Easy and Fast Discrimination of Female Sand Flies from Lutzomyia Species with Infrared Spectroscopy and Multivariate Analysis

Matheus E P Barbosa ^1,2, Miller Lacerda ³, Camila Calvani ³

⁰Programa de Pós-Graduação em Doenças Infecciosas e Parasitárias, Faculdade de Medicina, UFMS-Universidade Federal de Mato Grosso do Sul, Campo Grande, MS 79070-900, Brazil.

Analytical Chemistry +

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May 27, 2025

View abstract on PubMed

Summary

Accurate sandfly identification is key to controlling leishmaniasis. Fourier transform infrared (FTIR) spectroscopy with machine learning (ML) accurately distinguishes species, aiding disease control.

Area Of Science

Entomology
Spectroscopy
Bioinformatics

Background

Accurate sandfly identification is crucial for controlling visceral leishmaniasis in Latin America.
Morphological similarities between *Lutzomyia cruzi* and *Lutzomyia longipalpis* hinder traditional identification methods.
Need for innovative, reliable methods for sandfly species discrimination.

Purpose Of The Study

To evaluate Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis (PCA) and machine learning (ML) for distinguishing *Lutzomyia cruzi* and *Lutzomyia longipalpis*.
To identify informative spectral ranges for accurate species classification.

Main Methods

Fourier transform infrared (FTIR) spectroscopy was used to analyze sandfly samples.
Principal component analysis (PCA) and machine learning (ML) algorithms, including Linear support vector machine, were applied for classification.
Vibrational bands associated with lipid and carbohydrate molecules were analyzed.

Main Results

The FTIR-PCA-ML approach achieved over 95% classification accuracy.
Specific spectral ranges, including 2970-2800 cm<sup>-1</sup> (C-H stretching) and 1154-1109 cm<sup>-1</sup> (C-O and C═C stretching), were highly informative.
The method proved rapid, cost-effective, and nondestructive.

Conclusions

FTIR spectroscopy coupled with ML offers a powerful tool for accurate sandfly species identification.
This approach significantly enhances vector surveillance and entomological classification capabilities.
The integrated technique provides valuable support for visceral leishmaniasis control strategies.