Natural deep eutectic solvents (NADES) are ionic solutions, of great interest for extraction from biomass, biocatalysis, and nanoparticle synthesis. They are easily synthesised and eco-friendly, have low volatility and high dissolution power, and are biodegradable. However, water content in NADES is a critical parameter, affecting their optimal use and extraction efficiency. Vibrational spectroscopic techniques are rapid, label-free, non-destructive, non-invasive, and cost-effective analytical tools that can probe the molecular composition of samples. A direct comparison between a previous study using attenuated total reflectance infrared (ATR-IR) spectroscopy for water quantification in NADES and the same investigation performed with Raman spectroscopy is presently reported. Three NADES systems, namely betaine-glycerol (BG), choline chloride-glycerol (CCG), and glucose-glycerol (GG), containing a range of water concentrations between 0% (w/w) and 40% (w/w), have been analysed with Raman spectroscopy coupled to partial least squares regression multivariate analysis. The values of root mean square error of cross-validation (RMSECV) obtained from analysis performed on the pre-processed spectra over the full spectral range (150-3750 cm-1) are respectively 0.2966% (w/w), 0.4703% (w/w), and 0.2351% (w/w) for BG, GG, and CCG. While the direct comparison to previous ATR-IR results shows essentially similar outcomes for BG, the RMSECV is 33.14% lower and 65.84% lower for CG and CCG. Furthermore, mean relative errors obtained with Raman spectroscopy, and calculated from a set of samples used as independent samples, were 1.452% (w/w), 1.175% (w/w), and 1.188% (w/w). Ultimately, Raman spectroscopy delivered performances for quantification of water in NADES with similar accuracy to ATR-IR. The present demonstration clearly highlights the potential of Raman spectroscopy to support the development of new analytical protocols in the field of green chemistry.
Keywords: Label-free water quantification; Natural deep eutectic solvent; Partial least squares regression; Raman spectroscopy.
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