Rapid Quantification of Sodium Chloride in Aqueous Solutions Using Raman Spectroscopy and Partial Least Squares Regression

The rapid and non-destructive determination of sodium chloride (NaCl) in aqueous solutions is critical to food-process monitoring, pharmaceutical manufacturing, and the chlor-alkali and desalination industries. This study presents a method based on Raman spectroscopy (785 nm; 350–2000 cm−1) combined with partial least squares regression (PLS-R) for estimating NaCl in simple aqueous solutions at concentrations between 0.02 and 4.0 mol·L−1. Since Na+ and Cl− ions lack intrinsic Raman modes, the quantification was based on the modifications that ionic solvation induces on the hydrogen-bond network of water, mainly affecting the librational (450–780 cm−1) and H-O-H bending modes (~1640 cm−1). Four preprocessing strategies were systematically evaluated. The model with the combination of asymmetric least squares baseline correction and Savitzky–Golay smoothing (ALS-SG) provided the lowest root mean square error of cross-validation (RMSECV = 0.0608 mol·L−1), requiring only two latent variables (calibration coefficient of determination, R2cal = 0.9990 and cross-validated coefficient of determination, Q2CV = 0.9979). The holdout prediction set (n = 5) yielded a coefficient of determination (R2ext) of 0.9946 and a root mean square error of prediction (RMSEP) of 0.0681 mol·L−1, with an empirical lower limit of reliable prediction of ~0.18 mol·L−1. The holdout samples span 0.04–2.50 mol·L−1, so predictive performance between 2.50 and 4.00 mol·L−1 is supported by calibration and cross-validation only. Given the small size of this set, these figures are reported as a proof-of-concept indication and support the potential of the approach for NaCl monitoring in simple aqueous matrices.