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@article{2417777, author = {Mitu, Bilkis and Trojan, Václav and Halámková, Lenka}, article_location = {UNITED STATES}, article_number = {23}, doi = {http://dx.doi.org/10.3390/s23239412}, keywords = {ATR FT-IR spectroscopy; machine learning; artificial neural network (ANN); partial least-square discriminant analysis (PLS-DA); male; female}, language = {eng}, issn = {2379-3694}, journal = {ACS SENSORS}, title = {Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context}, url = {https://www.mdpi.com/1424-8220/23/23/9412}, volume = {23}, year = {2023} }
TY - JOUR ID - 2417777 AU - Mitu, Bilkis - Trojan, Václav - Halámková, Lenka PY - 2023 TI - Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context JF - ACS SENSORS VL - 23 IS - 23 SP - Neuvedeno EP - Neuvedeno PB - AMER CHEMICAL SOC SN - 23793694 KW - ATR FT-IR spectroscopy KW - machine learning KW - artificial neural network (ANN) KW - partial least-square discriminant analysis (PLS-DA) KW - male KW - female UR - https://www.mdpi.com/1424-8220/23/23/9412 N2 - This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor's sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors' fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications. ER -
MITU, Bilkis, Václav TROJAN and Lenka HALÁMKOVÁ. Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context. \textit{ACS SENSORS}. UNITED STATES: AMER CHEMICAL SOC, 2023, vol.~23, No~23, p.~Neuvedeno, 15 pp. ISSN~2379-3694. Available from: https://dx.doi.org/10.3390/s23239412.
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