Detection and identification of microbial species are crucial in a wide range of industries, including production of beverages, foods, cosmetics, and pharmaceuticals. Traditionally, colony formation and its morphological analysis (e.g., size, shape, and color) with a naked eye have been employed for this purpose. However, such a conventional method is time consuming, labor intensive, and not very reproducible. To overcome these problems, we propose a novel method that detects microcolonies (diameter 10–500 μm) using a lensless imaging system. When comparing colony images of five microorganisms from different genera (Escherichia coli, Salmonella enterica, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida albicans), the images showed obvious different features. Being closely related species, St. aureus and St. epidermidis resembled each other, but the imaging analysis could extract substantial information (colony fingerprints) including the morphological and physiological features, and linear discriminant analysis of the colony fingerprints distinguished these two species with 100% of accuracy. Because this system may offer many advantages such as high-throughput testing, lower costs, more compact equipment, and ease of automation, it holds promise for microbial detection and identification in various academic and industrial areas.

Detection and discrimination of bacteria are crucial in a wide range of industries, including clinical testing, and food and beverage production. Staphylococcus species cause various diseases, and are frequently detected in clinical specimens and food products. In particular, S. aureus is well known to be the most pathogenic species. Conventional phenotypic and genotypic methods for discrimination of Staphylococcus spp. are time-consuming and labor-intensive. To address this issue, in the present study, we applied a novel discrimination methodology called colony fingerprinting. Colony fingerprinting discriminates bacterial species based on the multivariate analysis of the images of microcolonies (referred to as colony fingerprints) with a size of up to 250 μm in diameter. The colony fingerprints were obtained via a lens-less imaging system. Profiling of the colony fingerprints of five Staphylococcus spp. (S. aureus, S. epidermidis, S. haemolyticus, S. saprophyticus, and S. simulans) revealed that the central regions of the colony fingerprints showed species-specific patterns. We developed 14 discriminative parameters, some of which highlight the features of the central regions, and analyzed them by several machine learning approaches. As a result, artificial neural network (ANN), support vector machine (SVM), and random forest (RF) showed high performance for discrimination of theses bacteria. Bacterial discrimination by colony fingerprinting can be performed within 11 h, on average, and therefore can cut discrimination time in half compared to conventional methods. Moreover, we also successfully demonstrated discrimination of S. aureus in a mixed culture with Pseudomonas aeruginosa. These results suggest that colony fingerprinting is useful for discrimination of Staphylococcus spp.

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