Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations : optimizing the classification approach

• The present work compares the dissimilarity and covariance baseckinsupervised chemometric classification approaches by, taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Mikrobiologi inom det medicinska området (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Microbiology in the medical area (hsv//eng)

Nyckelord

TSFS

Eigenvalue-Eigen vector decomposition

Dissimilarity

Covariance

PLSDA

Classification

Publikations- och innehållstyp

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