AIMC Topic: Chromatography, Gas

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Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning.

Sensors (Basel, Switzerland) Nov 23, 2024
This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least abs...
Machine Learning Honey Chromatography, Gas Food Contamination Support Vector Machine
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Development of deep learning software to improve HPLC and GC predictions using a new crown-ether based mesogenic stationary phase and beyond.

Journal of chromatography. A Oct 28, 2024
The application of AI to analytical and separative sciences is a recent challenge that offers new perspectives in terms of data prediction. In this work, we report an AI-based software, named Chrompredict 1.0, which based on chromatographic data of a...
Software Crown Ethers Chromatography, Gas Chromatography, High Pressure Liquid Deep Learning
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Prediction of Fatty Acid Intake from Serum Fatty Acid Levels Using Machine Learning Technique in Women Living in Toyama Prefecture.

Journal of oleo science Sep 20, 2024
Preventing lifestyle-related diseases requires understanding and managing the intake of total fats and specific types of fatty acids, especially trans fatty acids. There are several methods for measuring fat intake, each with its own strengths and li...
Middle Aged Adolescent Adult Fatty Acids Diet Surveys Japan Regression Analysis Biomarkers Young Adult Trans Fatty Acids Female Humans Machine Learning Dietary Fats Diet Records Chromatography, Gas
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Validation of the identification reliability of known and assumed UDMH transformation products using gas chromatographic retention indices and machine learning.

Chemosphere Jun 21, 2024
Thirty two commercially available standards were used to determine chromatographic retention indices for three different stationary phases (non-polar, polar and mid-polar) commonly used in gas chromatography. The selected compounds were nitrogen-cont...
Chromatography, Gas Machine Learning Hydrazines Reproducibility of Results
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Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review.

Journal of chromatography. A Oct 19, 2023
In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional ...
Chromatography, Gas Workflow Thermodynamics Algorithms Support Vector Machine
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Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity.

Critical reviews in analytical chemistry Sep 6, 2023
Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed c...
Neural Networks, Computer Quantitative Structure-Activity Relationship Chromatography Artificial Intelligence Chromatography, Gas
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Accurate prediction of isothermal gas chromatographic Kováts retention indices.

Journal of chromatography. A Jun 24, 2023
We describe a freely available web server called Retention Index Predictor (RIpred) (https://ripred.ca) that rapidly and accurately predicts Gas Chromatographic Kováts Retention Indices (RI) using SMILES strings as chemical structure input. RIpred pe...
Chromatography, Gas Databases, Factual Neural Networks, Computer Metabolome Humans
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Application of gas chromatographic data and 2D molecular descriptors for accurate global mobility potential prediction.

Environmental pollution (Barking, Essex : 1987) Dec 3, 2022
Mobility is a key feature affecting the environmental fate, which is of particular importance in the case of persistent organic pollutants (POPs) and emerging pollutants (EPs). In this study, the global mobility classification artificial neural netwo...
Environmental Pollutants Chromatography, Gas Neural Networks, Computer
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Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases.

International journal of molecular sciences Aug 25, 2021
Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that thei...
Chromatography, Gas Deep Learning
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Predicting Kováts Retention Indices Using Graph Neural Networks.

Journal of chromatography. A Mar 25, 2021
The Kováts retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independent of many experimental variables and, as such, is considered a near-un...
Chromatography, Gas Databases, Factual Neural Networks, Computer Deep Learning
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