Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context

MITU, Bilkis, Václav TROJAN a Lenka HALÁMKOVÁ. Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context. ACS SENSORS. UNITED STATES: AMER CHEMICAL SOC, 2023, roč. 23, č. 23, s. Neuvedeno, 15 s. ISSN 2379-3694. Dostupné z: https://dx.doi.org/10.3390/s23239412.

Základní údaje

• Originální název: Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context
• Autoři: MITU, Bilkis (840 Spojené státy), Václav TROJAN (203 Česká republika, domácí) a Lenka HALÁMKOVÁ (203 Česká republika).
• Vydání: ACS SENSORS, UNITED STATES, AMER CHEMICAL SOC, 2023, 2379-3694.

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Článek v odborném periodiku
• Obor: 10400 1.4 Chemical sciences
• Stát vydavatele: Velká Británie a Severní Irsko
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL
• Impakt faktor: Impact factor: 8.900 v roce 2022
• Organizační jednotka: Farmaceutická fakulta
• Doi: http://dx.doi.org/10.3390/s23239412
• UT WoS: 001116755500001
• Klíčová slova anglicky: ATR FT-IR spectroscopy; machine learning; artificial neural network (ANN); partial least-square discriminant analysis (PLS-DA); male; female
• Štítky: rivok, ÚPL
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.