Conformational transitions of double-stranded DNA in different environments have long been studied as vital parts of both in vitro and in vivo processes. In this study, utilizing Fourier transform infrared spectroscopy (FTIR), we provide detailed analysis of dynamics of A- to B-form transitions in DNA thin films of different hydrated states based on a statistical analysis of a substantial number of spectra and band shape analysis (peak fitting) in both the phosphate (1150–1000 cm−1) and sugar–phosphate (900–750 cm−1) region. Hydration of DNA thin films is systematically controlled by the time spent in the desiccator chamber (from 3 min to 40 min) allowing conformation and hydration signatures, in addition to variations due to ambient conditions, to be resolved in the spectra. Conformation transition from A-form to more ordered B-form is observed if sufficient time in the desiccator chamber is allowed and is confirmed by changes on the bands at ≈890, 860, 837, and 805 cm−1. Phosphate vibrations at ≈1230 cm−1 and 1089 cm−1, and backbone vibrations at ≈1030 cm−1 and 765 cm−1 were found to be sensitive to changes in hydration rather than conformation. Additionally, we found that spectral variations caused by ambient conditions can be significantly reduced without inducing conformational changes, which serves as a good basis for quality assurance.

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

some machine learning algorithms for sex identification based on linear mandibular measurements derived from CT scans .