Abstract

Compositional variations in the surfactants used to produce personal care products result in significant challenges during large scale manufacturing, for example errors in product viscosity. Characterisation of the surfactant can be completed using chromatographic techniques however these are time consuming and impractical during real-time manufacturing. Near infrared (NIR) absorption spectroscopy with a fibre-optic coupled transmission probe is proposed as an in-line method of determining the levels of unsulfated alcohol in sodium lauryl ether sulfate (SLES). NIR absorption spectra in the region of 4000 – 12000 cm⁻¹ were collected for a range of supplier samples at three temperatures. Gas chromatography - mass spectrometry was used as a reference technique to quantify samples of SLES and quantitative chemometric data analysis was used to produce partial least squares (PLS) calibration models for the prediction of surfactant composition. PLS regression was performed on the data in the spectral regions between 7509 – 5334 cm⁻¹ using a range of data pre-processing techniques to identify the best model. Models were evaluated using root mean square error of cross validation (RMSECV) and residual predictive deviation (RPD) as the primary indicator of model accuracy and robustness. A partial least squares regression model using a generalised least squares weighting data pre-processing approach was found to be the most robust in regards to sample non-homogeneity and temperature, producing a model with an RMSECV = 0.094 w/w% and RPD = 4.03. The model successfully predicted the unsulfated alcohol mass percentage in an external validation of unknown samples with alcohol levels within the model limits of 0.7–2.2 w/w%. Spectra acquired at a resolution of 8 cm⁻¹ with 32 scans take just 16 seconds to obtain, proving that NIR spectroscopy can successfully be applied as an alternative analytical method to gas chromatography for the determination of low level impurities in viscous surfactant systems.

© 2020 The Author(s)

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