · 2023 · External reference
AI in analytical chemistry: advancements, challenges, and future directions
2024 · External reference
Response surface methodology
10.1080/15458830.1996.11770760 · 1996 · External reference
Artificial intelligence-assisted modelling of heavy metal adsorption using cellulose-based and bio-waste adsorbents: a focus on ANN and ANFIS architectures
10.1016/j.rineng.2025.107147 · 2025 · External reference
10.5772/intechopen.1001640
10.5772/intechopen.1001640 · External reference
Unresolved reference
2017 · External reference
Introducing machine learning models to response surface methodologies, in: response Surf
2021 · External reference
A hybrid RSM-ANN-GA approach on optimization of ultrasound-assisted extraction conditions for bioactive component-rich stevia rebaudiana (Bertoni) leaves extract
10.3390/foods11060883 · 2022 · External reference
Comparison of an artificial neural network and a response surface model during the extraction of selenium-containing protein from selenium-enriched brassica napus L
10.3390/foods11233823 · 2022 · External reference
Artificial neural network in optimization of bioactive compound extraction: recent trends and performance comparison with response surface methodology
10.1007/s44211-024-00681-w · 2025 · External reference
Artificial Intelligence in HPLC method development: a critical review of technological integration, limitations, and future directions
2025 · External reference
Chemometrics
10.1021/ac303193j · 2013 · External reference
Unresolved reference
2018 · External reference
A practical tutorial for optimizing electromembrane extraction methods by response surface methodology
2025 · External reference
Model adequacy in assessing the predictive performance of regression models in pharmaceutical product optimization: the Bedaquiline solid lipid nanoparticle example
10.3390/scipharm92040064 · 2024 · External reference
Integration of response surface methodology (RSM), machine learning (ML), and artificial intelligence (AI) for enhancing properties of polymeric nanocomposites-A review
10.1002/pc.30011 · 2025 · External reference
Response surface methodology (RSM), Process Prod
1996 · External reference
Overview on the response surface methodology (RSM) in extraction processes
2015 · External reference
Doehlert matrix: a chemometric tool for analytical chemistry
10.1016/j.talanta.2004.01.015 · 2004 · External reference
Application of taguchi method in optimization of process factors of ready to eat peanut (Arachis hypogaea) Chutney, int
2015 · External reference
The Taguchi methodology as a statistical tool for biotechnological applications: a critical appraisal
2008 · External reference
Application of Taguchi design and response surface methodology for improving conversion of isoeugenol into vanillin by resting cells of psychrobacter sp. CSW4, Iran
2013 · External reference
Taguchi and quadratic via chromogenic design methodology: a better to best estimation process (Tizanidine HCl) bulk/pharmaceutical
2014 · External reference
Statistical techniques in pharmaceutical product development
2018 · External reference
Development of a green deep eutectic solvent-based thin film solid phase microextraction technique for the preconcentration of chlorophenoxy acid herbicides in drainage ditches and river waters using a central composite design
10.1016/j.microc.2022.108101 · 2022 · External reference
Combination of homogeneous liquid–liquid extraction and vortex assisted dispersive liquid–liquid microextraction for the extraction and analysis of ochratoxin A in dried fruit samples: central composite design optimization
10.1016/j.jfca.2023.105656 · 2023 · External reference
Green analytical comparison and central composite design optimization for simultaneous estimation of pain management drugs using RP-liquid chromatography
10.1016/j.microc.2024.112309 · 2025 · External reference
Magnetic polyoxometalate composite stabilized on the woven cotton yarn as a sorbent for thin film microextraction of some selected nonsteroidal anti-inflammatory drugs followed by high-performance liquid chromatography-ultraviolet detection
10.1016/j.chroma.2024.465615 · 2025 · External reference
In-situ synthesis of MIL-53@ ZIF-67 nanocomposite coated on the porous Ni foam substrate for thin film microextraction of phosalone and chlorpyrifos followed in fruit samples by high-performance liquid chromatography determination
10.1016/j.microc.2025.114865 · 2025 · External reference
