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Open AccessArticle

Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches

1
Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei, Tokyo 184-8588, Japan
2
Malcom Co., Ltd., 4-15-10, Honmachi, Shibuya-ku, Tokyo 151-0071, Japan
3
Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 2789; https://doi.org/10.3390/s18092789
Received: 25 July 2018 / Revised: 17 August 2018 / Accepted: 23 August 2018 / Published: 24 August 2018
(This article belongs to the Special Issue Sensors for Cell Analysis)
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. View Full-Text
Keywords: colony fingerprinting; Staphylococcus species; machine learning; lens-less imaging colony fingerprinting; Staphylococcus species; machine learning; lens-less imaging
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MDPI and ACS Style

Maeda, Y.; Sugiyama, Y.; Kogiso, A.; Lim, T.-K.; Harada, M.; Yoshino, T.; Matsunaga, T.; Tanaka, T. Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches. Sensors 2018, 18, 2789.

AMA Style

Maeda Y, Sugiyama Y, Kogiso A, Lim T-K, Harada M, Yoshino T, Matsunaga T, Tanaka T. Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches. Sensors. 2018; 18(9):2789.

Chicago/Turabian Style

Maeda, Yoshiaki; Sugiyama, Yui; Kogiso, Atsushi; Lim, Tae-Kyu; Harada, Manabu; Yoshino, Tomoko; Matsunaga, Tadashi; Tanaka, Tsuyoshi. 2018. "Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches" Sensors 18, no. 9: 2789.

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