Abstract
Use of e-cigarettes is increasing, alongside an expanding variety of devices and e-liquids. To match this growth and in line with the expanding legal and regulatory requirements applicable to manufacturers of e-cigarettes (e.g. disclosure of list of ingredients and quantities thereof in a product), rapid methods for determining levels of the main e-liquid constituents—namely, propylene glycol (PG), vegetable glycerol (VG), water and nicotine—are needed. We have assessed the ability of near infrared (NIR) spectroscopy, coupled with partial least squares (PLS) regression, to predict the levels of these constituents in e-liquid formulations. Using NIR spectral data from a large set of reference e-liquids incorporating working concentration ranges, flavourings, and other ingredients, linear calibration models were established for PG, VG, water and nicotine (predicted vs theoretical values, all R2 > 0.995). The performance of these models was then evaluated on commercial e-liquids using NIR and compared to results obtained by gas chromatography (GC). A strong correlation was observed between NIR-predicted values and measured values for PG, VG and nicotine (all R2 > 0.955). There was less consistency between predicted and GC measured values for water due to the relatively high limit of quantification (LOQ) of the GC method (2.6% w/w) versus the e-liquid content (0–18% w/w). The LOQ of the NIR method for water was 0.6% w/w, suggesting that NIR may be a more accurate method than GC to predict water concentration in e-liquids, especially at low levels (< 2.6% w/w). Collectively, although limitations of the technique have been identified, specifically for e-liquids containing compounds that might interfere with the set calibrations, our findings suggest that NIR combined with PLS regression is a suitable tool for rapid, simultaneous and high-throughput measurement of PG, VG, water and nicotine levels in most commercial e-liquids.
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