[Application of Raman spectra feature extraction in chemical fiber component qualitative identification].
According to the characteristics of the textile fibers Raman spectra, a qualitative identification method based on Raman feature extraction is proposed. This fast method consists of spectrum measurement and spectral data processing algorithm, including spectrum preprocessing, feature extraction and matching recognition. It can be used to identify the components of fibers or fabrics, especially chemical fibers, which is an inspective difficulty in daily analytic work for its remarkable Raman feature. The authors performed an experiment to analyze 4 typical and widely used kinds of fibers as algorithm verification. They are terylene fiber, acrylic fiber, nylon fiber and rayon fiber. To identify the components of one test sample, first the authors set up feature tables of these 4 standard samples, which describe the features of their preprocessed spectra containing both position information and intensity information, then extract features of the test sample. The authors match these features with the tables and calculate the matching confidence coefficients of the results, which can be used to filter the unexpected matching results caused by accident and attain the final qualitative identification result. The experimental results confirm that this method is effective, efficient and expansible, which means it can be used to identify more actual fiber types by adding more standard spectra to the feature table database. In addition, it is a pure optical method, which needs only a small quantity of sample without any pretreatment. The whole identification process is damage-free, pollution-free and suitable for various kinds of fabrics. Compared to all existing methods, this Raman spectrum identification method can solve the limitation of efficiency, pollution, universality, and fill a gap in fabric inspection field.
Qiao
Xi-Ya
XY
National Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China.
Dai
Lian-Kui
LK
Wu
Jian-Jian
JJ