Detection of tobacco-related biomarkers in urine samples by surface-enhanced Raman spectroscopy coupled with thin-layer chromatography

Abstract

The nicotine metabolites, cotinine and trans-3'-hydroxycotinine (3HC) are considered as superior biomarkers for identifying tobacco exposure. More importantly, the ratio of 3HC to cotinine is a good indicator to phenotype individuals for cytochrome P450 2A6 activity and to individualize pharmacotherapy for tobacco addiction. In this paper, a simple, robust and novel method based on surface-enhanced Raman spectroscopy coupled with thin-layer chromatography (TLC) was developed to directly quantify the biomarkers in human urine samples. This is the first time surface-enhanced Raman spectroscopy (SERS) was used to detect cotinine and 3HC in urine samples. The linear dynamic range for the detection of cotinine is from 40 nM to 8 μM while that of 3HC is from 1 μM to 15 μM. The detection limits are 10 nM and 0.2 μM for cotinine and 3HC, respectively. The proposed method was further validated by quantifying the concentration of both cotinine and 3HC in smokers' urine samples. This TLC-SERS method allows the direct detection of cotinine in the urine samples of both active and passive smokers and the detection of 3HC in smokers.

Quantifying creatinine and urea in human urine through Raman spectroscopy aiming at diagnosis of kidney disease

“…The calibration of the spectrometer was checked before data collection. The positions (Raman shift) of the main bands of naphthalene 24 were verified, since the compound has characteristic intense and well-spaced bands in the fingerprint region 500 to 1700 cm −1 . The calibration of the spectral response was performed by the equipment supplier and was checked by measuring the spectrum of a tungsten filament lamp with a National Institute of Standards and Technology traceable spectrum.…”

“…Bispo et al 5 used dispersive RS (830 nm) to identify biomarkers (urea, creatinine, and glucose) in the midstream urine of diabetic and hypertensive patients by principal component analysis (PCA) and a discriminating model, suggesting the possibility of diagnosis of clinical complications associated with diabetes and hypertension (HT) through RS. Using the same type of analysis via PCA, RS was also effective in the detection of antibiotics in urine 23 and other metabolites such as biomarkers of nicotine and cotinine for identification of exposure to tobacco smoke (active or passive), 24 and uric acid as indicative of pre-eclampsia in pregnant women. 25 Therefore, it has been demonstrated that RS could be employed in the direct measurement of the concentration of urea and creatinine in human urine samples for biochemical assay.…”

Rapid on-site detection of ephedrine and its analogues used as adulterants in slimming dietary supplements by TLC-SERS

“…The combination of TLC and SERS would be propitious for on-site detection due to its simplicity, rapidity, and sensitivity. For example, it has been used for detection of tobacco-related biomarkers in urine samples [22], identification of alkaloids from the seed extract of Syian rue (Peganum harmala) [23], distinguishing from structural analogues [24], and monitoring of chemical reactions [25].…”

Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications

“…Although nicotine is the major component in tobacco, it is rarely used as a biomarker due to its short half‐life (1‐2 hours) and rapid absorption from the lungs to the bloodstream and finally to the brain. Nicotine metabolizes in the body to a number of compounds, the main metabolite is cotinine which has a longer half‐life and therefore is a preferred biomarker The Raman spectra of pure nicotine and cotinine obtained using near infrared laser excitation at 785 nm as well as their tentative peak assignments are shown in Figure S2 and Table S1. By comparing the spectra of nicotine, cotinine and oral fluid (Figure A), it can be seen that all of the Raman bands for cotinine and nicotine overlap with Raman bands of oral fluid, which made it difficult to differentiate between them (Figure S3).…”