DC pole | Wartość | Język |
dc.contributor.author | Martyna, Agnieszka | - |
dc.contributor.author | Menżyk, Alicja | - |
dc.contributor.author | Damin, Alessandro | - |
dc.contributor.author | Michalska, Aleksandra | - |
dc.contributor.author | Martra, Gianmario | - |
dc.contributor.author | Alladio, Eugenio | - |
dc.contributor.author | Zadora, Grzegorz | - |
dc.date.accessioned | 2020-05-22T07:41:10Z | - |
dc.date.available | 2020-05-22T07:41:10Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Chemometrics and Intelligent Laboratory Systems, Vol. 202 (2020), Art. No. 104029 | pl_PL |
dc.identifier.issn | 0169-7439 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12128/14190 | - |
dc.description.abstract | Discrimination of the samples into predefined groups is the issue at hand in many fields, such as medicine,
environmental and forensic studies, etc. Its success strongly depends on the effectiveness of groups separation,
which is optimal when the group means are much more distant than the data within the groups, i.e. the variation
of the group means is greater than the variation of the data averaged over all groups. The task is particularly
demanding for signals (e.g. spectra) as a lot of effort is required to prepare them in a way to uncover interesting
features and turn them into more meaningful information that better fits for the purpose of data analysis. The
solution can be adequately handled by using preprocessing strategies which should highlight the features relevant
for further analysis (e.g. discrimination) by removing unwanted variation, deteriorating effects, such as noise or
baseline drift, and standardising the signals. The aim of the research was to develop an automated procedure for
optimising the choice of the preprocessing strategy to make it most suitable for discrimination purposes. The
authors propose a novel concept to assess the goodness of the preprocessing strategy using the ratio of the
between-groups to within-groups variance on the first latent variable derived from regularised MANOVA that is
capable of exposing the groups differences for highly multidimensional data. The quest for the best preprocessing
strategy was carried out using the grid search and much more efficient genetic algorithm. The adequacy of this
novel concept, that remarkably supports the discrimination analysis, was verified through the assessment of the
capability of solving two forensic comparison problems - discrimination between differently-aged bloodstains and
various car paints described by Raman spectra - using likelihood ratio framework, as a recommended tool for
discriminating samples in the forensics. | pl_PL |
dc.language.iso | en | pl_PL |
dc.rights | Uznanie autorstwa 3.0 Polska | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/pl/ | * |
dc.subject | Signals preprocessing | pl_PL |
dc.subject | Regularised MANOVA | pl_PL |
dc.subject | Discrimination | pl_PL |
dc.subject | Raman spectra | pl_PL |
dc.subject | Likelihood ratio | pl_PL |
dc.title | Improving discrimination of Raman spectra by optimising preprocessing strategies on the basis of the ability to refine the relationship between variance components | pl_PL |
dc.type | info:eu-repo/semantics/article | pl_PL |
dc.identifier.doi | 10.1016/j.chemolab.2020.104029 | - |
Pojawia się w kolekcji: | Artykuły (WNŚiT)
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