@article{fdi:010072344,
  title = {{C}ombining {R}aman microspectrometry and chemometrics for determining quantitative molecular composition and mixing state of atmospheric aerosol particles},
  author = {{S}iepka, {D}. and {U}zu, {G}a{\¨e}lle and {S}tefaniak, {E}. {A}. and {S}obanska, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{D}etermining quantitative molecular composition of atmospheric particles is required for assessing their environmental and health impacts. {T}he presented algorithm was designed to analyse numerous {R}aman spectra of metal rich atmospheric particles. {M}ultivariate curve resolution-alternating least squares procedure ({MCR}-{ALS}) has been applied to resolve complex data from {R}aman microanalysis by means of a computer-assisted analytical procedure called {S}ingle {P}article {A}nalysis ({SPA}). {T}he {SPA} - contrary to {R}aman mapping - provides data in which each single particle is assigned to a single spectrum, in the group with a statistically significant size. {D}uring the procedure, the relative contributions of individual compounds in the recorded {R}aman spectra have been specified. {G}rouping and relationship determination of the collected data have been performed by hierarchical cluster analysis ({HCA}) and principal component analysis ({PCA}). {A} new methodology is proposed to quantitatively determine the molecular composition and chemical mixing of single airborne particles based on the data from the automated {R}aman microspectrometry measurements.},
  keywords = {{R}aman microspectroscopy ; {A}erosol particles ; {M}ultivariate {C}urve ; {R}esolution ; {M}ultivariate data analysis ; {Q}uantitative {R}aman analysis},
  booktitle = {},
  journal = {{M}icrochemical {J}ournal},
  volume = {137},
  numero = {},
  pages = {119--130},
  ISSN = {0026-265{X}},
  year = {2018},
  DOI = {10.1016/j.microc.2017.10.005},
  URL = {https://www.documentation.ird.fr/hor/fdi:010072344},
}