Abstract

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Title:: Algorithmic identification for high-field asymmetric ion mobility spectra of common wound-infecting bacteria

Author(s):: LU Yanyi; YIN Jun; BI Yutian; YAN Jun; LI Min; HE Qinghua; State Key Laboratory of Trauma, Burns and Combined Injury/Surgery Institute of the Third Military Medical University, Chongqing 400042, China

Keywords:: ; Keywords: high-field asymmetric ion mobility spectra; common wound-infecting bacteria; pattern recognition algorithm

Abstract:: Objective To classify and recognize the high-field asymmetric ion mobility spectra (FAIMS) of common wound-infecting bacteria (escherichia coli, staphylococcus aureus and pseudomonas aeruginosa) cultured in thioglycolate (TH) broth and bare TH broth by pattern recognition algorithm. Methods The spectra of training set and testing set of four types of samples were obtained by FAIMS analyzer. After pretreatment, principal component analysis and linear discriminant analysis were used to reduce the dimension and extract features for obtaining the three-dimensional spatial didtribution patterns of training set and testing set. Finally, k-nearest neighbors algorithm was used to identify the four types of samples. Results After feature extraction, the samples showed good separation effects under both positive and negative modes, and the separability of positive mode was superior to that of negative mode. When K was appropriately assigned, the recognition rate under positive and negative modes could be up to 90% and 70%, respectively, or even higher. For the data model in this study, the optimal K was equal to the odd number closest to sample number of each type. Conclusion The proposed algorithm can be used to classify and recognize the FAIMS of common wound-infecting bacteria cultured in TH broth.