Periodontitis detection using Raman spectroscopy, support vector machine, and salivary biomarkers

Many research areas have developed techniques to diagnose lung cancer, cardiovascular diseases, stress, caries, and periodontitis by analyzing saliva. This paper describes a study that predicts periodontitis based on Raman spectra of saliva and biomarkers, such as albumin and alanine aminotransferase (ALT). The spectra were smoothed using a Whittaker filter and baseline correction in MATLAB. In addition, a residual analysis of intensities was performed, and the root mean square deviation was calculated and used as a threshold to establish the active bands of interest, based on the Raman bands associated with albumin and ALT. ORCA quantum chemistry software was used to predict the fundamental frequencies and intensities of some saliva constituents. Support vector machines were used to perform spectral distinction and discriminate between healthy and periodontitis patients.

Automated summary

This study predicts periodontitis by analyzing Raman spectra and biomarkers in saliva, such as albumin and alanine aminotransferase (ALT). The study uses MATLAB for data processing and analysis, ORCA software to predict fundamental frequencies and intensities, and support vector machines for spectral distinction.