Characterizing variability in in vivo Raman spectroscopic properties of different anatomical sites of normal tissue in the oral cavity

Abstract

Raman spectroscopy is an inelastic light scattering technique that is capable of probing biochemical and biomolecular structures and conformations of tissue. This study aims to characterize the in vivo Raman spectroscopic properties of different normal oral tissues in the fingerprint region (800-1800 cm -1) and to assess distinctive biochemical variations of different anatomical regions in the oral cavity. A specially designed fiber-optic Raman probe with a ball lens was utilized for real-time, in vivo Raman measurements of various oral tissue sites (i.e. inner lip, attached gingiva, floor, dorsal tongue, ventral tongue, hard palate, soft palate, and buccal). The semiquantitative non-negativity-constrained least squares minimization fitting of reference biochemicals representing oral tissue constituents (i.e. hydroxyapatite, keratin, collagen, DNA, and oleic acid) and partial least squares-discriminant analysis (PLS-DA) were employed to assess the significance of inter-anatomical variability. A total of 402 high-quality in vivo oral Raman spectra were acquired from 20 subjects. The histological characteristics of different oral tissues were found to have influence on the in vivo Raman spectra and could be grossly divided into three major clusterings: (1) buccal, inner lip, and soft palate; (2) dorsal, ventral tongue, and floor; (3) gingiva and hard palate. The PLS-DA multiclass algorithms were able to identify different tissue sites with varying accuracies (inner lip 83.1%, attached gingiva 91.3%, floor 86.1%, dorsal tongue 88.8%, ventral tongue 83.1%, hard palate 87.6%, soft palate 83.3%, and buccal mucosa 85.3%), bringing out the similarities among different oral tissues at the biomolecular level. This study discloses that inter-anatomical variability is significant and should be considered as an important parameter in the interpretation and rendering of Raman diagnostic algorithms for oral tissue diagnosis and characterization. Copyright © 2011 John Wiley & Sons, Ltd.