基于二维相关光谱分析的纯棉与丝光棉制品鉴别分析

doi:10.3964/j.issn.1000-0593(2019)05-1653-08

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Abstract Pure cotton and mercerized cotton products are widely used in daily life. It is difficult to classify the pure cotton and mercerized cotton products with simple methods because they are similar in chemical and physical structures. In this work, a new method of rapid identification of pure cotton and mercerized cotton products with two-dimension correlation spectra analysis was proposed. In this work, 200 textile samples including 100 pure cotton fiber products and 100 mercerized cotton fiber products were collected. For each sample, the water content was changed 4 times and one-dimension spectra was collected, among them, the water content of 4 times was 20.20%, 14.52%, 7.77% and 0% respectively. Then their simultaneous two-dimension correlation spectra were calculated based on correlational analysis. Three kinds of classification features were extracted from the synchronous two-dimension correlation spectra. Support Vector Machine (SVM) was combined with different kind of the classification features to construct different classifiers. In this work, an information fusion method was proposed to make the multi-classifier decision. To verify the feasibility and effectiveness of the proposed method, the comparative experiments have been done. The accuracy of identification with the classifier based on extracted one-dimensional spectra features with PCA was only 76%. The accuracy of identification with the three classifiers based on extracted features from two-dimensional correlation spectra were 88%, 90% and 88% respectively. The accuracy of identification with the proposed method was 92%. Compared with one-dimension spectra based feature extraction, the two-dimension correlation spectra based feature extraction achieved feature enhancement and the multi-classifier fusion decision could improve the accuracy of classification obviously. Two-dimensional correlation spectroscopy extended spectral information to higher dimensions, unfolded hidden fold peaks in one-dimensional spectra, and had higher classification accuracy. The proposed method provided a new way for rapid identification of pure cotton and mercerized cotton products.

Key words：Two-dimension correlation spectroscopy; Cotton and mercerized cotton; Support vector machine; Information entropy; Information fusion

Received: 2018-04-07 Accepted: 2018-09-12

ZTFLH:

O657.3

Corresponding Authors: ZHAO Zhong E-mail: zhaozhong@mail.buct.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)05-1653-08 OR https://www.gpxygpfx.com/EN/Y2019/V39/I05/1653