The application of multiple linear regression methods to FTIR spectra of fingernails for predicting gender and age of human subjects.

The paper explores the accuracy of gender and age prediction of human subjects based on the chemometric analysis of FTIR spectra from fingernails. The baseline and scaling over the 0-1 range were applied to FTIR spectra from fingernails of 123 subjects, and wavenumbers for which absorbance values showed a statistically significant correlation with gender and age were identified. The prediction accuracy was analyzed using: Multiple linear regression, Forward stepwise regression, Backward stepwise regression, Principal component regression, and Partial least squares regression. As regard the gender prediction, the principal component regression model proved to be the most accurate, with 8 extracted components allowing a prediction of 93.50 % (86.00 % for women, 98.63 % for men). The predictive power of the model showed that in case of new subjects, gender classification could be done in 91.06 % of cases (84.00 % for women, 95.89 % for men). For age prediction, the optimal backward stepwise regression model with 6 statistically significant predictors showed an average error of 11.39 % (13.70 % for women, 9.81 % for men). The predictive power of the model was 12.13 % (11.73 % for women, 10.35 % for men). The age prediction was less accurate, with a maximum error of 10.00 % achieved only in the case of 65.04 % of subjects.