Possibility of Human Gender Recognition Using Raman Spectra of Teeth.

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.

Principal Component Regression for Forensic Age Determination Using the Raman Spectra of Teeth.

Raman spectra of mineralized tooth tissues were used to build a principal component regression (PCR) age determination model for forensic application. A sample of 71 teeth was obtained from donors aging from 11 to 76 years. No particular selection criteria were applied; teeth affected with various pathological processes were deliberately included to simulate a realistic forensic scenario. In order to comply with the nondestructive specimen handling, Raman spectra were collected from tooth surfaces without any previous preparation. Different tooth tissues were evaluated by collecting the spectra from three distinct sites: tooth crown, tooth neck, and root apex. Whole recorded spectra (3500-200 cm-1) were used for principal component analysis and building of the age determination model using PCR. The predictive capabilities of the obtained age determination models varied according to the spectra collection site. Optimal age determination was attained by using Raman spectra collected from cementum at root apex (R2 values of 0.84 and 0.71 for male and female donors, respectively). For optimal performance of that model, male and female donors had to be analyzed separately, as merging both genders into a single model considerably diminished its predictive capability (R2 = 0.29).

Development of pre-breeding technology for root system study and selection of Kihara Afghan wheat landraces (KAWLR) to enhance wheat breeding in the rain-fed region.

To enhance a root trait-based selection program for rain-fed wheat breeding in Afghanistan, we simulated an efficient pre-breeding drought system. Plants were grown in 1 m pipes as control or 2 m pipes to simulate drought conditions soaking ground water up by capillary action supplemented by two different life supporting irrigations from top of the pipes (T1 and T2 droughts). T1 was used for studying genetic diversity in 360 Kihara Afghan wheat landraces (KAWLR). Both drought treatments were used to evaluate root traits in 30 selected genotypes. KAWLR showed large root length variations under T1, categorized as long root (>200 cm; LR), medium root (100-150 cm; MR) and short root (20-100 cm; SR) systems. LR genotypes were more drought resistant in terms of greater plant survivability under T1 and T2 compared with other groups and were capable of adjusting their root biomass partitioning at deepest part of the soil profile. Majority of the LR genotypes originated from predominantly rain-fed provinces, and most of their agronomic traits were strongly correlated with root biomass deep in the soil in response to drought. Three LR genotypes, including the longest root genotype LR-871 (KU7604), are recommended for rain-fed wheat breeding in Afghanistan.