Classification of Atmospheric Individual Aerosol Particles Sampled by Time-of-flight Mass Spectrometry Using Self-Organizing Map
10.11895/j.issn.0253-3820.131136
- VernacularTitle:用自组织特征映射神经网络对飞行时间质谱采集的大气气溶胶单粒子进行分类
- Author:
Xiaoyong GUO
;
Guozhu WEN
;
Deshuang HUANG
;
Li FANG
;
Weijun ZHANG
- Publication Type:Journal Article
- Keywords:
Individual aerosol particles;
Aerosol time-of-flight mass spectrometry;
Self-organizing map;
Clustering analysis
- From:
Chinese Journal of Analytical Chemistry
2014;(7):937-941
- CountryChina
- Language:Chinese
-
Abstract:
Large amount of data including chemical composition and size information of individual particles would be generated in the measurement of aerosol particles using atmospheric aerosol time-of-flight mass spectrometry ( ATOFMS ) . Our home-made ATOFMS was used to measure the indoor individual aerosol particles in real-time for 24 h, and the obtained mass spectrometric data were clustering analysis by self-organizing map ( SOM ) because of its ability of vector quantization and data dimensionality reduction. 20 classification results were got which includedCalcium-Containing,Salt+Secondary particles,Secondary particles,Organic Amines,K+-Rich Organics andSoil particles, etc. Compared with previous mass spectrometric methods, SOM is a natural visualization tool, more classification results can be obtained. This classification information would be useful to assess the response and toxicity of atmospheric aerosol particles and identify the origin of atmospheric aerosol particles.
