Raman Spectroscopic Detection of Salivary Thiocyanate for Smoking Exposure Stratification

Abstract

Distinguishing smokers from non-smokers and stratifying the smokers based on their smoking history/exposure (years) is essential for evaluating the risk of tobaccoassociated diseases and supporting early preventive care. It enhances the reliability of epidemiological research by examining the 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 the 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 Raman spectroscopy-based methodology for the rapid identification of thiocyanate (SCN⁻), a salivary biomarker for assessing the tobacco smoking exposure. Based on the intensity values of a spectrally isolated peak at 2061 cm - 1 , the study could discriminate smokers (S) from nonsmokers (NS) and stratify the shortterm (<5 years) & long-term (> 5 years) smoke exposure. The study also demonstrated the classification of non-smokers & nonsmokers and stratification of smoking exposure using supervised machine learning with good accuracy. Owing to its minimal sample requirement, 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.