An Analysis of Fourier Transform Infrared Spectroscopy Data to Predict Herpes Simplex Virus 1 Infection

• Main Author: Champion, Patrick D
• Format: Others
• Published: Digital Archive @ GSU 2008
• Subjects: Specificity; Bootstrap; ROC Curve; Kolmogorov-Smirnov; Partial least squares; Wilcoxon-Mann-Whitney; Disease predictor; Collinearity; FTIR; Mathematics
• Online Access: http://digitalarchive.gsu.edu/math_theses/62; http://digitalarchive.gsu.edu/cgi/viewcontent.cgi?article=1061&context=math_theses

The purpose of this analysis is to evaluate the usefulness of Fourier Transform Infrared (FTIR) spectroscopy in the detection of Herpes Simplex Virus 1 (hsv1) infection at an early stage. The raw absorption values were standardized to eliminate inter-sampling error. Wilcoxon-Mann-Whitney (WMW) statistic's Z score was calculated to select significant spectral regions. Partial least squares modeling was performed because of multicollinearity. Kolmogorov-Smirnov statistic showed models for healthy tissues from different time groups were not from same distribution. The additional 24 hour dataset was evaluated using the following methods. Variables were selected by WMW Z score. Difference of Composites statistic, DC, was created as a disease indicator and evaluated using area under the ROC curve, specificities, and confidence intervals using bootstrap algorithm. The specificity of DC was high, however the confidence intervals were large. Future studies are required with larger sample sizes to test this statistic's usefulness.