Use of inline near-infrared spectroscopy to predict the viscosity of shampoo using multivariate analysis.

K Haroon,Ć Mendoza,T Rodgers,P Martin,A Arafeh,M Baker

doi:10.1111/ics.12536

Abstract

ObjectiveIn the personal care industry, viscosity is a critical quality attribute that influences product quality and process economics. Like many industrial liquids, personal care liquids are complex non‐Newtonian liquids made up of aqueous surfactant systems whose viscosity depends on the build‐up of micellar networks. Measuring the viscosity of complex liquids offline is easily done using benchtop rheometers and viscometers. The challenge lies in measuring the viscosity of personal care liquids online during manufacturing. Being able to track the viscosity of such products through their manufacturing cycle will not only allow for better process control but also more enhanced quality control. Therefore, the aim of this work was to investigate how proxy measurements using inline near‐infrared (NIR) spectroscopy in transmission mode can be used to predict the viscosity of shampoo. NIR spectroscopy has not, to the best our knowledge, been used to predict the viscosity of complex surfactant systems like shampoo and could significantly affect the way quality is monitored in a manufacturing environment.MethodThis work focuses on viscosity changes because of differences in chloride content as salt is often used to adjust viscosity. The relationship between salt content and the viscosity of shampoo is well known following the salt curve. From an industrial perspective the region of interest for the formulation studied in this work only covers a small section of this curve. Therefore, two predictive models were developed: one covering the full range of the salt curve and another focusing on the industrially applicable region.ResultModels were produced using partial least squares (PLS) where both datasets showed some predictive ability with the concentrated region of interest showing enhanced performance [root mean square error of prediction (RMSEP) – 2.32 Pa s] compared with the larger range (RMSEP – 4.44 Pa s).ConclusionThis work provides a good starting point for developing robust predictive models for in situ viscosity measurements for shampoo manufacturing, where further work into different sources of variation and the extent of the modelling capability with regards to different formulations should be studied.

Highlights

Characterizing the rheological properties of materials is of great importance in many industries in terms of product quality and process economics
As NIR is prone to overlapping peaks, the CH vibrational overtones comprise of CH, CH2 and CH3 bond absorptions, with the CH2 having the largest presence because of the long alkyl chains present in sodium lauryl ether sulphate
The OH overtones are representative of water molecules as water accounts for at least 65% of each sample

Summary

Introduction

Characterizing the rheological properties of materials is of great importance in many industries in terms of product quality and process economics. Measuring the viscosity of products like shampoos and conditioners offline is well established using benchtop rheometers and viscometers [1,2,3,4]. Cambridge Piston viscometers that base their viscosity measurements on the speed that an oscillating piston moves through the liquid can be found in both laboratory and manufacturing environments. These instruments have been found to have issues with robustness, cleaning and have poor data acquisition rates [4,5]

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Results

Conclusion