Quantification of cotton content in textiles by near-infrared spectroscopy

Importance of Topic

Accurate identification of cotton content in textile materials is essential for regulatory compliance, quality control, and consumer transparency. Traditional methods for determining fiber composition rely on chemical, mechanical, or microscopic techniques that are often laborious, time consuming, and destructive. Near-infrared spectroscopy (NIRS) presents a rapid, non-destructive, and reagent-free alternative, offering results within seconds and minimal sample preparation.

Objectives and Study Overview

This study aimed to develop and validate a quantitative NIR spectroscopy model for determining the percentage of cotton in textile blends. By evaluating a set of samples with known cotton-polyester ratios, the research sought to demonstrate the feasibility of routine, high-throughput analysis of fabric composition using a portable solid-state NIR analyzer.

Methodology and Instrumentation

Ten textile samples covering a wide range of cotton and polyester blends were prepared. Spectral acquisition was performed on a DS2500 Solid Analyzer equipped with a large sample cup and lid to ensure consistent sample presentation. The spectral range extended from 400 to 2500 nm. Data collection and model development were conducted using Vision Air Complete software, employing chemometric algorithms to correlate NIR spectra with reference cotton percentages.

Results and Discussion

The calibration dataset produced highly reproducible Vis-NIR spectra across all samples. A partial least squares regression model yielded an R² of 0.9975, indicating excellent linear correlation between predicted and reference values. The standard error of calibration (SEC) was 1.2 %, and the standard error of cross-validation (SECV) was 1.4 %, confirming the method’s robustness and predictive accuracy for routine analysis.

Benefits and Practical Applications

• Rapid analysis: results delivered in under 30 seconds per sample.
• Non-destructive testing: preserves sample integrity for further use.
• Chemical-free procedure: eliminates need for solvents or reagents.
• Portable platform: suitable for both laboratory and production environments.
• High accuracy: low prediction errors support reliable quality control.

Future Trends and Opportunities

Continued advancements in NIR sensors, data analytics, and machine learning will enhance prediction models and enable inline monitoring within textile manufacturing lines. Integration of cloud-based databases and automation can further streamline quality assurance workflows, while expanding spectral libraries to include additional fiber types will broaden application scope.

Conclusion

The application of NIR spectroscopy using the DS2500 Solid Analyzer demonstrates a fast, accurate, and non-destructive approach for quantifying cotton content in textile blends. With strong figures of merit and ease of use, this method represents a practical solution for routine fiber composition analysis in regulatory, research, and industrial settings.

Reference

• Metrohm Application Note AN-NIR-118: Quantification of cotton content in textiles by near-infrared spectroscopy