Quantification of active ingredients in shampoo using near-infrared spectroscopy (NIR)
Applications | 2017 | MetrohmInstrumentation
Shampoo products combine cleaning detergents and functional additives such as preservatives and anti-dandruff agents.
Accurate and rapid analysis of these ingredients is critical for quality control and batch release.
Traditional methods like HPLC require lengthy sample preparation, solvents and generate waste.
Near-infrared spectroscopy (NIRS) offers a fast, non-destructive, solvent-free alternative for simultaneous multi-constituent analysis.
This feasibility study aimed to evaluate NIRS for quantifying one preservative and two active ingredients in liquid shampoo.
A total of 48 samples with varying concentrations of components A, B and C were prepared.
The goal was to develop calibration models for each analyte and assess prediction errors.
The low SEC values and strong correlation plots demonstrate reliable quantification capability of NIRS for multiple analytes in shampoo with analysis times under one minute.
Near-infrared spectroscopy has been validated as a rapid and accurate tool for quantification of preservatives and active agents in shampoo.
This approach offers a cost-effective, eco-friendly alternative to conventional chromatographic methods for routine quality control.
No external literature references were provided in the original text.
NIR Spectroscopy
IndustriesOther
ManufacturerMetrohm
Summary
Significance of the Topic
Shampoo products combine cleaning detergents and functional additives such as preservatives and anti-dandruff agents.
Accurate and rapid analysis of these ingredients is critical for quality control and batch release.
Traditional methods like HPLC require lengthy sample preparation, solvents and generate waste.
Near-infrared spectroscopy (NIRS) offers a fast, non-destructive, solvent-free alternative for simultaneous multi-constituent analysis.
Objectives and Study Overview
This feasibility study aimed to evaluate NIRS for quantifying one preservative and two active ingredients in liquid shampoo.
A total of 48 samples with varying concentrations of components A, B and C were prepared.
The goal was to develop calibration models for each analyte and assess prediction errors.
Methodology and Used Instrumentation
- Instrumentation
- Metrohm NIRS XDS RapidLiquid Analyzer
- NIRS Quartz Cuvette Open Top 0.5 mm with spacer
- Vision Air 2.0 Complete software for data processing
- Data Acquisition
- Spectra collected in relevant NIR regions for each analyte
- Second derivative spectral pretreatment
- Partial Least Squares regression for calibration
Main Results and Discussion
- Preservative A
- PLS model with 4 factors
- Standard Error of Calibration (SEC): 0.111% across 0–4% range
- Active Ingredient B
- PLS model with 4 factors
- SEC: 0.147% over 0–4% concentration range
- Active Ingredient C
- PLS model with 2 factors
- SEC: 0.026% across 0–1% range
The low SEC values and strong correlation plots demonstrate reliable quantification capability of NIRS for multiple analytes in shampoo with analysis times under one minute.
Benefits and Practical Applications
- Non-destructive, solvent-free analysis reduces chemical usage and waste.
- Rapid measurement under one minute supports high-throughput quality control.
- Simultaneous multi-component detection enhances efficiency.
- Eliminates the need for costly chromatography columns and lengthy separations.
Future Trends and Potential Applications
- Extension to other personal care and cosmetic formulations.
- Integration with process analytical technology for real-time monitoring.
- Development of portable NIR systems for on-site quality checks.
- Advanced chemometric methods to further improve prediction accuracy.
Conclusion
Near-infrared spectroscopy has been validated as a rapid and accurate tool for quantification of preservatives and active agents in shampoo.
This approach offers a cost-effective, eco-friendly alternative to conventional chromatographic methods for routine quality control.
Reference
No external literature references were provided in the original text.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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