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

Separations ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 52 ◽  
Author(s):  
Nicholas Thurn ◽  
Mary Williams ◽  
Michael Sigman
Keyword(s):  
Standard Test ◽  
Test Method ◽  
Gas Chromatography Mass Spectrometry ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Ignitable Liquids ◽  
Fire Debris ◽  
Ignitable Liquid ◽  
Self Organizing

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.

scholarly journals Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 801
Author(s):  
Barbara Falatová ◽  
Marta Ferreiro-González ◽  
José Luis P. Calle ◽  
José Ángel Álvarez ◽  
Miguel Palma
Keyword(s):  
Accurate Determination ◽  
Hierarchical Cluster ◽  
Mass Spectrometry Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Linear Discriminant ◽  
Synthetic Fibers ◽  
Ignitable Liquids ◽  
Fire Debris ◽  
American Society ◽  
Ignitable Liquid

Interpretation of data from fire debris is considered as one of the most challenging steps in fire investigation. Forensic analysts are tasked to identify the presence or absence of ignitable liquid residues (ILRs) which may indicate whether a fire was started deliberately. So far, data analysis is subjected to human interpretation following the American Society for Testing and Materials’ guidelines (ASTM E1618) based on gas chromatography–mass spectrometry data. However, different factors such as interfering pyrolysis compounds may hinder the interpretation of data. Some substrates release compounds that are in the range of common ignitable liquids, which interferes with accurate determination of ILRs. The aim of the current research is to investigate whether headspace–mass spectroscopy electronic nose (HS-MS eNose) combined with pattern recognition can be used to classify different ILRs from fire debris samples that contain a complex matrix (petroleum-based substrates or synthetic fibers carpet) that can strongly interfere with their identification. Six different substrates—four petroleum-derived substrates (vinyl, linoleum, polyester, and polyamide carpet), as well as two different materials for comparison purposes (cotton and cork) were used to investigate background interferences. Gasoline, diesel, ethanol, and charcoal starter with kerosene were used as ignitable liquids. In addition, fire debris samples were taken after different elapsed times. A total of 360 fire debris samples were analyzed. The obtained total ion mass spectrum was combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) as well as supervised linear discriminant analysis (LDA). The results from HCA show a strong tendency to group the samples according to the ILs and substrate used, and LDA allowed for a full identification and discrimination of every ILR regardless of the substrate.

scholarly journals Evolution of insurance company service quality survey, using self-learning neural network

2011 ◽  
Vol 59 (2) ◽  
pp. 149-154
Author(s):  
Vladimír Konečný ◽  
Oldřich Trenz ◽  
Dana Dvořáková
Keyword(s):  
Experimental Model ◽  
Software Tool ◽  
Motor Vehicles ◽  
Self Organizing Map ◽  
Insurance Company ◽  
Critical Factors ◽  
Self Organizing Maps ◽  
Self Learning ◽  
Self Organizing

The objective of the paper is to demonstrate the abilities and possible approaches to classification of set of objects using self-organizing maps. As the objects, clients of an insurance company that made an agreement regarding mandatory insurance of motor vehicles were selected. The opinions of the clients and their overall satisfaction reflected in responses to presented answers. The clients were classified into three groups. The first two contained satisfied clients (i.e. good clients for the company), the last group contained clients that could potentially switch to the competitors. Subsequent analysis enabled discovering the reasons of low customer satisfaction and critical factors of losing the least satisfied clients. For the analysis of the responses (one hundred fifty-one) and the insurance company, experimental model of self-organizing map realized at the Department of informatics was used. Used experimental model has proved very effective software tool.

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