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Objectives Investigate the biochemistry of in vivo healthy oral tissues through Raman spectroscopy. We aimed to characterize the biochemical features of healthy condition in oral subsites (buccal mucosa, lip, tongue, and gingiva) of healthy subjects. More specifically, we investigated Raman spectral characteristics and biochemical content of in vivo healthy tissues on Brazilian population. This characterization can be used to better define normal tissue and improve the detection of oral premalignant conditions in future studies. Materials and methods For spectroscopic analysis a Raman spectrometer (Kaiser Optical Systems imaging spectrograph Holospec, f / 1.8i-NIR) coupled with a laser 785 nm, 60 mW was used. Raman measurements were obtained by means of an optical fiber (EMVision fiber optic probe) coupled between the laser and the spectrometer. Three spectra per site were acquired from the lip, buccal mucosa, tongue, and gingiva of ten healthy volunteers. This resulted in 30 spectra per oral sub-site and in total 120 spectra.Results We report detailed biochemical information on these subsites and their relative composition based on deconvolution studies of their spectra. Finally, we also report classification efficiency of 61, 83, 41, and 93% for buccal, gingiva, lip, and tongue respectively after applying multivariate statistical tools. Conclusions We quantitated the contribution of various biochemicals in terms of percentage, and this will enable comparison not only across anatomical sites but also across studies. Raman spectroscopy can rapidly probe tissue biochemistry of healthy oral regions. Moreover, the study suggests the possibility of using Raman spectroscopy combined with signal processing and multivariate analysis methods to differentiate the oral sites in healthy conditions and compare with pathological conditions in future studies. Clinical relevance The spectral characterization of the healthy condition of oral tissues by a noninvasive, label-free, and real-time analytical techniques is important to create a spectral reference for future diagnosis of pathological conditions.

Most oral injuries are diagnosed by histopathological analysis of a biopsy, which is an invasive procedure and does not give immediate results. On the other hand, Raman spectroscopy is a real time and minimally invasive analytical tool with potential for the diagnosis of diseases. The potential for diagnostics can be improved by data post-processing. Hence, this study aims to evaluate the performance of preprocessing steps and multivariate analysis methods for the classification of normal tissues and pathological oral lesion spectra. A total of 80 spectra acquired from normal and abnormal tissues using optical fiber Raman-based spectroscopy (OFRS) were subjected to PCA preprocessing in the z-scored data set, and the KNN (K-nearest neighbors), J48 (unpruned C4.5 decision tree), RBF (radial basis function), RF (random forest), and MLP (multilayer perceptron) classifiers at WEKA software (Waikato environment for knowledge analysis), after area normalization or maximum intensity normalization. Our results suggest the best classification was achieved by using maximum intensity normalization followed by MLP. Based on these results, software for automated analysis can be generated and validated using larger data sets. This would aid quick comprehension of spectroscopic data and easy diagnosis by medical practitioners in clinical settings.

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The role that tobacco consumption plays in the etiology of oral cancer carcinogenesis, and of alcohol consumption acting as a co-factor, have been well established. However, in recent years, the contribution of alcohol consumption alone to oral cancer has been proposed. In fact, a high percentage of patients who develop oral cancer have both habits (tobacco and alcohol consumption), and other small patient groups only consume alcohol or do not have any other identifiable bad habits. In the present study, we demonstrate for the first time, using a combination of dynamic molecular modelling and Raman spectroscopy, that ethanol has a significant effect on oral cells in vitro, mainly interacting with the lipids of the cell membrane, changing their conformation. Thus, it is possible to conclude that ethanol can affect the cell permeability, and by consequence serve as a possible trigger in oral carcinogenesis.