Study On Identification Of Cashmere And Wool Using Near Infrared Spectroscopy

In textile industry, it has always been a difficult problem to identify and test contents of cashmere and wool in textiles. Traditional identification relies on the projection microscope. This method needs very experienced inspectors. Moreover, it is time-consuming. As an easy, efficient and non-destructive way of quantitative analysis, near infrared (NIR) spectral can determine the content of each component in the blend of samples of several components simultaneously without the need of sample preparation. Therefore, it is suitable for the characterization and identification of textile materials, such as cashmere and wool.In this work, the uncolored fibers of cashmere and wool sourcing from different locations were studied via NIR spectroscopy technology. During this process, principal component analysis (PCA) was used to develop qualitative identification model for cashmere and wool; Different spectral pre-treatment and regression methods were then studied for the NIRS quantitative analysis of cashmere-wool fiber blends. The best methods were applied to build the quantitative analysis model and predict unknown samples; The NIRS was finally used to inspect the colored blend fabric of cashmere and wool and the influence of color of the fabrics on NIRS was investigated.It is indicated that the qualitative model can identify cashmere and wool accurately. Then, Savitzky-Golay smoothing pre-treatment and partial least squares (PLS) regression were selected and the best quantitative analysis model were developed. The model, which yielded correlation coefficient (R2) 0.9976 and absolute error 1.5, can predict the contents of uncolored cashmere- wool blends.In the end of the article, it is shown that color fading of tinged cashmere-wool blend fabric can not weaken the influence on NIRS. The best modeling wavelength range was selected to develop the blend fabric model, which yielded standard error of prediction (SEP) 3.56. Finally, an accurate prediction of wool content in the blend fabric was achieved.