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Citation: NAN Di-Na, LIU Wei-Wei, FU Wen-Xiang, LI Bao-Qiang, KONG Jing-Lin. Study on Fast Recognition of Biotoxins and Biological Modifiers Using Data Fusion Algorithm[J]. Chinese Journal of Analytical Chemistry, ;2020, 48(10): 1343-1350. doi: 10.19756/j.issn.0253-3820.201137 shu

Study on Fast Recognition of Biotoxins and Biological Modifiers Using Data Fusion Algorithm

State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China

Received Date: 17 March 2020
Revised Date: 2 July 2020

Fund Project: This work was supported by the National Natural Science Foundation of China (No. 61775017).

A method for analysis of various biotoxins and biological modifiers based on electrospray ionization-ion mobility spectrometry (ESI-IMS) and Raman spectroscopy was established. A database of 26 kinds of white powders, including alkaloids such as aconitine and tetrodotoxin, biological modifiers such as bradykinin, substance P and their structural analogs, biotoxins such as conotoxins, α-bungarotoxin and ricin, and common white powder such as salt, flour and bovine serum albumin was constructed. Six pattern recognition algorithms, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), naive bayesian (NB), classification tree (CT) and support vector machine (SVM), were used to classify the single spectrum and the fusion data with this database. The results showed that the recognition accuracy of different methods was 76.0%-97.2%, and among which, the fusion recognition algorithm based on the support vector machine model achieved a high recognition accuracy of 97.2%. Meanwhile, the difference of recognition accuracy was analyzed in this study. This method could also distinguish multiple structural analogues of the two biological modifiers, which could be used in rapid distinguish of unknown white powder.
- Data fusion,
- Pattern recognition,
- Raman spectroscopy,
- Ion mobility spectrometry,
- Biotoxin
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