University of Natural Resources and Life Sciences, Vienna (BOKU)  Research portal
Selected Publication:
Szalontai, B; Debreczeny, M; Fintor, K; Bagyinka, C.
(2020):
SVDclustering, a general imageanalyzing method explained and demonstrated on model and Raman microspectroscopic maps
SCI REPUK. 2020; 10(1), 4238
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 Abstract:
 An image analyzing method (SVDclustering) is presented. Amplitude vectors of SVD factorization (V1...Vi) were introduced into the imaging of the distribution of the corresponding Ui basisspectra. Since each Vi vector contains each point of the map, plotting them along the X, Y, Z dimensions of the map reconstructs the spatial distribution of the corresponding Ui basisspectrum. This gives valuable information about the first, second, etc. higherorder deviations present in the map. We extended SVD with a clustering method, using the significant Vi vectors from the VT matrix as coordinates of image points in a n(e)dimensional space (n(e) is the effective rank of the data matrix). This way every image point had a corresponding coordinate in the n(e)dimensional space and formed a point set. Clustering was applied to this point set. SVDclustering is universal; it is applicable to any measurement where data are recorded as a function of an external parameter (time, space, temperature, concentration, species, etc.). Consequently, our method is not restricted to spectral imaging, it can find application in many different 2D and 3D image analyses. Using SVDclustering, we have shown on models the theoretical possibilities and limitations of the method, especially in the context of creating, meaning/interpreting of cluster spectra. Then for realworld samples, two examples are presented, where we were able to reveal minute alterations in the samples (changing cation ratios in minerals, differently structured cellulose domains in plant root) with spatial resolution.
 Authors BOKU Wien:

Debreczeny Monika
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