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Title: Chagas disease vectors identification using visible and near-infrared spectroscopy
Authors: Depickere, Stephanie
Ravelo García, Antonio Gabriel 
Lardeux, Frederic
UNESCO Clasification: 320505 Enfermedades infecciosas
3314 Tecnología médica
Keywords: Species Determination
Chagas Disease
Machine Learning
Classification, et al
Issue Date: 2020
Journal: Chemometrics and Intelligent Laboratory Systems 
Abstract: Chagas disease, caused by the parasite Trypanosoma cruzi, is widespread in Latin America, where the disease remains one of the major public health problems. This condition is mostly transmitted by triatomines which are haematophagous insects all their life. With 154 species described in the world, the correct determination of the species involved in the transmission is crucial to develop efficient control strategies. This can be achieved by taxonomic keys (available only for adult stages, nymphal instars must be reared), or by molecular techniques. Both are time and/or money consuming, showing the needs of new identification tools, especially for nymphal instars which are the most frequently found on the field. Visible and near-infrared spectroscopy (VIS-NIR), used successfully these last years in various organisms' determination, was applied on a sample of three species from Bolivia: Triatoma infestans, Triatoma sordida and Triatoma guasayana. The spectrum of the dorsal part of the head from nymphal instars and adult stages was taken for each specimen of each species. Different methods of preprocessing and selection of variables (wavelengths) were tested to find the best model of classification for the three species. Each model was evaluated by different indices: accuracy, specificity, and F1 score. The comparison of the performance of each model evidenced that the best results were obtained when using a short spectrum (400-2000 nm) without pre-processing. A total of 32 components were retained by tuning, and 933 wavelengths were kept by the backward feature selection algorithm. Applying it on a new sample of insects, this model showed a global accuracy of 97.2% (95.0-98.6). The F1 score was greater than 0.95, and the specificity greater than 0.94 for all the species. For the first time, a tool is available to quickly identify and with a high accuracy nymphal instars and adults of triatomines.
ISSN: 0169-7439
DOI: 10.1016/j.chemolab.2019.103914
Source: Chemometrics And Intelligent Laboratory Systems [ISSN 0169-7439], v. 197, 103914
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