(Invited) Rapid and Robust Discrimination of Food-Contaminating Microorganisms Guided By Machine Learning

Interests in safety and security of foods are more growing than ever with rising global populations. Manufacturing processes for food production need to be strictly managed to exclude the possible contamination by toxic microorganisms including bacteria and fungi. Upon occurrence of microbial contamination, the species of the contaminating microorganisms should be rapidly discriminated to limit further expansion of contamination. Pre-testing of microbial contamination of food ingredients prior to manufacturing processes is also carried out to ensure that specific toxic microorganisms are not detected from the ingredients. Conventionally, sequencing of the barcode regions in the microbial genomes including the genes encoding 16S and 18S rRNA (for prokaryotes and eukaryotes, respectively) has been widely employed for microbial discrimination. More recently, matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS)-based discrimination method emerged. However, these approaches have several drawbacks such as requirements of expensive equipment, highly skilled operators, and relatively long assay time for several days. Therefore, it is not easy to employ these methods in food manufacturers, majorities of which are small-sized companies. To address this issue, we have developed a novel bioimaging-based approach for discrimination of bacteria and fungi, termed “colony fingerprinting”. In colony fingerprinting, bacterial or fungal colonies were formed on transparent agar media. The colonies were irradiated by light emitting diode (LED), and complementary metal-oxide-semiconductor (CMOS) image sensors are used to detect the light penetrating or dispersed by the colonies which generate microbial species-specific optical patterns, termed “colony fingerprints”. We extracted a number of discriminative parameters representing the features of morphology and intensity-distribution of the colony fingerprints with the aid of bioimage informatics tools. Discrimination of microbial species were carried out by machine learning-based analyses of the extracted parameters. As a proof-of-concept study, we developed a colony fingerprinting platform consisting of a CMOS image sensor (pixel size: 3.2 μm, imaging area: 6.55 × 4.92 mm 2 ), pinhole, and blue LED. Colonies of 5 closely related Staphylococcus spp. ( S. aureus , S. epidermidis , S. haemolyticus , S. saprophyticus , and S. simulans ) were visualized, and 14 types of discriminative parameter