eNose technology can detect and classify human pathogenic molds in vitro: a proof-of-concept study of Aspergillus fumigatus and Rhizopus oryzae

• Published in: Journal of breath research Vol. 10; no. 3; p. 036008
• Main Authors: de Heer, K, Vonk, S I, Kok, M, Kolader, M, Zwinderman, A H, van Oers, M H J, Sterk, P J, Visser, C E
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 22.07.2016
• Subjects: Accuracy; Algorithms; Aspergillus fumigatus - isolation & purification; Breath Tests; Candida albicans - isolation & purification; Discriminant Analysis; Electronic Nose; Exhalation; Fungal infections; Humans; invasive mycosis; Mold; Rhizopus - isolation & purification; ROC Curve; Streptococcus infections; VOCs; Volatile organic compounds; Yard waste
• ISSN: 1752-7155; 1752-7163; 1752-7163
• DOI: 10.1088/1752-7155/10/3/036008

Invasive pulmonary mold disease (IPMD) is often fatal in neutropenic patients. This is because IPMD is difficult to diagnose timely, especially when non-Aspergillus molds are the causative agent, as they are usually not associated with a positive galactomannan assay. In 2013 we showed that exhaled breath analysis might be used to diagnose invasive aspergillosis through profiling of patterns in exhaled volatile organic compounds (VOCs) by electronic nose (eNose) technology. The current study aimed to determine (1) whether molds can be discriminated from other microorganisms (using two mold species: Aspergillus fumigatus and a pathogenic mold not associated with a positive galactomannan assay, i.c. Rhizopus oryzae) and (2) whether both molds can be discriminated from each other. First, we cultured strains of Streptococcus pneumoniae, Escherichia coli, Pseudomonas aeruginosa, Candida albicans, A. fumigatus and R. oryzae in separate airtight bottles. We examined whether an eNose (Cyranose 320) could discriminate the headspaces of bottles with molds from those with bacteria/yeasts. Second, we examined whether an eNose could discriminate A. fumigatus and R. oryzae. Diagnostic algorithms were created using canonical discriminant analysis after principle component analysis. Primary outcome parameter was the validated accuracy. The eNose discriminated A. fumigatus from bacteria/yeasts with a cross-validated accuracy of 92.9% (sensitivity 95.2%, specificity 91.9%). The eNose had an accuracy (validated using split-half analysis) of 100% in discriminating A. fumigatus from R. oryzae. Our study suggests that an eNose can identify and classify molds in vitro. This warrants prospective in vivo studies aimed at detecting and classifying IPMD using exhaled breath.