AIMC Topic: Gas Chromatography-Mass Spectrometry
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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-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...
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...
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 ...
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...
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...
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...
A deep convolutional neural network for the estimation of gas chromatographic retention indices.
Journal of chromatography. A
Jul 29, 2019
A deep convolutional neural network was used for the estimation of gas chromatographic retention indices on non-polar (polydimethylsiloxane and polydimethyl(5%-phenyl) siloxane) stationary phases. The neural network can be used for candidate ranking ...
Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application.
Sensors (Basel, Switzerland)
Jul 11, 2019
Cocoa is an important commodity crop, not only to produce chocolate, one of the most complex products from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied...
Streamlining Quality Review of Mass Spectrometry Data in the Clinical Laboratory by Use of Machine Learning.
Archives of pathology & laboratory medicine
Feb 20, 2019
CONTEXT.—: Turnaround time and productivity of clinical mass spectrometric (MS) testing are hampered by time-consuming manual review of the analytical quality of MS data before release of patient results.
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