| 光谱学与光谱分析 |
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| Identification of Fine Wool and Cashmere by Using Vis/NIR Spectroscopy Technology |
| WU Gui-fang1,2,ZHU Deng-sheng3,HE Yong1* |
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China
2. College of Mechanical and Electrical Engineering, Inner Mongolia Agriculture University, Huhhot 010018, China
3. Jinhua College of Profession and Technology, Jinhua 321017, China |
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Abstract As a rapid and non-destructive methodology, near infrared spectroscopy technique has been attracting much attention recently. The present study applied Vis/NIR spectra to the identification of cashmere and fine wool fiber. Cashmere and fine wool are resemble in superficies, but they differs in diameter, height, thickness, angle of inclination, and marginal morphology of surface scale. Although researchers both at home and abroad did a lot researches and experiments to distinguish fine wool from cashmere, the resolution of cashmere and fine wool is still not satisfactory, and it is always a challenging task to differentiate and recognize fine wool and cashmere. This paper presents an automatic recognition scheme for the fine wool fiber and cashmere fiber by Vis/NIR spectroscopy technique, aiming at the characteristics of Vis/NIR spectra of 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: Vis/NIRS spectroscopy diffuse techniques were used to collect the spectral data of cashmere and fine wool, and two kinds of data pretreatment methods were applied: the standard normal variate (SNV) was used for scatter correction. Savitzky-Golay with the segment size 3 was used as the smoothing way to decrease the noise processed. Following 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%, and the scores of these 6 PCs would be taken as the input of the three-layer back-propagation (BP) artificial neural network (BP-ANN). The BP-ANN was trained with samples in calibration collection and predicted the samples in prediction collection were predicted. Experiments demonstrate that the system works quickly and effectively, and has remarkable advantages in comparison with the previous systems. The result indicated that a model had been built to discriminate cashmere from fine wool using Vis/NIR spectra method combined with PCA-BP technology.
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Received: 2007-01-08
Accepted: 2007-04-12
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Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
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