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Two‐stage approach for the inference of the source of high‐dimensional and complex chemical data in forensic science
Author(s) -
Ausdemore Madeline A.,
Neumann Cedric,
Saunders Christopher P.,
Armstrong Douglas,
Muehlethaler Cyril
Publication year - 2021
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3247
Subject(s) - inference , computer science , leverage (statistics) , bayes' theorem , statistical inference , kernel (algebra) , identification (biology) , data mining , machine learning , artificial intelligence , bayesian probability , mathematics , statistics , botany , combinatorics , biology
Abstract Forensic chemists are often criticised for the lack of quantitative support for the conclusions of their examinations. While scholars advocate for the use of a Bayes factor to quantify the weight of forensic evidence, it is often impossible to assign the necessary probability measures to perform likelihood‐based inference on chemical data. To address this issue, we leverage the properties of kernel functions to offer a method that allows for statistically supporting the inference of the identity of source of sets of trace and control objects by way of a single test. Our method is generic in that it can be easily tailored to any type of data encountered in forensic chemistry, and our method does not depend on the dimension or the type of the considered data. The application of our method to paint evidence analysed by FTIR shows that this type of evidence carries substantial probative value. Finally, our approach can easily be extended to other types of chemical evidence such as glass, fibres, and dust.