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

Sensors
Yoshiaki MaedaTsuyoshi Tanaka

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 appr...Continue Reading

References

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Citations

Oct 13, 2020·Critical Reviews in Clinical Laboratory Sciences·Sander De BruyneJoris R Delanghe
Oct 7, 2019·Biosensors & Bioelectronics·Yoshiaki MaedaTsuyoshi Tanaka

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Methods Mentioned

BETA
Raman spectroscopy
light scattering

Software Mentioned

IC Capture
MATLAB
ImageJ
R

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