Identification of fine wool and cashmere by Vis/NIR spectroscopy technology

Abstract

As a rapid and non-destructive methodology, near infrared spectroscopy technique has been paid much attention recently. This paper presents an automatic recognition scheme for the fine wool fiber and cashmere fiber by Vis/NIR spectroscopy technique, aim at the characteristics of Vis/NIR spectra on cashmere and fine wool. One mixed algorithm was presented to discriminate cashmere and fine wool with principal component analysis (PCA) and Artificial Neural Network (ANN). Preliminary qualitative analysis model has been built: We adopt Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere and fine wool, two kinds of data pretreatment methods were applied: the standard normal variate (SNV) was used as scatter correction. Savitzky-Golay with the segment size 3 was used as the smoothing way to decrease the noise processed. Followed the pretreatment, spectral data were processed using principal component analysis, 6 principal components (PCs) were selected based on the reliabilities of PCs of 99.8%, the scores of these 6 PCs would be taken as the input of the three-layer back-propagation (BP) artificial neural network (BP-ANN). Trained the BP-ANN with samples in calibration collection and predicted the samples in prediction collection. Experiments demonstrate that the system works quickly and effectively, and has remarkable advantages in comparison with the previous systems, The result indicted a model had been built to discriminate cashmere from fine wool using Vis/NIR spectra method combined with PCA-BP technology. The model works well, which indicates that this kind of approach is effective and promising, can raise resolution of cashmere and fine wool.