A novel method for human gender classification using Raman spectroscopy of fingernail clippings.

This paper illustrates a novel method for human gender classification by measuring the Raman spectrum of fingernail clippings. As Raman spectroscopy reveals the characteristics of vibrational frequencies of the fingernails, it provides unique chemical fingerprints that can be used to describe the molecular structure differences of fingernail between males and females. As the differences of Raman spectra of human fingernails are very subtle, they are enhanced by using a pattern recognition method. In the present study, a combination algorithm of principal component analysis (PCA) and support vector machines (SVM) was implemented to perform the data classification. This combined algorithm provides a classification accuracy of up to 90%. The success of this present method may be used as an alternative rapid tool to identify human gender in forensic applications.