Raman spectra unmixing to identify waste polymers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F25%3A00014208" target="_blank" >RIV/46747885:24410/25:00014208 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.softx.2025.102411" target="_blank" >https://doi.org/10.1016/j.softx.2025.102411</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.softx.2025.102411" target="_blank" >10.1016/j.softx.2025.102411</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Raman spectra unmixing to identify waste polymers

Popis výsledku v původním jazyce

This paper explores advanced methodology for special spectral decomposition into components (endmembers) corresponding to real chemical components in the mixture.. The multivariate linear technique based on principal component analysis (PCA), realized by singular value decomposition (SVD) and subsequent transformation (rotation) by independent component analysis, is used for dimensionality reduction and transforming PCA components to meaningful endmembers. Ensuring that the extracted endmembers correspond to real chemical components in the mixture, the statistical independence among extracted endmembers and their constraints to be non-negative with their sum should be fulfilled. This problem is solved using constrained quadratic programming by the Newton method. The proposed algorithm is validated by demixing simulated spectra containing four components. The RAMIX program is written in the Python language, which is used for the analysis of simulated and experimental RAMAN spectra of polymeric mixtures. The reconstructed concentrations are compared with the true original concentration, with very low differences. An example of premortem plastics mixture waste chip Raman spectra analysis shows the usefulness of this approach to analyzing real polymeric mixtures.

Název v anglickém jazyce

Raman spectra unmixing to identify waste polymers

Popis výsledku anglicky

This paper explores advanced methodology for special spectral decomposition into components (endmembers) corresponding to real chemical components in the mixture.. The multivariate linear technique based on principal component analysis (PCA), realized by singular value decomposition (SVD) and subsequent transformation (rotation) by independent component analysis, is used for dimensionality reduction and transforming PCA components to meaningful endmembers. Ensuring that the extracted endmembers correspond to real chemical components in the mixture, the statistical independence among extracted endmembers and their constraints to be non-negative with their sum should be fulfilled. This problem is solved using constrained quadratic programming by the Newton method. The proposed algorithm is validated by demixing simulated spectra containing four components. The RAMIX program is written in the Python language, which is used for the analysis of simulated and experimental RAMAN spectra of polymeric mixtures. The reconstructed concentrations are compared with the true original concentration, with very low differences. An example of premortem plastics mixture waste chip Raman spectra analysis shows the usefulness of this approach to analyzing real polymeric mixtures.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

SOFTWAREX>

ISSN

2352-7110

e-ISSN

—

Svazek periodika

32

Číslo periodika v rámci svazku

DEC

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

7

Strana od-do

—

Kód UT WoS článku

001601279600001

EID výsledku v databázi Scopus

2-s2.0-105020971674