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  • 1.
    Alm, Erik
    et al.
    Stockholm University, Faculty of Science, Department of Analytical Chemistry.
    Bro, Rasmus
    Engelsen, Sören B.
    Karlberg, Bo
    Stockholm University, Faculty of Science, Department of Analytical Chemistry.
    Torgrip, Ralf J. O.
    Stockholm University, Faculty of Science, Department of Analytical Chemistry.
    Vibrational overtone combination spectroscopy (VOCSY)—a new way of using IR and NIR data2007In: Analytical and Bioanalytical Chemistry, ISSN 1618-2642, E-ISSN 1618-2650, Vol. 388, no 1, p. 179-188Article in journal (Refereed)
    Abstract [en]

    This work explores a novel method for rearranging 1st order (one-way) infra-red (IR) and/or near infra-red (NIR) ordinary spectra into a representation suitable for multi-way modelling and analysis. The method is based on the fact that the fundamental IR absorption and the first, second, and consecutive overtones of NIR absorptions represent identical chemical information. It is therefore possible to rearrange these overtone regions of the vectors comprising an IR and NIR spectrum into a matrix where the fundamental, 1st, 2nd, and consecutive overtones of the spectrum are arranged as either rows or columns in a matrix, resulting in a true three-way tensor of data for several samples. This tensorization facilitates explorative analysis and modelling with multi-way methods, for example parallel factor analysis (PARAFAC), N-way partial least squares (N-PLS), and Tucker models. The vibrational overtone combination spectroscopy (VOCSY) arrangement is shown to benefit from the “order advantage”, producing more robust, stable, and interpretable models than, for example, the traditional PLS modelling method. The proposed method also opens the field of NIR for true peak decomposition—a feature unique to the method because the latent factors acquired using PARAFAC can represent pure spectral components whereas latent factors in principal component analysis (PCA) and PLS usually do not.

  • 2.
    Csenki, Leonard
    et al.
    Stockholm University, Faculty of Science, Department of Analytical Chemistry.
    Alm, Erik
    Stockholm University, Faculty of Science, Department of Analytical Chemistry.
    Torgrip, Ralf J. O.
    Stockholm University, Faculty of Science, Department of Analytical Chemistry.
    Åberg, K. Magnus
    Stockholm University, Faculty of Science, Department of Analytical Chemistry.
    Nord, Lars I.
    Schuppe-Koistinen, Ina
    Lindberg, Johan
    Proof of principle of a generalized fuzzy Hough transform approach to peak alignment of one-dimensional 1H NMR data2007In: Analytical and Bioanalytical Chemistry, ISSN 1618-2642, E-ISSN 1618-2650, Vol. 389, no 3, p. 875-885Article in journal (Refereed)
    Abstract [en]

    In metabolic profiling, multivariate data analysis techniques are used to interpret one-dimensional (1D) 1H NMR data. Multivariate data analysis techniques require that peaks are characterised by the same variables in every spectrum. This location constraint is essential for correct comparison of the intensities of several NMR spectra. However, variations in physicochemical factors can cause the locations of the peaks to shift. The location prerequisite may thus not be met, and so, to solve this problem, alignment methods have been developed. However, current state-of-the-art algorithms for data alignment cannot resolve the inherent problems encountered when analysing NMR data of biological origin, because they are unable to align peaks when the spatial order of the peaks changes—a commonly occurring phenomenon. In this paper a new algorithm is proposed, based on the Hough transform operating on an image representation of the NMR dataset that is capable of correctly aligning peaks when existing methods fail. The proposed algorithm was compared with current state-of-the-art algorithms operating on a selected plasma dataset to demonstrate its potential. A urine dataset was also processed using the algorithm as a further demonstration. The method is capable of successfully aligning the plasma data but further development is needed to address more challenging applications, for example urine data.

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