Rapid determination of benzalkonium chloride in aqueous samples by FTIR spectroscopy in tandem with chemometrics

Abstract

In this research, a powerful regression model coupled with FTIR spectroscopy (Mid, 800–1700 cm−1) has been proposed as an efficient method for precise determination of Benzalkonium chloride (BAK) in aqueous samples. For this purpose, both partial least squares regression (PLS-R) and support vector regression (SVR) as methods of multivariate calibration were used for evaluation, and their results were compared. Accordingly, root mean square error of prediction, and leave-one-out cross-validation root mean square error, and correlation coefficients between the calculated (Rcal2) and the predicted (Rpred2) values were used. In comparison to PLS (Rpred2 = 0.975; RMSEP = 0.321), SVR had a higherRpred2 (0.991) and a lower value of root mean square error of prediction (RMSEP = 0.218). The lower detection limit was 0.00068% w/w for PLS and 0.0011% w/w for SVR model in a concentration range from 0.013 to 1% w/w. Hence, FTIR spectroscopy combined with SVR can be considered an efficient approach for real-time determination of BAK in aqueous samples.