AIMC Topic: Gas Chromatography-Mass Spectrometry

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Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza.

EBioMedicine Aug 19, 2021
BACKGROUND: Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flig...
Child Molecular Diagnostic Techniques Metabolomics Male Adolescent Child, Preschool Machine Learning Female Humans Orthomyxoviridae Adult Metabolome Influenza, Human Nasal Mucosa Gas Chromatography-Mass Spectrometry Pyrrolidonecarboxylic Acid
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Deep-Learning-Assisted multivariate curve resolution.

Journal of chromatography. A Nov 13, 2020
Gas chromatography-mass spectrometry (GC-MS) is one of the major platforms for analyzing volatile compounds in complex samples. However, automatic and accurate extraction of qualitative and quantitative information is still challenging when analyzing...
Gas Chromatography-Mass Spectrometry Multivariate Analysis Neural Networks, Computer Deep Learning Least-Squares Analysis
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Presence of emerging organic contaminants and solvents in schools using passive sampling.

The Science of the total environment Oct 13, 2020
In this study, we report on the applicability of passive sampling with Carbopack X adsorbent tubes followed by thermal desorption gas-chromatography-mass spectrometry (TD-GC-MS) to monitor the concentrations of emerging organic contaminants (EOCs) an...
Solvents Schools Diffusion Gas Chromatography-Mass Spectrometry Environmental Monitoring Air Pollutants
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Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics.

Analytical chemistry Aug 12, 2020
Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. ...
Gas Chromatography-Mass Spectrometry Machine Learning Deep Learning Metabolomics Humans
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Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning.

Analytical and bioanalytical chemistry Aug 4, 2020
For the first time, a prototype HS-GC-MS-IMS dual-detection system is presented for the analysis of volatile organic compounds (VOCs) in fields of quality control of brewing hop. With a soft ionization and drift time-based ion separation in IMS and a...
Machine Learning Reproducibility of Results Fermentation Volatile Organic Compounds Solid Phase Microextraction Principal Component Analysis Gas Chromatography-Mass Spectrometry Quality Control Ion Mobility Spectrometry Humulus
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Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils.

Molecules (Basel, Switzerland) May 25, 2020
In the last decade essential oils have attracted scientists with a constant increase rate of more than 7% as witnessed by almost 5000 articles. Among the prominent studies essential oils are investigated as antibacterial agents alone or in combinatio...
Gas Chromatography-Mass Spectrometry Chlorocebus aethiops Vero Cells Herpesvirus 1, Human Lamiales Plant Oils Structure-Activity Relationship Supervised Machine Learning Oils, Volatile Antiviral Agents Humans Animals Microbial Sensitivity Tests
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Analysis of phthalate plasticizer migration from PVDC packaging materials to food simulants using molecular dynamics simulations and artificial neural network.

Food chemistry Feb 22, 2020
Based on the experimental data of gas chromatography-mass spectrometry, an improved artificial neural network was first established to predict the migration of 2-ethylhexyl phthalate (DEHP) plasticizer from poly(vinylidene chloride) (PVDC) into food ...
Plasticizers Polyvinyl Chloride Diethylhexyl Phthalate Neural Networks, Computer Molecular Dynamics Simulation Temperature Gas Chromatography-Mass Spectrometry Food Packaging Solubility Food Contamination
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Aroma perceptual interactions of benzaldehyde, furfural, and vanillin and their effects on the descriptor intensities of Huangjiu.

Food research international (Ottawa, Ont.) Nov 21, 2019
Aldehydes are important in the aroma of Huangjiu and contribute the almond and sweet aromas to Huangjiu. The perceptual interactions of 3 important aldehyde compounds were investigated using S-curves. Three volatiles, benzaldehyde, furfural, and vani...
Odorants Humans Smell Taste Female Volatile Organic Compounds Consumer Behavior Food Analysis Benzaldehydes Young Adult Wine Furaldehyde Adult Gas Chromatography-Mass Spectrometry Male
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Steroid identification via deep learning retention time predictions and two-dimensional gas chromatography-high resolution mass spectrometry.

Journal of chromatography. A Oct 28, 2019
Untargeted steroid identification represents a great analytical challenge even when using sophisticated technology such as two-dimensional gas chromatography coupled to high resolution mass spectrometry (GC × GCHRMS) due to the chemical similarity of...
Deep Learning Support Vector Machine Least-Squares Analysis Gas Chromatography-Mass Spectrometry Static Electricity Neural Networks, Computer Steroids
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Peak alignment of gas chromatography-mass spectrometry data with deep learning.

Journal of chromatography. A Aug 22, 2019
We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for bioma...
Gas Chromatography-Mass Spectrometry Deep Learning Area Under Curve Neural Networks, Computer Algorithms
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