Classification of cola beverages on the basis of mass spectra measured by membrane inlet mass spectrometry

Author(s):  
Raimo A. Ketola ◽  
Jukka Heikkonen ◽  
Sulo Piepponen ◽  
Frants R. Lauritsen ◽  
Tapio Kotiaho
2018 ◽  
Author(s):  
Paolo Inglese ◽  
Gonçalo Correia ◽  
Pamela Pruski ◽  
Robert C Glen ◽  
Zoltan Takats

AbstractSupervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images, and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models.Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to co-expression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.


2020 ◽  
Author(s):  
Jie Cheng ◽  
Yuchen Tang ◽  
Baoquan Bao ◽  
Ping Zhang

<p><a></a><a></a><a></a><a><b>Objective</b></a>: To screen all compounds of Agsirga based on the HPLC-Q-Exactive high-resolution mass spectrometry and find potential inhibitors that can respond to 2019-nCoV from active compounds of Agsirga by molecular docking technology.</p> <p><b>Methods</b>: HPLC-Q-Exactive high-resolution mass spectrometry was adopted to identify the complex components of Mongolian medicine Agsirga, and separated by the high-resolution mass spectrometry Q-Exactive detector. Then the Orbitrap detector was used in tandem high-resolution mass spectrometry, and the related molecular and structural formula were found by using the chemsipider database and related literature, combined with precise molecular formulas (errors ≤ 5 × 10<sup>−6</sup>) , retention time, primary mass spectra, and secondary mass spectra information, The fragmentation regularities of mass spectra of these compounds were deduced. Taking ACE2 as the receptor and deduced compounds as the ligand, all of them were pretreated by discover studio, autodock and Chem3D. The molecular docking between the active ingredients and the target protein was studied by using AutoDock molecular docking software. The interaction between ligand and receptor is applied to provide a choice for screening anti-2019-nCoV drugs.</p> <p><b>Result</b>: Based on the fragmentation patterns of the reference compounds and consulting literature, a total of 96 major alkaloids and stilbenes were screened and identified in Agsirga by the HPLC-Q-Exactive-MS/MS method. Combining with molecular docking, a conclusion was got that there are potential active substances in Mongolian medicine Agsirga which can block the binding of ACE2 and 2019-nCoV at the molecular level.</p>


2018 ◽  
Author(s):  
Zhiwu An ◽  
Fuzhou Gong ◽  
Yan Fu

We have developed PTMiner, a first software tool for automated, confident filtering, localization and annotation of protein post-translational modifications identified by open (mass-tolerant) search of large tandem mass spectrometry datasets. The performance of the software was validated on carefully designed simulation data. <br>


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