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Raman spectroscopy is a label-free, non-destructive, non-invasive analytical tool that provides insight into the molecular composition of samples with minimum or no sample preparation. The increased availability of commercial portable Raman devices presents a potentially easy and convenient analytical solution for day-to-day analysis in laboratories and production lines. However, their performance for highly specific and sensitive analysis applications has not been extensively evaluated. This study performs a direct comparison of such a commercially available, portable Raman system, with a research grade Raman microscope system for the analysis of water content of Natural Deep Eutectic Solvents (NADES). NADES are renewable, biodegradable and easily tunable “green” solvents, outcompeting existing organic solvents for applications in extraction from biomass, biocatalysis, and nanoparticle synthesis. Water content in NADES is, however, a critical parameter, affecting their properties, optimal use and extraction efficiency. In the present study, portable Raman spectroscopy coupled with Partial Least Squares Regression (PLSR) is investigated for rapid determination of water content in NADES samples in situ, i.e., directly in glassware. 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 28.5% (w/w), were studied. The results are directly compared with previously published studies of the same systems, using a research grade Raman microscope. PLSR results demonstrate the reliability of the analysis, surrendering R2 values above 0.99. Root Mean Square Errors Prediction (RMSEP) of 0.6805%, 0.9859% and 1.2907% w/w were found for respectively unknown CCG, BG and GG samples using the portable device compared to 0.4715%, 0.3437% and 0.7409% w/w previously obtained by analysis in quartz cuvettes with a Raman confocal microscope. Despite the relatively higher values of RMSEP observed, the comparison of the percentage of relative errors in the predicted concentration highlights that, overall, the portable device delivers accuracy below 5%. Ultimately, it has been demonstrated that portable Raman spectroscopy enables accurate quantification of water in NADES directly through glass vials without the requirement for sample withdrawal. Such compact instruments provide solvent and consumable free analysis for rapid analysis directly in laboratories and for non-expert users. Portable Raman is a promising approach for high throughput monitoring of water content in NADES that can support the development of new analytical protocols in the field of green chemistry in research and development laboratories but also in the industry as a routine quality control tool.

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Vibrational spectroscopic techniques, i.e., attenuated total reflectance infrared (ATR-IR), near infrared spectroscopy (NIRS) and Raman spectroscopy (RS), coupled with Partial Least Squares Regression (PLSR), were evaluated as cost-effective label-free and reagent-free tools to monitor water content in Levulinic Acid/L-Proline (LALP) (2:1, mol/mol) Natural Deep Eutectic Solvent (NADES). ATR-IR delivered the best outcome of Root Mean Squared Error (RMSE) of Cross-Validation (CV) = 0.27% added water concentration, RMSE of Prediction (P) = 0.27% added water concentration and mean % relative error = 2.59%. Two NIRS instruments (benchtop and handheld) were also compared during the study, respectively yielding RMSECV = 0.35% added water concentration, RMSEP = 0.56% added water concentration and mean % relative error = 5.13% added water concentration, and RMECV = 0.36% added water concentration, RMSEP = 0.68% added water concentration and mean % relative error = 6.23%. RS analysis performed in quartz cuvettes enabled accurate water quantification with RMECV = 0.43% added water concentration, RMSEP = 0.67% added water concentration and mean % relative error = 6.75%. While the vibrational spectroscopic techniques studied have shown high performance in relation to reliable determination of water concentration, their accuracy is most likely related to their sensitivity to detect the LALP compounds in the NADES. For instance, whereas ATR-IR spectra display strong features from water, Levulinic Acid and L-Proline that contribute to the PLSR predictive models constructed, NIRS and RS spectra are respectively dominated by either water or LALP compounds, representing partial molecular information and moderate accuracy compared to ATR-IR. However, while ATR-IR instruments are common in chemistry and physics laboratories, making the technique readily transferable to water quantification in NADES, Raman spectroscopy offers promising potential for future development for in situ, sample withdrawal-free analysis for high throughput and online monitoring.

Attenuated Total Reflectance–infrared spectroscopy (ATR-IR) coupled to Partial Least Squares Regression (PLSR), was evaluated as a rapid, label free and cost-effective tool to quantify water content in extracts obtained from...

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Raman spectroscopy is a well-established technique for the molecular characterisation of samples and does not require extensive pre-analytical processing for complex cosmetic products. As an illustration of its potential, this study investigates the quantitative performance of Raman spectroscopy coupled with partial least squares regression (PLSR) for the analysis of Alginate nanoencapsulated Piperonyl Esters (ANC-PE) incorporated into a hydrogel. A total of 96 ANC-PE samples covering a 0.4% w/w–8.3% w/w PE concentration range have been prepared and analysed. Despite the complex formulation of the sample, the spectral features of the PE can be detected and used to quantify the concentrations. Using a leave-K-out cross-validation approach, samples were divided into a training set (n = 64) and a test set, samples that were previously unknown to the PLSR model (n = 32). The root mean square error of cross-validation (RMSECV) and prediction (RMSEP) was evaluated to be 0.142% (w/w PE) and 0.148% (w/w PE), respectively. The accuracy of the prediction model was further evaluated by the percent relative error calculated from the predicted concentration compared to the true value, yielding values of 3.58% for the training set and 3.67% for the test set. The outcome of the analysis demonstrated the analytical power of Raman to obtain label-free, non-destructive quantification of the active cosmetic ingredient, presently PE, in complex formulations, holding promise for future analytical quality control (AQC) applications in the cosmetics industry with rapid and consumable-free analysis.