Application of Self-Organizing Maps to the Analysis of Ignitable Liquid and Substrate Pyrolysis Samples

被引:8
作者
Thurn, Nicholas [1 ]
Williams, Mary R. [2 ]
Sigman, Michael E. [1 ,2 ]
机构
[1] Univ Cent Florida, Dept Chem, POB 162367, Orlando, FL 32816 USA
[2] Univ Cent Florida, Natl Ctr Forens Sci, POB 162367, Orlando, FL 32816 USA
关键词
fire debris; Kohonen networks; self-organizing maps; arson; CLASSIFICATION;
D O I
10.3390/separations5040052
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Classification of un-weathered ignitable liquids is a problem that is currently addressed by visual pattern recognition under the guidelines of Standard Test Method for Ignitable Liquid Residues in Extracts from Fire Debris Samples by Gas Chromatography-Mass Spectrometry, ASTM E1618-14. This standard method does not separately address the identification of substrate pyrolysis patterns. This report details the use of a Kohonen self-organizing map coupled with extracted ion spectra to organize ignitable liquids and substrate pyrolysis samples on a two-dimensional map with groupings that correspond to the ASTM-classifications and separate the substrate pyrolysis samples from the ignitable liquids. The component planes give important information regarding the ions from the extracted ion spectra that contribute to the different classes. Some additional insight is gained into grouping of substrate pyrolysis samples based on the nature of the unburned material as a wood or non-wood material. Further subclassification was not apparent from the self-organizing maps (SOM) results.
引用
收藏
页数:9
相关论文
共 50 条
[31]   Dynamic Formation of Self-Organizing Maps [J].
Fix, Jeremy .
ADVANCES IN SELF-ORGANIZING MAPS AND LEARNING VECTOR QUANTIZATION, 2014, 295 :25-34
[32]   Pairwise Elastic Self-Organizing Maps [J].
Hartono, Pitoyo ;
Take, Yuto .
2017 12TH INTERNATIONAL WORKSHOP ON SELF-ORGANIZING MAPS AND LEARNING VECTOR QUANTIZATION, CLUSTERING AND DATA VISUALIZATION (WSOM), 2017, :50-56
[33]   Self-organizing maps for texture classification [J].
Petrov, Nedyalko ;
Georgieva, Antoniya ;
Jordanov, Ivan .
NEURAL COMPUTING & APPLICATIONS, 2013, 22 (7-8) :1499-1508
[34]   Probabilistic PCA Self-Organizing Maps [J].
Lopez-Rubio, Ezequiel ;
Ortiz-de-Lazcano-Lobato, Juan Miguel ;
Lopez-Rodriguez, Domingo .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2009, 20 (09) :1474-1489
[35]   Assessment of clusteranalysis and self-organizing maps [J].
Petersohn, H .
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 1998, 6 (02) :139-149
[36]   Self-organizing maps by difference of convex functions optimization [J].
Hoai An Le Thi ;
Manh Cuong Nguyen .
DATA MINING AND KNOWLEDGE DISCOVERY, 2014, 28 (5-6) :1336-1365
[37]   SELF-ORGANIZING MAPS APPLIED TO DECLUSTERING IN PREFERENTIAL SAMPLING [J].
Ayache, N. K. ;
Santos, A. E. M. ;
Nascimento, A. E. A. ;
De Castro, S. A. B. ;
Da Silva, D. F. S. .
HOLOS, 2023, 39 (08)
[38]   Multivariate Student-t self-organizing maps [J].
Lopez-Rubio, Ezequiel .
NEURAL NETWORKS, 2009, 22 (10) :1432-1447
[39]   Quantitative self-organizing maps for clustering electron tomograms [J].
Pascual-Montano, A ;
Taylor, KA ;
Winkler, H ;
Pascual-Marqui, RD ;
Carazo, JM .
JOURNAL OF STRUCTURAL BIOLOGY, 2002, 138 (1-2) :114-122
[40]   Self-Organizing Maps for In Silico Screening and Data Visualization [J].
Digles, Daniela ;
Ecker, Gerhard F. .
MOLECULAR INFORMATICS, 2011, 30 (10) :838-846