Rapid discrimination of fungal species by the colony fingerprinting

Abstract The contamination of foods and beverages by fungi is a severe health hazard. The rapid identification of fungi species in contaminated goods is important to avoid further contamination. To this end, we developed a fungal discriminationmethod based on the bioimage informaticsapproach of colony fingerprinting. This method involves imaging and visualizing microbial colonies(referred to as colony fingerprints) using a lens-less imaging system. Subsequently, the quantitative image features were extracted as discriminativeparameters and subjected to analysis using machine learning approaches. Colony fingerprintinghas been previously found to be a promising approach to discriminatebacteria. In the present proof-of-concept study, we tested whether this method is also useful for fungal discrimination. As a result, 5 fungi belonging to the Aspergillus, Penicilium, Eurotium, Alternaria, and Fusarium genera were successfully discriminatedbased on the extracted parameters, including the number of hyphae and their branches, and their intensity distributions on the images. The discriminationof 6 closely-related Aspergillus spp. was also demonstrated using additional parameters. The cultivation time required to generate the fungal colonieswith a sufficient size for colony fingerprintingwas less than 48 h, shorter than those for other discriminationmethods, including MALDI-TOF-MS. In addition, colony fingerprintingdid not require any cumbersome pre-treatment steps prior to discrimination. Colony fingerprintingis promising for the rapid and easy discriminationof fungi for use in the ensuring the safety of food manufacturing.