AIMC Topic: Gas Chromatography-Mass Spectrometry

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Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis.

Scientific reports
The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed model...

Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling.

Molecules (Basel, Switzerland)
Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, an...

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

EBioMedicine
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...

Deep-Learning-Assisted multivariate curve resolution.

Journal of chromatography. A
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...

Presence of emerging organic contaminants and solvents in schools using passive sampling.

The Science of the total environment
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...

Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics.

Analytical chemistry
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-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning.

Analytical and bioanalytical chemistry
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...

Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils.

Molecules (Basel, Switzerland)
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...

Analysis of phthalate plasticizer migration from PVDC packaging materials to food simulants using molecular dynamics simulations and artificial neural network.

Food chemistry
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 ...

Aroma perceptual interactions of benzaldehyde, furfural, and vanillin and their effects on the descriptor intensities of Huangjiu.

Food research international (Ottawa, Ont.)
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...