NIR Characterization of Waste Polyester/Viscose Blended Fabrics with Different Composition Ratios
2022年11月11日
Abstract: To find a more suitable method for waste textiles sorting, 467 polyester/viscose blend fabrics samples with different contents were tested via an online NIR spectrometer and a handheld NIR spectrometer, respectively. Among them, 184 samples were used as a training set for modeling and the remaining 283 samples were used for model validation. The spectra collected by the online NIR spectrometer were subjected to firstorder derivatives with smoothing and trend correction processes; the fiber content of waste fabrics is classified and identified by principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA).The final model had an external validation accuracy of 100% and an identification rate of 98.37% for unknown samples. The spectra collected by the handheld NIR spectrometer were analyzed by PCA and ordinary least squares (OLS) to build segmental regression models of the components for unmeasured fabrics. And then,importing the model into the handheld spectrometer can generate 97.88% of the recognition rate of the waste fabrics. The qualitative accuracy of NIR spectra for the waste polyester/viscose blended textiles samples decreased as the content of viscose fiber increased, indicating that the viscose fiber content was inversely related to the qualitative accuracy of the samples. The maximum error of the average value of polyester fiber matching degree and the content of each component are all below 4.5%, which provides a reference for the quantitative analysis in the next stage.
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