The Quantitative Analysis of Multicomponent Gaseous Mixtures of Organic Compounds by FT-IR

Wang, Junde; Clench, Malcolm R.; Chen, Zuoru; Luo, Yunhua; Mowthorpe, D. J.; Cooke, Michael

doi:10.1080/00387019708002593

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Since Dockery et al [1] published their study linking aerosol particles to human morbidity and mortality, researchers have been raising their attention to the source and effects of atmospheric particulate matter. The investigation of aerosol particle conventionally aims to address the following problems [2]: (1) Chemical composition of particle. (2) The influence of chemical component of particle on human being.…”

Section: Introductionmentioning

confidence: 99%

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Application of Self-Organizing Map in Aerosol Single Particles Data Clustering

Wen

Guo

Huang

et al. 2007

2007 International Joint Conference on Neural Networks

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In this paper, Self-Organizing Map (SOM) is used to visualize and cluster the data set of aerosol single particle mass spectrum, which was collected by aerosol time-of-flight mass spectrometry (ATOFMS). In view of the characteristic feature of aerosol particle data, the TF-IDF scheme used widely in document clustering is employed to preprocess. Subsequently for data clustering analysis, a two-level clustering framework is proposed, wherein SOM is firstly used to cluster input data and get the primary results, and then the results are again clustered by semiautomatic k-means algorithm. In order to demonstrate the validity of clustering, the chemical significance for cluster centroid is also investigated, wherein inorganic salts, "CalciumContaining" particles, biogenic soot particles, and carbonaceous particles etc. are identified.

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Section: Introductionmentioning

confidence: 99%

“…The investigation of aerosol particle conventionally aims to address the following problems [2]: (1) Chemical composition of particle. (2) The influence of chemical component of particle on human being. (3) The source of particle and its ratio of contribution.…”

Section: Introductionmentioning

confidence: 99%

Application of Self-Organizing Map in Aerosol Single Particles Data Clustering

Wen

Guo

Huang

et al. 2007

2007 International Joint Conference on Neural Networks

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Infrared Spectroscopy

et al. 1998

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Simultaneous Determination of Multicomponents in Air Toxic Organic Compounds Using Artificial Neural Networks in Ftir Spectroscopy

Yan

Yang

Wang

et al. 1999

Spectroscopy Letters

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The application of Artificial Neural Networks (ANNsj for nonlinear multivariate calibration using simulated FTIR data was demonstrated in this paper. Neural networks consisting of three layers of nodes were trained by using the back-propagation iearning rule. Since parameters affect the performance of the network greatly, simulated data were used to train the network in order to get a satisfactory combination of all parameters. The mixtures of four air toxic organic compounds whose FTIR spectra are overlapped were chosen to evaluate the calibration and prediction ability of the network. The relative standard error (RSD%), the percent standard error of prediction samples (YoSEP) and the percent standard error of calibration samples (%SEC) are used for evaluating the ability of the neural network.

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The Quantitative Analysis of Multicomponent Gaseous Mixtures of Organic Compounds by FT-IR

Cited by 3 publications

References 14 publications

Application of Self-Organizing Map in Aerosol Single Particles Data Clustering

Application of Self-Organizing Map in Aerosol Single Particles Data Clustering

Infrared Spectroscopy

Simultaneous Determination of Multicomponents in Air Toxic Organic Compounds Using Artificial Neural Networks in Ftir Spectroscopy

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