Title:	Rapid identification of feline sporotrichosis by ATR‑FTIR spectroscopy coupled with machine learning.
Authors:	DOMINGOS, E. L. ZEIN, A. K. el SUREK, M. GRECA JUNIOR, H. SANTOS‑WEISS, I. C. R. dos FEIRREIRA, L. M. PONTAROLO, P.
Affiliation:	ERIC LUIZ DOMINGOS, UNIVERSIDADE FEDERAL DO PARANÁ AHMAD KASSEM EL ZEIN, UNIVERSIDADE FEDERAL DO PARANÁ MONICA SUREK, CPATSA HAROLDO GRECA JUNIOR, SECRETARIA MUNICIPAL DE SAÚDE DE SÃO JOSÉ DOS PINHAIS IZABELLA CASTILHOS RIBEIRO DOS SANTOS‑WEISS, UNIVERSIDADE FEDERAL DO PARANÁ LUANA MOTA FERREIRA, UNIVERSIDADE FEDERAL DO PARANÁ ROBERTO PONTAROLO, UNIVERSIDADE FEDERAL DO PARANÁ.
Date Issued:	2026
Citation:	Analytical and Bioanalytical Chemistry, Jul. 2026.
Description:	Feline sporotrichosis is a zoonotic subcutaneous mycosis of major relevance in endemic regions of Latin America, particu- larly in Brazil. Current diagnostic methods are limited by turnaround time, specificity, and cost. In this context, spectros- copy combined with machine learning has emerged as a promising alternative. This study aimed to develop and validate predictive models for the rapid and minimally invasive diagnosis of feline sporotrichosis using ATR-FTIR spectroscopy applied to plasma samples. Seventy-five cats were included and classified by fungal culture as the reference standard. Plasma samples were analyzed by ATR-FTIR (4000–400 cm⁻1 ), with 20 spectra acquired per animal. Data were evaluated using six machine learning algorithms under three validation frameworks: spectral-level internal testing, Leave-One-Patient-Out (LOPO) cross-validation, and independent external validation (n = 15 animals). On the internal test set, Random Forest, KNN, and LightGBM showed similarly strong discriminative ability (AUC ≥ 0.998), with no significant differences among them. The LOPO cross-validation confirmed biological generalization, with SVM achieving the highest AUC (0.896). External validation confirmed consistent performance, with Random Forest and LightGBM achieving an AUC of 1.000 and excellent probabilistic calibration (low Brier scores). Spectral variable importance and SHAP analyses revealed that discrimination was primarily driven by regions associated with carbohydrates (1040–1050 cm⁻1 ) and lipids (1746–1751 cm⁻1 ), reflecting the host’s systemic metabolic and immunological response to the infection. These findings suggest that ATR-FTIR combined with machine learning provides consistent and generalizable diagnostic performance, representing a rapid and minimally invasive approach within the One Health framewor.
Thesagro:	Gato Doença Animal
NAL Thesaurus:	Animal diseases
Keywords:	Esporotricose felina Espectroscopia ATR-FTIR Modelo de diagnóstico Aprendizado de máquina Saúde Única Amostras de plasma
DOI:	https://doi.org/10.1007/s00216-026-06671-3
Notes:	On-line
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CPATSA)