AIMC Topic: Gas Chromatography-Mass Spectrometry
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Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning.
Analytical chemistry
Aug 28, 2024
Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learni...
Chemometrics methods, sensory evaluation and intelligent sensory technologies combined with GAN-based integrated deep-learning framework to discriminate salted goose breeds.
Food chemistry
Aug 22, 2024
The authenticity of salted goose products is concerning for consumers. This study describes an integrated deep-learning framework based on a generative adversarial network and combines it with data from headspace solid phase microextraction/gas chrom...
An initial exploration of machine learning for establishing associations between genetic markers and THC levels in Cannabis sativa samples.
Forensic science international. Genetics
Aug 9, 2024
Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate st...
Characterization and exploration of dynamic variation of volatile compounds in vine tea during processing by GC-IMS and HS-SPME/GC-MS combined with machine learning algorithm.
Food chemistry
Jul 31, 2024
It is imperative to unravel the dynamic variation of volatile components of vine tea during processing to provide guidance for tea quality evaluation. In this study, the dynamic changes of volatile compounds of vine tea during processing were charact...
Online sequential analysis of volatile and semivolatile organic compounds in water matrices by double robotic sample preparations and dual-channel mono and comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry system.
Journal of chromatography. A
May 4, 2024
The monitoring of organic compounds in aquatic matrices poses challenges due to its complexity and time-intensive nature. To address these challenges, we introduce a novel approach utilizing a dual-channel mono (D) and comprehensive two-dimensional (...
Deep Learning Bridged Bioactivity, Structure, and GC-HRMS-Readable Evidence to Decipher Nontarget Toxicants in Sediments.
Environmental science & technology
May 2, 2024
Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typ...
VOC data-driven evaluation of vehicle cabin odor: from ANN to CNN-BiLSTM.
Environmental science and pollution research international
Apr 26, 2024
Emissions of volatile organic compounds (VOCs) in vehicles represent a significant problem, causing unpleasant odors. To mitigate VOCs and odors in vehicles, it is critical to choose interior parts with low odor and VOC emissions. However, prevailing...
Development of machine learning-based shelf-life prediction models for multiple marine fish species and construction of a real-time prediction platform.
Food chemistry
Apr 10, 2024
At least 10 million tons of seafood products are spoiled or damaged during transportation or storage every year worldwide. Monitoring the freshness of seafood in real time has become especially important. In this study, four machine learning algorith...
Characterization of lamb shashliks with different roasting methods by intelligent sensory technologies and GC-MS to simulate human muti-sensation: Based on multimodal deep learning.
Food chemistry
Dec 24, 2023
To simulate the functions of olfaction, gustation, vision, and oral touch, intelligent sensory technologies have been developed. Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) with electronic noses (E-noses...
Fusion of Quality Evaluation Metrics and Convolutional Neural Network Representations for ROI Filtering in LC-MS.
Analytical chemistry
Jan 4, 2023
Region of interest (ROI) extraction is a fundamental step in analyzing metabolomic datasets acquired by liquid chromatography-mass spectrometry (LC-MS). However, noises and backgrounds in LC-MS data often affect the quality of extracted ROIs. Therefo...
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