Determination of sodium dodecyl sulfate in toothpaste using Vis-NIR spectroscopy

Significance of the topic

Toothpaste must meet rigorous quality standards for additive content, coloration and active ingredients to guarantee consumer safety and product performance.
Traditional analytical techniques such as high-performance liquid chromatography are accurate but require extensive sample preparation, consumables and hazardous solvents.
Vis-NIR spectroscopy provides a non-destructive, reagent-free approach enabling rapid quantification of key components like sodium dodecyl sulfate with minimal running costs and high throughput.

Goals and study overview

This study aims to demonstrate the feasibility of using visible–near infrared spectroscopy for quantifying SDS content in toothpaste formulations.
Spectral data were acquired on 37 real samples with reference SLS concentrations, covering a range of product variations, to build and validate a chemometric model.

Methodology

Spectra were collected in transflection mode across 400–2500 nm using a NIRS XDS RapidContent Analyzer without any sample pre-treatment.
Chemometric analysis employed partial least squares regression with six latent variables.
Spectral pre-processing included a second derivative treatment and selection of two key wavelength intervals (1190–1298 nm and 1626–1804 nm) to focus on SDS-specific features.

Instrumentation used

• NIRS XDS RapidContent Analyzer (Metrohm 2.921.1110)
• Liquid Sample Transflection Kit (Metrohm 6.7400.010)
• Vision Air 2.0 Complete software (Metrohm 6.6072.208)

Main results and discussion

The calibration model achieved an R2 of 0.996, demonstrating excellent correlation between Vis-NIR predictions and reference values.
Standard error of calibration (SEC) was 0.021% and standard error of cross-validation (SECV) was 0.026% over an SDS concentration range of 0.5–1.8%.
The correlation plot shows tight clustering around the identity line, confirming high predictive accuracy and robustness of the method.

Benefits and practical applications

• Rapid analysis without reagents or solvent disposal.
• Reduced operational costs and improved laboratory safety.
• Potential for at-line or in-line monitoring during toothpaste production.
• Applicability to other quality parameters such as moisture, color, or alternative surfactants.

Future trends and potentials

Expansion to multi-analyte NIR methods for simultaneous monitoring of several quality markers.
Integration of portable Vis-NIR devices for real-time quality assurance in manufacturing lines.
Utilization of advanced machine learning algorithms to enhance model transferability and reduce calibration effort.

Conclusion

Vis-NIR spectroscopy combined with chemometric modeling provides an efficient, safe and cost-effective solution for quantifying SDS in toothpaste formulations.
The demonstrated method delivers high accuracy and repeatability, offering a viable alternative to traditional HPLC analyses and supporting rapid quality control workflows.