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  • 1. Bengtsson, M.
    et al.
    Wallström, Stina
    KTH, School of Chemical Science and Engineering (CHE), Fibre and Polymer Technology.
    Sjöholm, M.
    Grönlund, R.
    Anderson, B.
    Larsson, A.
    Karlsson, Sigbritt
    KTH, School of Chemical Science and Engineering (CHE), Fibre and Polymer Technology.
    Kröll, Stefan
    Svanberg, S.
    Fungus covered insulator materials studied with laser-induced fluorescence and principal component analysis2005In: Applied Spectroscopy, ISSN 0003-7028, E-ISSN 1943-3530, Vol. 59, no 8, p. 1037-1041Article in journal (Refereed)
    Abstract [en]

    A method combining laser-induced fluorescence and principal component analysis to detect and discriminate between algal and fungal growth on insulator materials has been studied. Eight fungal cultures and four insulator materials have been analyzed. Multivariate classifications were utilized to characterize the insulator material, and fungal growth could readily be distinguished from a clean surface. The results of the principal component analyses make it possible to distinguish between algae infected, fungi infected, and clean silicone rubber materials. The experiments were performed in the laboratory using a fiber-optic fluorosensor that consisted of a nitrogen laser and an optical multi-channel analyzer system.

  • 2. Cappel, Ute B
    et al.
    Bell, Ian M
    Pickard, Laura K
    Removing cosmic ray features from Raman map data by a refined nearest neighbor comparison method as a precursor for chemometric analysis.2010In: Applied Spectroscopy, ISSN 0003-7028, E-ISSN 1943-3530, Vol. 64, no 2, p. 195-200Article in journal (Refereed)
    Abstract [en]

    An algorithm to remove cosmic ray (CR) features from Raman spectra collected in mapping experiments using a charge-coupled device (CCD) is presented. Each spectrum is compared to spectra collected from adjacent points in space using correlation values. The most similar neighbor (MSN) spectrum is selected, offset, and used for identification of CRs. The offset values are defined in terms of the noise level for data with a low signal-to-noise ratio and in terms of the peak height for data with a high signal-to-noise ratio. Scaled intensity values of the MSN spectra are used for replacement of contaminated pixels, allowing for full recovery of underlying spectral features. The algorithm is applicable for any Raman map where the particle sizes within the analyzed mixture are larger than the sampling size or to any other data where the sampling is more frequent than the variation, e.g., time series or temperature profiles. Its application to several maps of pharmaceutical samples is discussed here. With an appropriate offset value for the MSN spectra, no misdetections occur, and all CRs more intense than the offset are removed, which includes the CRs that would have hampered subsequent chemometric analysis by methods such as principal component analysis (PCA).

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