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Citation: YAN Fan, ZHU Qi-Bing, HUANG Min, LIU Cai-Zheng, ZHANG Li-Wen, ZHANG Heng. Identification of Mixture Components Using Sparse Non-Negative Least Squares Algorithm Base on Raman Spectroscopy[J]. Chinese Journal of Analytical Chemistry, ;2020, 48(2): 298-305. doi: 10.19756/j.issn.0253-3820.191256 shu

Identification of Mixture Components Using Sparse Non-Negative Least Squares Algorithm Base on Raman Spectroscopy

1.
Key Laboratory of Advanced Process Control for Light Industy, Ministry of Education, Jiangnan University, Wuxi 214122, China;
2.
Beijing Zhuoli Hanguang Instrument Co. Ltd., Beijing 101102, China

Received Date: 6 May 2019
Revised Date: 15 November 2019

Fund Project: This work was supported by the National Natural Science Foundation of China (No. 61775086).

Raman spectral data contain fingerprint spectral information corresponding to the components of the measured substances, which is an effective method to identify the components of mixtures. The traditional mixture component detection methods based on Raman spectrum have some issues such as the difficulty in extracting spectral features, the performance of search and peak matching algorithms is easily affected by the database, and the recognition accuracy is difficult to guarantee. To overcome these problems, a method of mixture components recognition using Raman spectrum based on sparse non-negative least squares algorithm is proposed. In this method, the spectral data of the mixture to be recognized is regarded as the linear representation of the spectral data of all kinds of pure substances. Considering the sparse characteristic of the mixture components quantity relative to the pure substance quantity in the database, the linear representation coefficient of the mixture spectrum in the pure substance spectrum data is obtained by the sparse non-negative least squares algorithm. And the suspected components are determined according to the statistical 2δ principle. On this basis, the iterative least squares algorithm combined with the T-distribution test method is used to realize the final identification of the mixture components. The Raman spectral data of 500 pure substances are used to build a standard database for identification of the experimental sample of 19 mixtures with equal volume ratio of components and 81 mixtures with different volume ratio of components. The results show that the precision is 90.24% and the recall is 88.10% under the condition of equal volume ratios, and the precision is 93.22% and the recall is 83.65% under the condition of different volume ratios, which proves the good stability and accuracy of the proposed algorithm.
- Identification of mixture components,
- Raman spectroscopy,
- Sparse non-negative least squares algorithm
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