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
[21]   Fast Self-Organizing Maps Training [J].
Giobergia, Flavio ;
Baralis, Elena .
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2019, :2257-2266
[22]   Initialization Issues in Self-organizing Maps [J].
Valova, Iren ;
Georgiev, George ;
Gueorguieva, Natacha ;
Olson, Jacob .
COMPLEX ADAPTIVE SYSTEMS: EMERGING TECHNOLOGIES FOR EVOLVING SYSTEMS: SOCIO-TECHNICAL, CYBER AND BIG DATA, 2013, 20 :52-57
[23]   Fault tolerance of self-organizing maps [J].
Bernard Girau ;
Cesar Torres-Huitzil .
Neural Computing and Applications, 2020, 32 :17977-17993
[24]   Self-Organizing Maps with supervised layer [J].
Platon, Ludovic ;
Zehraoui, Farida ;
Tahi, Fariza .
2017 12TH INTERNATIONAL WORKSHOP ON SELF-ORGANIZING MAPS AND LEARNING VECTOR QUANTIZATION, CLUSTERING AND DATA VISUALIZATION (WSOM), 2017, :161-168
[25]   Fault tolerance of self-organizing maps [J].
Girau, Bernard ;
Torres-Huitzil, Cesar .
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (24) :17977-17993
[26]   Self-organizing maps for texture classification [J].
Nedyalko Petrov ;
Antoniya Georgieva ;
Ivan Jordanov .
Neural Computing and Applications, 2013, 22 :1499-1508
[27]   Hyperparameter Tuning for Self-Organizing Maps [J].
Guerin, Axel ;
Chauvet, Pierre ;
Saubion, Frederic .
2024 CONFERENCE ON AI, SCIENCE, ENGINEERING, AND TECHNOLOGY, AIXSET, 2024, :228-235
[28]   Self-Organizing Maps with Convolutional Layers [J].
Elend, Lars ;
Kramer, Oliver .
ADVANCES IN SELF-ORGANIZING MAPS, LEARNING VECTOR QUANTIZATION, CLUSTERING AND DATA VISUALIZATION, WSOM+ 2019, 2020, 976 :23-32
[29]   Fuzzy Relational Self-Organizing Maps [J].
Khalilia, Mohammed ;
Popescu, Mihail .
2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2012,
[30]   Monitoring Blockchains with Self-Organizing Maps [J].
Chawathe, Sudarshan S. .
2018 17TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (IEEE TRUSTCOM) / 12TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING (IEEE BIGDATASE), 2018, :1870-1875