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

Abstract 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, ricin and common white powder such as salt, flour and bovine serum albumin was constructed. Six pattern recognition algorithms of 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 based on this database. The results showed that the recognition accuracy of different methods was 76.0%–97.2%, among which the fusion recognition algorithm based on the SVM model achieved the highest recognition accuracy of 97.2%. Besides, the difference of recognition accuracy was analyzed in this study. This method could also distinguish multiple structural analogues of the two biological modifiers, and was suitable for the rapid identification of unknown white powder.