Plant disease outbreaks pose serious threats to global food security. A rigid methodology that accounts for rapid identification of the earliest point of infection caused by plant viruses is necessary. Raman spectroscopy that generates spectral signatures of cellular-level dynamics resonates the virus induced alteration in plants through moderations in spectral features. Here, we present a model study to identify the earliest point of infection. Measured spectra from healthy and virus infected Arabidopsis thaliana plants are applied to principal component analysis. We found a separation as early as 8 days post inoculation between healthy and virus infected plants.
In Raman-based diagnostic applications, principal component analysis (PCA) has often been used to distinguish different cell types or abnormalities. The performance of PCA greatly depends on the baseline adjustment of the measured spectra. Hence, the effect of erroneous baseline fitting on PCA requires to be addressed. Thus, we investigate the impact of baseline error for Raman spectra on PCA through the application of polynomial function with different orders in the fingerprint region (~600-1800cm-1). We found that the third order polynomial baseline fitting generated the fitted spectra closest to the mean spectrum and provided more precise PCA results.
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