Direct Analysis in Real Time-Mass Spectrometry and Kohonen Artificial Neural Networks for Species Identification of Larva, Pupa and Adult Life Stages of Carrion Insects
| Autor: | Jennifer Y. Rosati, Justine E. Giffen, Samira Beyramysoltan, Rabi A. Musah |
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| Rok vydání: | 2018 |
| Předmět: |
Self-organizing map
Insecta 01 natural sciences Mass Spectrometry Analytical Chemistry 03 medical and health sciences 0302 clinical medicine Species Specificity Animals Carrion insects Carrion 030216 legal & forensic medicine Calliphoridae Phoridae Larva biology Chemistry business.industry fungi 010401 analytical chemistry Pupa Pattern recognition Linear discriminant analysis biology.organism_classification 0104 chemical sciences Neural Networks Computer Artificial intelligence business |
| Zdroj: | Analytical Chemistry. 90:9206-9217 |
| ISSN: | 1520-6882 0003-2700 |
| Popis: | Species determination of the various life stages of flies (Order: Diptera) is challenging, particularly for the immature forms, because analogous life stages of different species are difficult to differentiate based on morphological features alone. It is demonstrated here that direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) combined with supervised Kohonen Self-Organizing Maps (SOM) enables accomplishment of species-level identification of larva, pupa, and adult life stages of carrion flies. DART-HRMS data for each life stage were acquired from analysis of ethanol suspensions representing Calliphoridae, Phoridae, and Sarcophagidae families, without additional sample preparation. After preprocessing, the data were subjected to a combination of minimum Redundancy Maximal Relevance (mRMR) and Sparse Discriminant Analysis (SDA) methods to select the most significant variables for creating accurate SOM models. The resulting data were divided into training and validation sets and then analyzed by the SOM method to define the proper discrimination models. The 5-fold venetian blind cross-validation misclassification error was below 7% for all life stages, and the validation samples were correctly identified in all cases. The multiclass SOM model also revealed which chemical components were the most significant markers for each species, with several of these being amino acids. The results show that processing of DART-HRMS data using artificial neural networks (ANNs) based on the Kohonen SOM approach enables rapid discrimination and identification of fly species even for the immature life stages. The ANNs can be continuously expanded to include a larger number of species and can be used to screen DART-HRMS data from unknowns to rapidly determine species identity. |
| Databáze: | OpenAIRE |
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