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Dimensionality reduction of multielement glass evidence to calculate likelihood ratios
Author(s) -
Gupta Anjali,
Corzo Ruthmara,
Akmeemana Anuradha,
Lambert Katelyn,
Jimenez Kenneth,
Curran James M.,
Almirall Jose R.
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.3298
Subject(s) - principal component analysis , dimensionality reduction , multivariate statistics , curse of dimensionality , calibration , computation , mathematics , multivariate analysis , reduction (mathematics) , statistics , analytical chemistry (journal) , computer science , pattern recognition (psychology) , chemistry , artificial intelligence , algorithm , chromatography , geometry
Dimensionality reduction of multivariate elemental concentrations of glass is reported for computing likelihood ratios ( LR s). The LR s calculated using principal component analysis (PCA) and a post hoc calibration steps result in very low ( < 1%) false inclusions when comparing glass samples known to originate from different sources and very low ( < 1%) false exclusions when comparing glass samples known to originate from the same source. The LR s calculated using the novel PCA approach are compared with previously reported LR s calculated using a more computationally intensive Multivariate Kernel (MVK) model followed by a calibration step using a Pool Adjacent Violators (PAV) algorithm. In both cases, the calibrated LR s limited the magnitude of the misleading evidence, providing only weak to moderate support for the incorrect hypotheses. Most of the different pairs that were found to be falsely included were explained by chemical relatedness (same manufacturer of the glass sources in very close time interval between manufacture). The computation of LR s using dimensionality reduction of elemental concentrations using PCA may transfer to other multivariate data‐generating evidence types.