{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T02:32:32Z","timestamp":1792290752399,"version":"4.4.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Chem."],"abstract":"<jats:p><jats:italic>Candida auris<\/jats:italic> and <jats:italic>Candida haemulonii<\/jats:italic> are two emerging opportunistic pathogens that have caused an increase in clinical cases in the recent years worldwide. The differentiation of some <jats:italic>Candida<\/jats:italic> species is highly laborious, difficult, costly, and time-consuming depending on the similarity between the species. Thus, this study aimed to develop a new, faster, and less expensive methodology for differentiating between <jats:italic>C<\/jats:italic>. <jats:italic>auris<\/jats:italic> and <jats:italic>C. haemulonii<\/jats:italic> based on near-infrared (NIR) spectroscopy and multivariate analysis. <jats:italic>C. auris<\/jats:italic> CBS10913 and <jats:italic>C. haemulonii<\/jats:italic> CH02 were separated in 15 plates per species, and three isolated colonies of each plate were selected for Fourier transform near-infrared (FT-NIR) analysis, totaling 90 spectra. Subsequently, principal component analysis (PCA) and variable selection algorithms, including the successive projections algorithm (SPA) and genetic algorithm (GA) coupled with linear discriminant analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA, and GA algorithms associated with LDA achieved 100% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures such as polysaccharides, peptides, proteins, or molecules resulting from yeasts\u2019 metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of <jats:italic>Candida<\/jats:italic>-like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.<\/jats:p>","DOI":"10.3389\/fchem.2024.1412288","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T05:10:20Z","timestamp":1720588220000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Near-infrared spectroscopy and multivariate analysis as effective, fast, and cost-effective methods to discriminate Candida auris from Candida haemulonii"],"prefix":"10.3389","volume":"12","author":[{"given":"Ayrton L. F.","family":"Nascimento","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony G. 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