Application of Machine Learning Methods to Raman Spectroscopy Technique in Dentistry

Abstract

Raman spectroscopy is nowadays regarded as a practical optical method and non-destructive photonic tool, which can be applied in several biomedical fields for analyzing the molecular composition. This technique is considered appropriate for human dental tissues characterization, from caries detection to evaluation of demineralization caused by acidic external agents. Discrimination techniques (linear regression), and classification techniques (neural networks) are often used for spectroscopic data analysis in disease detection and identification. Usually, Raman raw spectra obtained from teeth are processed using baseline correction, smoothing, normalized for noise, fluorescence, shot noise removal, and subsequently analysed using principal component analysis, to reduce the variable dimensionality. Raman chemical images can be constructed with another simple and uncomplicated unsupervised machine learning method – represented by k-means clustering, enabling the identification of similar areas/features, for classifying different acquired spectral data. The Machine Learning methods choice depends always on type and amount of information provided by Raman spectra. In this paper, was applied Principal Component Analysis methods to the analysis and interpretation of several parameters extracted from Raman spectra acquired before and after a simulated acid challenge of human enamel. These parameters and their correlation allow to assess the protective effect of a fluoride-based dental varnish.