MOF-199@ tryptophan coated on magnetic bar for stir-bar sorptive extraction of warfarin and aspirin from biological samples followed by quantification through HPLC-UV
10.1016/j.jchromb.2025.124877 · 2026 · External reference
Factorial design optimization of dispersive liquid–liquid microextraction for analysis of metals in natural and drinking waters
10.1016/j.microc.2022.108029 · 2022 · External reference
Vacuum-assisted headspace solid-phase microextraction and gas chromatography coupled to mass spectrometry applied to source rock analysis
2022 · External reference
Optimization of a macro-element extraction system from cocoa honey samples using ultrasound and Doehlert design with multiple responses
10.1016/j.foodchem.2025.143058 · 2025 · External reference
Application of hollow fiber-protected liquid-phase microextraction combined with GCMS– in determining endrin, chlordane, and dieldrin in rice samples
10.1007/s10653-023-01570-3 · 2023 · External reference
Sensitive quantification of acetochlor and metolachlor in water using Taguchi-optimized DLLME coupled with high-performance liquid chromatography
10.1016/j.microc.2024.110499 · 2024 · External reference
Magnetic hydrophobic deep eutectic Solvent-based microextraction for rapid and simultaneous detection of sulfonamide and estrogen residues in milk using high-performance liquid chromatography-diode array detection
10.1016/j.microc.2025.114107 · 2025 · External reference
A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique
2023 · External reference
Application of complex systems topologies in artificial neural networks optimization: an overview
10.1016/j.eswa.2021.115073 · 2021 · External reference
ANN-assisted forecasting of adsorption efficiency to remove heavy metals
10.3906/kim-1902-28 · 2019 · External reference
A generalized method for modeling the adsorption of heavy metals with machine learning algorithms
2020 · External reference
Support vector models-based quantitative structure–retention relationship (QSRR) in the development and validation of RP-HPLC method for multi-component analysis of anti-diabetic drugs
10.1007/s10337-023-04292-x · 2024 · External reference
Exploration of the prediction and generation patterns of heterocyclic aromatic amines in roast beef based on genetic algorithm combined with support vector regression
2025 · External reference
Application of response surface methodology and genetic algorithm for optimization and determination of iron in food samples by dispersive liquid–liquid microextraction coupled UV–visible spectrophotometry, Arab
2018 · External reference
Particle swarm optimization–artificial neural network modeling and optimization of leachable zinc from flour samples by miniaturized homogenous liquid–liquid microextraction
10.1016/j.jfca.2013.11.002 · 2014 · External reference
Simultaneous extraction of Cu2+ and Cd2+ ions in water, wastewater, and food samples using solvent-terminated dispersive liquid–liquid microextraction: optimization by multiobjective evolutionary algorithm based on decomposition
10.1007/s10661-019-7383-6 · 2019 · External reference
Recent trends in microextraction techniques employed in analytical and bioanalytical sample preparation
10.3390/separations4040036 · 2017 · External reference
Recent progress in solid-and liquid-phase microextraction methods for the extraction and quantification of current-use pesticides
2025 · External reference
Solvent screening for separation processes using machine learning and high-throughput technologies
10.1021/cbe.4c00170 · 2025 · External reference
SUSSOL—Using artificial intelligence for greener solvent selection and substitution
10.3390/molecules25133037 · 2020 · External reference
Optimization of solvent terminated dispersive liquid–liquid microextraction of copper ions in water and food samples using artificial neural networks coupled bees algorithm
10.1007/s00128-017-2263-7 · 2018 · External reference
Rapid extraction of copper ions in water, tea, milk and apple juice by solvent-terminated dispersive liquid–liquid microextraction using p-sulfonatocalix (4) arene: optimization by artificial neural networks coupled bat inspired algorithm and response su
10.1007/s13197-019-03892-6 · 2019 · External reference
Extraction of phenolic acids and tetramethylpyrazine in Shanxi aged vinegar base on vortex-assisted liquid-liquid microextraction-hydrophobic deep eutectic solvent: COSMO-RS calculations and ANN-GA optimization
