Colony fingerprint-based discrimination of staphylococcus species with machine learning approaches

Yoshiaki Maeda, Yui Sugiyama, Atsushi Kogiso, Tae Kyu Lim, Manabu Harada, Tomoko Yoshino, Tadashi Matsunaga, Tsuyoshi Tanaka

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number2789
JournalSensors (Switzerland)
Volume18
Issue number9
DOIs
Publication statusPublished - 2018 Sep 1

Fingerprint

machine learning
staphylococcus
Dermatoglyphics
Staphylococcus
discrimination
Learning systems
Bacteria
Beverages
Imaging systems
Support vector machines
Lenses
Personnel
Neural networks
Food and Beverages
Testing
Pseudomonas aeruginosa
bacteria
Industry
time discrimination
Multivariate Analysis

Keywords

  • Colony fingerprinting
  • Lens-less imaging
  • Machine learning
  • Staphylococcus species

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Maeda, Y., Sugiyama, Y., Kogiso, A., Lim, T. K., Harada, M., Yoshino, T., ... Tanaka, T. (2018). Colony fingerprint-based discrimination of staphylococcus species with machine learning approaches. Sensors (Switzerland), 18(9), [2789]. https://doi.org/10.3390/s18092789

Colony fingerprint-based discrimination of staphylococcus species with machine learning approaches. / Maeda, Yoshiaki; Sugiyama, Yui; Kogiso, Atsushi; Lim, Tae Kyu; Harada, Manabu; Yoshino, Tomoko; Matsunaga, Tadashi; Tanaka, Tsuyoshi.

In: Sensors (Switzerland), Vol. 18, No. 9, 2789, 01.09.2018.

Research output: Contribution to journalArticle

Maeda, Y, Sugiyama, Y, Kogiso, A, Lim, TK, Harada, M, Yoshino, T, Matsunaga, T & Tanaka, T 2018, 'Colony fingerprint-based discrimination of staphylococcus species with machine learning approaches', Sensors (Switzerland), vol. 18, no. 9, 2789. https://doi.org/10.3390/s18092789
Maeda, Yoshiaki ; Sugiyama, Yui ; Kogiso, Atsushi ; Lim, Tae Kyu ; Harada, Manabu ; Yoshino, Tomoko ; Matsunaga, Tadashi ; Tanaka, Tsuyoshi. / Colony fingerprint-based discrimination of staphylococcus species with machine learning approaches. In: Sensors (Switzerland). 2018 ; Vol. 18, No. 9.
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