Literature DB >> 24502629

Progress toward the determination of correct classification rates in fire debris analysis II: utilizing soft independent modeling of class analogy (SIMCA).

Erin E Waddell1, Mary R Williams, Michael E Sigman.   

Abstract

A multistep classification scheme was used to detect and classify ignitable liquid residues in fire debris into the classes defined by the ASTM E1618-10 standard method. The total ion spectra (TIS) of the samples were classified by soft independent modeling of class analogy (SIMCA) with cross-validation and tested on fire debris. For detection of ignitable liquid residue, the true-positive rate was 94.2% for cross-validation and 79.1% for fire debris, with false-positive rates of 5.1% and 8.9%, respectively. Evaluation of SIMCA classifications for fire debris relative to a reviewer's examination led to an increase in the true-positive rate to 95.1%; however, the false-positive rate also increased to 15.0%. The correct classification rates for assigning ignitable liquid residues into ASTM E1618-10 classes were generally in the range of 80-90%, with the exception of gasoline samples, which were incorrectly classified as aromatic solvents following evaporative weathering in fire debris.
© 2014 American Academy of Forensic Sciences.

Keywords:  chemometrics; fire debris; forensic science; gas chromatography-mass spectrometry; multivariate statistics; soft independent modeling of class analogy

Year:  2014        PMID: 24502629     DOI: 10.1111/1556-4029.12417

Source DB:  PubMed          Journal:  J Forensic Sci        ISSN: 0022-1198            Impact factor:   1.832


  2 in total

1.  Determination of Ignitable Liquids in Fire Debris: Direct Analysis by Electronic Nose.

Authors:  Marta Ferreiro-González; Gerardo F Barbero; Miguel Palma; Jesús Ayuso; José A Álvarez; Carmelo G Barroso
Journal:  Sensors (Basel)       Date:  2016-05-13       Impact factor: 3.576

2.  Effects of Fire Suppression Agents and Weathering in the Analysis of Fire Debris by HS-MS eNose.

Authors:  Barbara Falatová; Marta Ferreiro-González; Carlos Martín-Alberca; Danica Kačíková; Štefan Galla; Miguel Palma; Carmelo G Barroso
Journal:  Sensors (Basel)       Date:  2018-06-14       Impact factor: 3.576

  2 in total

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