Artificial neural networks applied to the classification of hair samples according to pigment and sex using non-invasive analytical techniques

Full text
Author(s):	Berto, Tamires Messias ; Santos, Monica Cardoso ; Pereira, Fabiola Manhas Verbi ; Filletti, Erica Regina Total Authors: 4
Document type:	Journal article
Source:	X-RAY SPECTROMETRY; v. 49, n. 6, p. 10-pg., 2020-06-09.
Abstract
In this study, we investigated the possibility of using an artificial neural network (ANN) to classify human hair samples according to pigment (original or bleached hair) and sex (female or male) from numerical data obtained by wavelength dispersive X-ray fluorescence (WDXRF) and by laser-induced breakdown spectroscopy (LIBS). The results were promising, showing that the developed ANNs are able to classify the pigment and donor sex of hair samples with 100% and 89.5% accuracy, respectively, in the test set using WDXRF data. For the LIBS data in the test set, 100% of the pigment classifications were correct, and 78.9% of the donor sex classifications were correct. (AU)

FAPESP's process:	19/01102-8 - Multi-user equipament approved in grant 2018/18212-8: X-Ray Fluorescence spectrometer
Grantee:	Fabiola Manhas Verbi Pereira
Support Opportunities:	Multi-user Equipment Program


FAPESP's process:	18/18212-8 - Development of chemometric models for determination of solid impurities in raw sugarcane using energy dispersive X-ray fluorescence
Grantee:	Fabiola Manhas Verbi Pereira
Support Opportunities:	Regular Research Grants