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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