10.1016/j.foodchem.2024.141353 · 2025 · External reference
Recent trends in synthesis and application of 2D mxene-based nanomaterials for microextraction purposes
10.1016/j.microc.2025.113788 · 2025 · External reference
Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar
2025 · External reference
Enhanced machine learning prediction of biochar adsorption for dyes: parameter optimization and experimental validation
10.1007/s44246-025-00213-9 · 2025 · External reference
Magnetic sorbents: synthetic pathways and application in dispersive (micro) extraction techniques for bioanalysis
10.1016/j.trac.2023.117486 · 2024 · External reference
Solid supports and New advanced materials used in microextraction processes metal-organic framework-based solid-phase microextraction for air samples analysis: a mini-review
10.1016/j.trac.2024.118021 · 2024 · External reference
Unresolved reference
2017 · External reference
Unresolved reference
2025 · External reference
Metal-organic framework mixed-matrix disks: versatile supports for automated solid-phase extraction prior to chromatographic separation
10.1016/j.chroma.2017.01.069 · 2017 · External reference
Dissolvable layered double hydroxide coated magnetic nanoparticles for extraction followed by high performance liquid chromatography for the determination of phenolic acids in fruit juices
10.1016/j.chroma.2014.09.024 · 2014 · External reference
Response surface methodology for process optimization in livestock wastewater treatment: a review
10.1016/j.heliyon.2024.e30326 · 2024 · External reference
Development of nontargetd volatilomics with solid-phase microextraction for the authentication of plant-based milk alternatives
10.1016/j.talanta.2025.128239 · 2025 · External reference
Evaluation of VOCs from fungal strains, building insulation materials and indoor air by solid phase microextraction arrow, thermal desorption–gas chromatography-mass spectrometry and machine learning approaches
10.1016/j.envres.2023.115494 · 2023 · External reference
Enhancing food quality analysis: the transformative role of artificial neural networks in modern analytical techniques
10.1080/10408347.2025.2554239 · 2025 · External reference
Unresolved reference
External reference
A comprehensive review on ensemble deep learning: opportunities and challenges
2023 · External reference
Deep learning in spectral analysis: modeling and imaging
10.1016/j.trac.2024.117612 · 2024 · External reference
Application of deep learning and machine learning models with enhanced feature extraction for the prediction of plant extraction yields using supercritical CO2: an optimization and comparative analysis
10.1016/j.supflu.2025.106755 · 2026 · External reference
Machine learning in gas separation membrane developing: ready for prime time
10.1016/j.seppur.2023.123493 · 2023 · External reference
Recent advances and applications of deep learning methods in materials science
10.1038/s41524-022-00734-6 · 2022 · External reference
Deep learning modelling techniques: current progress, applications, advantages, and challenges
10.1007/s10462-023-10466-8 · 2023 · External reference
Small data, big challenges: machine-and deep-learning strategies for data-limited drug discovery
10.1016/j.addr.2025.115762 · 2026 · External reference
Unsupervised feature-learning for hyperspectral data with autoencoders
10.3390/rs11070864 · 2019 · External reference
Deep convolutional autoencoder for the simultaneous removal of baseline noise and baseline drift in chromatograms
10.1016/j.chroma.2021.462093 · 2021 · External reference
DeepSpectra: an end-to-end deep learning approach for quantitative spectral analysis
10.1016/j.aca.2019.01.002 · 2019 · External reference
Explainable AI to facilitate understanding of neural network-based metabolite profiling using NMR spectroscopy
10.3390/metabo14060332 · 2024 · External reference
Artificial intelligence, machine learning, and drug repurposing in cancer
10.1080/17460441.2021.1883585 · 2021 · External reference
Analytical methods for determination of opioids in biological samples (2014-2025): a green chemistry perspective
10.1016/j.trac.2026.118892 · 2026 · External reference
Current advances in solid-phase microextraction technique as a green analytical sample preparation approach
10.1080/17518253.2025.2486135 · 2025 · External reference
Advanced artificial intelligence for efficient and rapid evaluation of analytical procedures greenness and sustainability
2025 · External reference
