Evaluation of the Simultaneous Analysis of Organic and Inorganic Gunshot Residues Within a Large Population Data Set Using Electrochemical Sensors* , †
| Autor: | Luis E. Arroyo, Colby E. Ott, Ana Lorena Alvarado-Gámez, Kourtney A. Dalzell, Tatiana Trejos, Pedro José Calderón-Arce |
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| Rok vydání: | 2020 |
| Předmět: |
Detection limit
education.field_of_study Artificial neural network Computer science business.industry 010401 analytical chemistry Population Large population Pattern recognition 01 natural sciences 0104 chemical sciences Pathology and Forensic Medicine Data set 03 medical and health sciences Naive Bayes classifier 0302 clinical medicine Current practice Genetics Population data 030216 legal & forensic medicine Artificial intelligence business education |
| Zdroj: | Journal of Forensic Sciences. 65:1935-1944 |
| ISSN: | 1556-4029 0022-1198 |
| DOI: | 10.1111/1556-4029.14548 |
| Popis: | The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen-printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4-dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field-testing technique and assist with decisions in case management. |
| Databáze: | OpenAIRE |
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