Application of pattern recognition techniques to mass spectrometric data for sequencing C-terminal peptide residue series

1993 ◽  
Vol 33 (3) ◽  
pp. 332-337 ◽  
Author(s):  
Rupika Delgoda ◽  
James D. Pulfer
Keyword(s):  
Pattern Recognition ◽  
Mass Spectrometric ◽  
Mass Spectrometric Data ◽  
Spectrometric Data ◽  
Pattern Recognition Techniques ◽  
Peptide Residue ◽  
Residue Series

Application of Factor Analysis in Imaging Mass Spectrometric Data Analysis

2014 ◽  
Vol 42 (8) ◽  
pp. 1099-1103 ◽  
Author(s):  
Yi CHEN ◽  
Fei TANG ◽  
Tie-Gang LI ◽  
Jiu-Ming HE ◽  
Zeper ABLIZ ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Data Analysis ◽  
Mass Spectrometric ◽  
Mass Spectrometric Data ◽  
Spectrometric Data

Two-dimensional LC-MS fractioning and cross-matching of mass spectrometric data for rational identification of bioactive compounds in crude extracts

2011 ◽  
Vol 34 (23) ◽  
pp. 3359-3363 ◽  
Author(s):  
Geir Klinkenberg ◽  
Håvard Sletta ◽  
Espen Fjaervik ◽  
Kolbjørn Zahlsen ◽  
Per Bruheim
Keyword(s):  
Bioactive Compounds ◽  
Mass Spectrometric ◽  
Two Dimensional ◽  
Mass Spectrometric Data ◽  
Spectrometric Data ◽  
Crude Extracts ◽  
Cross Matching

Deconvolution and spectral clean-up of two-component mixtures by factor analysis of gas chromatographic–mass spectrometric data

The Analyst ◽  
1996 ◽  
Vol 121 (8) ◽  
pp. 993-1001 ◽  
Author(s):  
Peter Hindmarch ◽  
Cevdet Demir ◽  
Richard G. Brereton
Keyword(s):  
Factor Analysis ◽  
Mass Spectrometric ◽  
Mass Spectrometric Data ◽  
Spectrometric Data ◽  
Two Component ◽  
Chromatographic Mass

scholarly journals Dynamic Cluster Analysis: An Unbiased Method for Identifying A + 2 Element Containing Compounds in Liquid Chromatographic High-Resolution Time-of-Flight Mass Spectrometric Data

2016 ◽  
Vol 88 (24) ◽  
pp. 12461-12469 ◽  
Author(s):  
Aaron John Christian Andersen ◽  
Per Juel Hansen ◽  
Kevin Jørgensen ◽  
Kristian Fog Nielsen
Keyword(s):  
Cluster Analysis ◽  
High Resolution ◽  
Time Of Flight ◽  
Mass Spectrometric ◽  
Resolution Time ◽  
Mass Spectrometric Data ◽  
Spectrometric Data ◽  
Dynamic Cluster ◽  
Liquid Chromatographic

scholarly journals Constructing a data-driven receptor model for organic and inorganic aerosol – a synthesis analysis of eight mass spectrometric data sets from a boreal forest site

2019 ◽  
Vol 19 (6) ◽  
pp. 3645-3672 ◽  
Author(s):  
Mikko Äijälä ◽  
Kaspar R. Daellenbach ◽  
Francesco Canonaco ◽  
Liine Heikkinen ◽  
Heikki Junninen ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Organic Aerosol ◽  
Mass Spectrometric ◽  
Chemical Components ◽  
Forest Site ◽  
Aerosol Composition ◽  
Mass Spectrometric Data ◽  
Spectrometric Data ◽  
Aerosol Mass ◽  
Inorganic Aerosol

Abstract. The interactions between organic and inorganic aerosol chemical components are integral to understanding and modelling climate and health-relevant aerosol physicochemical properties, such as volatility, hygroscopicity, light scattering and toxicity. This study presents a synthesis analysis for eight data sets, of non-refractory aerosol composition, measured at a boreal forest site. The measurements, performed with an aerosol mass spectrometer, cover in total around 9 months over the course of 3 years. In our statistical analysis, we use the complete organic and inorganic unit-resolution mass spectra, as opposed to the more common approach of only including the organic fraction. The analysis is based on iterative, combined use of (1) data reduction, (2) classification and (3) scaling tools, producing a data-driven chemical mass balance type of model capable of describing site-specific aerosol composition. The receptor model we constructed was able to explain 83±8 % of variation in data, which increased to 96±3 % when signals from low signal-to-noise variables were not considered. The resulting interpretation of an extensive set of aerosol mass spectrometric data infers seven distinct aerosol chemical components for a rural boreal forest site: ammonium sulfate (35±7 % of mass), low and semi-volatile oxidised organic aerosols (27±8 % and 12±7 %), biomass burning organic aerosol (11±7 %), a nitrate-containing organic aerosol type (7±2 %), ammonium nitrate (5±2 %), and hydrocarbon-like organic aerosol (3±1 %). Some of the additionally observed, rare outlier aerosol types likely emerge due to surface ionisation effects and likely represent amine compounds from an unknown source and alkaline metals from emissions of a nearby district heating plant. Compared to traditional, ion-balance-based inorganics apportionment schemes for aerosol mass spectrometer data, our statistics-based method provides an improved, more robust approach, yielding readily useful information for the modelling of submicron atmospheric aerosols physical and chemical properties. The results also shed light on the division between organic and inorganic aerosol types and dynamics of salt formation in aerosol. Equally importantly, the combined methodology exemplifies an iterative analysis, using consequent analysis steps by a combination of statistical methods. Such an approach offers new ways to home in on physicochemically sensible solutions with minimal need for a priori information or analyst interference. We therefore suggest that similar statistics-based approaches offer significant potential for un- or semi-supervised machine-learning applications in future analyses of aerosol mass spectrometric data.

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