| 光谱学与光谱分析 |
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| Data Analysis of Laser Desorption/Ionization Mass Spectrum of Individual Particle Using Adaptive Resonance Theory Based Neural Network |
| LIN Ying, GUO Xiao-yong, GU Xue-jun, XIA Wei-wei, ZHENG Hai-yang, ZHANG Wei-jun, FANG Li |
| Lab of Environmental Spectroscopy, Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences, Hefei 230031, China |
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Abstract On-line measurement of size and chemical composition of single particle using an aerosol laser time-of-flight mass spectrometer (ALTOFMS) was designed in our lab. Each particle’s aerodynamic diameter is determined by measuring the delay time between two continuous-wave lasers operating at 650 nm. A Nd∶YAG laser desorbs and ionizes molecules from the particle, and the time-of-flight mass spectrometer collects a mass spectrum of the generated ions. Then the composition of single particle is obtained. ALTOFMS generates large amount of data during the process period. How to process these data quickly and extract valuable information is one of the key problems for the ALTOFMS. In the present paper, an adaptive resonance theory-based neural network, ART-2a algorithm, was used to classify mixed mass spectra of aerosol particles of NaCl, CaCl2, dioctylphthalate (DOP), and 2,5-dihydroxybenzoic acid (DHB). Compared with the traditional methods, ART-2a can recognize input patterns self-organically, self-adaptively and self-steadily without considering the complexity and the number of the patterns, so it is more favorable for the analysis of the mass spectra data. Experimental results show that when vigilance parameter is 0.40, learning rate is 0.05 and iteration number is 6, ART-2a algorithm can successfully reveal these four particle categories. The weight vectors for these four particle classes were obtained, which can represent the characters of these four particle classes remarkably.
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Received: 2007-11-26
Accepted: 2008-03-06
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Corresponding Authors:
LIN Ying
E-mail: linying6092@126.com
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