Raman spectroscopic detection of salivary thiocyanate for smoking exposure stratification

Abstract

Distinguishing smokers from non-smokers and stratifying smokers based on their smoking history/exposure (years) are essential for evaluating the risk of tobacco-associated diseases and supporting early preventive care. It enhances the reliability of epidemiological research by examining tobacco exposure within populations. In forensic science and toxicology, detection of smoking biomarkers aids in lifestyle assessment and confirmation of tobacco exposure. Most importantly, regularly tracking SCN⁻ levels in saliva helps in assessing smoking cessation programs and enables personalized treatment based on smoking habits. Therefore, the current study investigates the use of a Raman spectroscopy-based methodology for the rapid identification of thiocyanate (SCN⁻), a salivary biomarker for assessing tobacco smoking exposure. Based on the intensity values of a spectrally isolated peak at 2061 cm⁻¹, this study could discriminate smokers (S) from non-smokers (NS) and stratify short-term (<5 years) and long-term (>5 years) smoking exposure. This study also demonstrated the classification of non-smokers and smokers and stratification of smoking exposure using supervised machine learning with good accuracy. Owing to its minimal sample requirement, easy preparation and rapid analysis time, the proposed method offers a practical approach for screening SCN⁻ and distinguishing smokers from non-smokers in large populations, enabling its potential application in diverse clinical, epidemiological, forensic and public health settings.