Determination of electronic symmetries from polarized Raman spectra

The goal of this work was to evaluate the ability of Raman spectroscopy to identify molecular organization and chemical composition of extracellular matrix such as the collagen fibers arrangement, the level of mineralization, and the carbonate accumulation in mineral phase in spongy bone of the human head of the femur. Changes in composition and structure of the spongy bone tissue were illustrated using maps of polarized Raman spectra. In particular, the purpose of the present study was determination of arrangement of mineralized collagen on surface of trabecula by using transformations of Raman spectra maps. Transformations of Raman spectra maps were needed in order to remove impact of chemical composition on images of Raman spectra map, which display the collagen fibers orientation. These transformations allow to obtain simultaneously the distribution of constituents of bone and arrangement of collagen fibers on tissue surface. A method to indicate the collagen orientations is developed to understand the molecular organization in healthy and unhealthy bone at the microstructural level.

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Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽

10.3390/molecules26133983 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3983

Author(s):

Ozren Gamulin ◽

Marko Škrabić ◽

Kristina Serec ◽

Matej Par ◽

Marija Baković ◽

...

Keyword(s):

Raman Spectra ◽

Principal Component ◽

Support Vector ◽

Gender Recognition ◽

Proof Of Concept ◽

Male And Female ◽

Tooth Type ◽

Tooth Apex ◽

The Difference

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.

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