AIMC Topic: Ion Mobility Spectrometry

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Large Data Set Analysis Reveals Structural Origin of Peptide Collisional Cross Section Bimodal Behavior.

Journal of the American Society for Mass Spectrometry
Recent advances in ion mobility spectrometry have enabled the measurement of rotationally averaged collisional cross-sectional area (CCS) for millions of peptides as part of routine proteomic mass spectrometry workflows. One of the most striking find...

Characterization of Gastrodiae Rhizoma from different geographical origins by HS-GC-IMS and authenticity identification combined with deep learning.

Journal of chromatography. A
Given the growing demand for Gastrodiae Rhizoma (GR), it is important to establish a strategy for origin authentication and adulteration screening to ensure the high-quality products in the market. Currently, efficient and dependable methods for iden...

Evaluation of ion mobility, uni- and multidimensional liquid chromatography for non-target screening of phenolic compounds in wheat flag leaves.

Journal of chromatography. A
Non-target screening (NTS) of plant secondary metabolites is analytically challenging due to the complexity of mixtures with structurally similar compounds and isomers. This study evaluates the added value of ion mobility spectrometry (IMS) and compr...

Machine learning discrimination of quality grades of base liquor integrating GC-TOF/MS and GC-IMS data analysis: Case study of strong-flavor Chinese baijiu.

Food chemistry
The flavor of base liquor is critical to the grading quality of Baijiu. This study focuses on the base liquor grades of five different strong-flavor Baijiu brands. Headspace Solid-phase Microextraction Gas Chromatography Time-of-flight Mass spectrome...

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

Analytical chemistry
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 ...

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence.

Analytical chemistry
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...

Discrimination of coal geographical origins through HS-GC-IMS assisted with machine learning algorithms in larceny case.

Journal of chromatography. A
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 ...

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
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...

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
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...

Application and performance enhancement of FAIMS spectral data for deep learning analysis using generative adversarial network reinforcement.

Analytical biochemistry
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...