Augosto Asor Misolas, Marta Ferreiro-González, Miguel Palma
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引用次数: 0
Abstract
The identification and classification of ignitable liquid residues (ILRs) are among the most challenging steps in a fire investigation. Traditionally, fire debris samples are analyzed by GC–MS according to ASTM E1618 guidelines, where ILRs are determined via chromatographic pattern comparison with reference samples and classified according to ASTM classes. This complex and partially subjective task relies heavily on an experienced analyst and does not lend itself to automation. This study aims to provide alternative methods for the automatic data classification based on the total ion spectrum (TIS) combined with machine learning (ML) algorithms. We evaluated and compared thermal desorption of ILRs from activated charcoal strips (ACS samples) and direct headspace analysis of different fire debris samples to develop automated discrimination of different ILRs. Hierarchical cluster analysis (HCA) and supervised ML algorithms like linear discriminant analysis (LDA), support vector machines (SVM), and random forest (RF) were employed. The samples tended to initially cluster according to the presence/absence of ILRs followed by the type of ILR. Three datasets were tested (1) ILRs presence/absence, (2) ILR classification, and (3) combination of both. For ACS samples, all the datasets achieved 100% classification accuracies. For direct headspace analysis, dataset (1) resulted in 100% accuracies when the three algorithms were used. Models using dataset (2) and (3) achieved less than 100%. A prototype web-based application for the automatized discrimination of ACS samples was created using the SVM model. The application of ML algorithms demonstrated the effectiveness of the developed methods as supplementary to traditional ASTM method. These methods provide significant potential for automating the classification of ILRs therefore they can be useful to the experts in interpreting fire debris data.
期刊介绍:
The Microchemical Journal is a peer reviewed journal devoted to all aspects and phases of analytical chemistry and chemical analysis. The Microchemical Journal publishes articles which are at the forefront of modern analytical chemistry and cover innovations in the techniques to the finest possible limits. This includes fundamental aspects, instrumentation, new developments, innovative and novel methods and applications including environmental and clinical field.
Traditional classical analytical methods such as spectrophotometry and titrimetry as well as established instrumentation methods such as flame and graphite furnace atomic absorption spectrometry, gas chromatography, and modified glassy or carbon electrode electrochemical methods will be considered, provided they show significant improvements and novelty compared to the established methods.