AIMC Topic: Ion Mobility Spectrometry

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Surface-Induced Unfolding Reveals Unique Structural Features and Enhances Machine Learning Classification Models.

Analytical chemistry Mar 14, 2025
Native ion mobility-mass spectrometry combined with collision-induced unfolding (CIU) is a powerful analytical method for protein characterization, offering insights into structural stability and enabling the differentiation of analytes with similar ...
Machine Learning Cattle Mass Spectrometry Immunoglobulin G Serum Albumin, Bovine Animals Ion Mobility Spectrometry Protein Unfolding Surface Properties Lactoglobulins
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Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence.

Analytical chemistry Jan 26, 2025
Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database sear...
Humans Neural Networks, Computer Amino Acid Sequence Escherichia coli Ions Mass Spectrometry Peptides Deep Learning Ion Mobility Spectrometry
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Discrimination of coal geographical origins through HS-GC-IMS assisted with machine learning algorithms in larceny case.

Journal of chromatography. A Aug 30, 2024
The process of globalization and industrialization has resulted in a rise in the theft of coal and other related products, thereby becoming a focal point for forensic science. This situation has engendered an escalated demand for effective detection ...
Geography Ion Mobility Spectrometry Volatile Organic Compounds Forensic Sciences Coal Gas Chromatography-Mass Spectrometry Machine Learning Algorithms
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Discrimination of Common Strains in Urine by Liquid Chromatography-Ion Mobility-Tandem Mass Spectrometry and Machine Learning.

Journal of the American Society for Mass Spectrometry Aug 5, 2024
Accurate identification of bacterial strains in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials, leading to antibiotic resistance. In this study, we ut...
Chromatography, Liquid Escherichia coli Infections Biomarkers Escherichia coli Tandem Mass Spectrometry Ion Mobility Spectrometry Humans Machine Learning
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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...
Camellia sinensis Tea Gas Chromatography-Mass Spectrometry Odorants Machine Learning Solid Phase Microextraction Algorithms Volatile Organic Compounds Ion Mobility Spectrometry Food Handling
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Application and performance enhancement of FAIMS spectral data for deep learning analysis using generative adversarial network reinforcement.

Analytical biochemistry Jul 20, 2024
When using High-field asymmetric ion mobility spectrometry (FAIMS) to process complex mixtures for deep learning analysis, there is a problem of poor recognition performance due to the lack of high-quality data and low sample diversity. In this paper...
Neural Networks, Computer Deep Learning Ion Mobility Spectrometry
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Application and integration of deep learning in FAIMS for identifying acetone concentration.

Analytical biochemistry Dec 18, 2023
In practical applications, analytical instruments are used for both qualitative and quantitative analysis. However, for high-field asymmetric-waveform ion mobility spectrometry (FAIMS), most studies to date have been focused on the qualitative analys...
Acetone Ion Mobility Spectrometry Deep Learning
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Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools.

Journal of agricultural and food chemistry Jul 20, 2022
The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such c...
Machine Learning Databases, Factual Mass Spectrometry Chromatography, Liquid Ion Mobility Spectrometry
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High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites.

Journal of the American Society for Mass Spectrometry May 11, 2022
Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a ra...
Machine Learning Ions Ion Mobility Spectrometry Databases, Factual Mass Spectrometry
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Collision Cross Section Calculations to Aid Metabolite Annotation.

Journal of the American Society for Mass Spectrometry Apr 4, 2022
The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low o...
Ion Mobility Spectrometry Molecular Structure Machine Learning Molecular Conformation Reproducibility of Results
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