Abstract
The objective of this paper is a novel interpretation of the spectral and imaging data analysis process which takes into account the measurement of the variance caused by disease infestationof a cell. Using multivariate analysis, the Karhounen-LoeveExpansion(KLE) of hyperspectral reflectance data, taken from healthy and diseased states of several plant species, is used to identify a basis setof functions which represent the distribution of reflected signal energy. By spectral decomposition, the eigenvalues are related to the KLEbasis set. The eigenvalues can be used to identify the KLE eigenvectorswhich comprise the highest variation in the data. These componentscan be interpreted as the weighted variables which carry with themmost of the information on the reflectance spectrum of the cell. Fromindications presented by this multivariate KLE analysis, a frequencyreconstruction is adapted to convert the eigenvector information to awave function. This reconstruction via KLE and frequency transformation forms the signature identification process fordeveloping a database of healthy cell reflectance pattern features andvariations produced by disease or other factors. These frequencyspectra can be used as average signature reflectance patterns for cellidentification, classification and biomarkers for diseases. The defining of these spectral identification biomarkers or signatures, is purposefulsince it could lead to less invasive techniques for classification and disease diagnostics. The techniques used to determine thesereflectance spectra require a unique and rarely used transformationmethod. These processes need further testing and verification throughmultiple refinements of this procedure.