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SUMMARY:STAT IR-9
DTSTART:20270526T070000Z
DTEND:20270528T164500Z
DTSTAMP:20261022T044900Z
UID:indico-event-187@indico.synchrotron-soleil.fr
CONTACT:STAT-IR9-2027@synchrotron-soleil.fr
DESCRIPTION:WELCOME\n \nThe SMIS beamline at the SOLEIL synchrotron is pl
 eased to announce the 9th edition of its training workshop\, STAT-IR 9\, f
 ocused on multivariate and machine learning analysis of infrared microspec
 troscopy data. The three-day course will take place from Wednesday\, May 2
 6th to Friday\, June 28th\, 2025\, at the SOLEIL synchrotron and will be c
 onducted in English.\nThe workshop aims to provide participants with hands
 -on experience on the use of Multivariate Analysis (MVA) techniques such a
 s PCA\, and Machine Learning (ML) methods including both unsupervised and 
 supervised clustering approaches for infrared microspectroscopy data analy
 sis. The course will also cover spectral preprocessing techniques used in 
 preparation for MVA and ML. Additionally\, the course will cover the use o
 f these techniques in analyzing hyperspectral images and maps. The trainin
 g program will emphasize biomedical datasets\, though the methodologies ar
 e equally applicable to data from cultural heritage and polymer sciences. 
 These techniques can also be extended to data obtained from Raman microspe
 ctroscopy and X-ray fluorescence.\nInfrared microspectroscopy and imaging 
 provide essential insights into the chemical composition\, spatial organiz
 ation\, and molecular conformation of complex materials with spatial resol
 utions ranging from microns to submicron. Efficient data analysis is key t
 o unlocking the rich information embedded in infrared spectra\, especially
  when dealing with large datasets comprising tens to hundreds of thousands
  of spectra.  This requires the use of automated techniques to effectivel
 y extract meaningful information. \nOne of the most effective approaches t
 o handling large datasets is statistical analysis which can reveal spectra
 l variability\, identify correlations\, classify data objectively\, train 
 identification algorithms\, integrate data from multiple techniques\, and 
 establish predictive models for quantification. \nThe success of these met
 hods hinges on the application of appropriate spectral preprocessing to el
 iminate artifacts and confusing variations. Proper data analysis strategie
 s must be employed to validate statistical models and guarantee that they 
 are robust and generalizable. Mastery of these specialized software tools 
 and techniques requires dedicated training in signal processing that will 
 be taught during the workshop.\nThe SMIS beamline at the SOLEIL Synchrotro
 n\, located near Paris\, has over 15 years of expertise in the measurement
  and analysis of microspectroscopy data. This course will be led by:\n \n
 \nFerenc Borondics\, expert in physical chemistry\,\n\n \n\nMarko Toplak\
 , machine learning specialist and one of the developers of Quasar software
 \,\n\n \n\nChristophe Sandt\, biospectroscopy expert.\n\n \nThe course w
 ill utilize the open-source software Quasar\, and participants will have t
 he opportunity for hands-on practice with their own data.\nThe workshop wi
 ll take place on-site at the SOLEIL synchrotron and is open to all past an
 d future users of the SMIS beamline. The registration fee is 120 €\, whi
 ch includes on-site meals.\n  \n\nhttps://indico.synchrotron-soleil.fr/ev
 ent/187/
LOCATION:Principal/0-59 - Salle A1.0.59/Phénix (Batiment Principal)
URL:https://indico.synchrotron-soleil.fr/event/187/
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