Development and validation of an at-line fast and non-destructive Raman spectroscopic method for the quantification of multiple components in liquid detergent compositions

Abstract

Implementation of process analytical technology (PAT) tools in the manufacturing process of liquid detergent compositions should allow fast and non-destructive evaluation of the product quality. The aim of this study was to develop and validate a rapid method for quantifying the chemical compounds of five washing liquid precursors. Raman spectroscopy was applied in combination with a two-step multivariate modeling procedure. In first instance, a SIMCA (Soft Independent Modeling of Class Analogy) model was developed and validated, allowing the distinction between the different laundry detergents. Once the product was correctly identified, it was aimed at predicting the concentration of its individual components using partial least squares (PLS) models. Raman spectra were collected at-line with a total acquisition time of 20 seconds, using a non-contact fiber-optic probe. 3The SIMCA model was perfectly capable of differentiating between the classes of the laundry liquid precursors. Per detergent, the concentration of at least three main ingredients could be predicted with a recovery between 98 % and 102 % and a standard deviation below 2.5 %. Accuracy profiles based on the analysis results of validation samples were then calculated to prove the reliability of the developed regression models. β-expectation tolerance intervals were calculated for each model and for each validated concentration level. The acceptance limits were set at 5 % relative bias, indicating that at least 95 % of future measurements should not deviate more than 5 % from the true value. Furthermore, based on the data of the accuracy profiles, the measurement uncertainty was determined.The developed Raman spectroscopic method demonstrated to be able to rapidly and adequately determine the concentration of the components of interest in the liquid detergent compositions at-line. Keywords

The development and validation of a quality by design based process analytical tool for the inline quantification of Ramipril during hot-melt extrusion

“…Thus, a large T2 range for a given sample is an indication that it is far from other samples in the score space of the model. Hence, it is likely to be an outlying observation that may detrimentally distort the calibration model and, subsequently, give false results for unknown samples (Brouckaert et al, 2016). Values larger than the 95% confidence limit are suspect, while values larger than the 99% confidence limit can be considered as serious outliers and should be excluded from the calibration model.…”

Raman Spectroscopy for Quantitative Analysis in the Pharmaceutical Industry

“…The method most commonly used in the multivariate analysis is the partial least squares (PLS) method (10,38,39), described by Wold in 1966 (40). Furthermore, for multicomponent matrices, classical least squares (CLS), alternating least squares (MCR), principal component regression (CRP), and principal component analysis (PCA) are commonly used (41).…”