Summary: | <i>Leishmania infantum</i> is the agent of visceral leishmaniasis in the Mediterranean basin. It is transmitted by sandflies of the subgenus <i>Larroussius</i>. Although <i>Phlebotomus perniciosus</i> is the most important vector in this area, an atypical <i>Ph. perniciosus</i> easily confused with <i>Ph. longicuspis</i> has been observed in North Africa. MALDI-TOF MS, an important tool for vector identification, has recently been applied for the identification of sandflies. Spectral databases presented in the literature, however, include only a limited number of <i>Larroussius</i> species. Our objective was to create an in-house database to identify Mediterranean sandflies and to evaluate the ability of MALDI-TOF MS to discriminate close species or atypical forms within the <i>Larroussius</i> subgenus. Field-caught specimens (<i>n</i> = 94) were identified morphologically as typical <i>Ph. perniciosus</i> (PN; <i>n</i> = 55), atypical <i>Ph. perniciosus</i> (PNA; <i>n</i> = 9), <i>Ph. longicuspis</i> (<i>n</i> = 9), <i>Ph. ariasi</i> (<i>n</i> = 9), <i>Ph. mascittii</i> (<i>n</i> = 3), <i>Ph. neglectus</i> (<i>n</i> = 5), <i>Ph. perfiliewi</i> (<i>n</i> = 1), <i>Ph. similis</i> (<i>n</i> = 9) and <i>Ph.</i> <i>papatasi</i> (<i>n</i> = 2). Identifications were confirmed by sequencing of the mtDNA CytB region and sixteen specimens were included in the in-house database. Blind assessment on 73 specimens (representing 1073 good quality spectra) showed a good agreement (98.5%) between MALDI-TOF MS and molecular identification. Discrepancies concerned confusions between <i>Ph. perfiliewi</i> and <i>Ph. perniciosus</i>. Hierarchical clustering did not allow classification of PN and PNA. The use of machine learning, however, allowed discernment between PN and PNA and between the lcus and lcx haplotypes of <i>Ph. longicuspis</i> (accuracy: 0.8938 with partial-least-square regression and random forest models). MALDI-TOF MS is a promising tool for the rapid and accurate identification of field-caught sandflies. The use of machine learning could allow to discriminate similar species.
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