Nanopore Identification of Nicotine in Cigarette Tobacco and Its Smoke

Abstract

Abstract Smoking poses a severe threat to global public health. Nicotine, as the predominant addictive constituent existing in both tobacco matrices and inhaled smoke, requires direct and reliable detection for health risk evaluation and tobacco quality control. In this work, we developed a single-molecule nicotine detection system using a nickel-functionalized Mycobacterium smegmatis porin A (MspA) nanopore. Structural analogues of nicotine, including cotinine, nicotinic acid, anatabine, and nornicotine, are also detectable with the same sensing system and can be readily distinguished from nicotine. With the assistance of machine learning, a cross-validation accuracy of 90.85% was achieved for the five analytes. Leveraging the metal coordination chemistry and machine learning algorithm, this system achieves specific identification of nicotine within complex matrices. With a calibrated standard curve, this method enables quantitative analysis of nicotine in tobacco samples. When combined with a smoke collection device, the system successfully detected nicotine in real cigarette smoke, demonstrating great potential for environmental smoke exposure evaluation and on-site field screening.