Literature DB >> 25643595

On the application of optical forward-scattering to bacterial identification in an automated clinical analysis perspective.

Umberto Minoni1, Alberto Signoroni2, Giulia Nassini2.   

Abstract

The Optical Forward Scattering (OFS) technique can be used to identify pathogens by direct observation of bacteria colonies growing on a culture plate. The identification is based on the acquisition of scattering images from isolated colonies and their subsequent comparison with reference images acquired from known bacteria. The technique has been mainly studied for the identification of pathogens in the food-safety field. This paper focuses on the possibility of extending the applicability of the technique to the field of clinical laboratory automation. This scenario requires that the paradigm of image acquisition at fixed colony-dimension, well established in the food-safety applications, should be substituted by an acquisition at fixed incubation time. As a consequence, the scatterometer must be adjustable in real-time for adapting to the actual features of the bacterial colony. The paper describes an OFS system prototype qualified by the possibility to tune both the laser beam diameter and the acquisition camera field of view. Preliminary experiments on bacteria cultures from pathogens causing infections of the urinary tract show that the proposed approach is promising for the development of an automated bacteria identification station. The new OFS approach also involves an alternative method for building a reference image database for subsequent image analysis.
Copyright © 2015 Elsevier B.V. All rights reserved.

Keywords:  Bacterial rapid identification; Colonies analysis; Light scattering.

Mesh:

Year:  2015        PMID: 25643595     DOI: 10.1016/j.bios.2015.01.047

Source DB:  PubMed          Journal:  Biosens Bioelectron        ISSN: 0956-5663            Impact factor:   10.618


  4 in total

1.  Automatic Digital Plate Reading for Surveillance Cultures.

Authors:  Thomas J Kirn
Journal:  J Clin Microbiol       Date:  2016-08-10       Impact factor: 5.948

2.  Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis.

Authors:  Kwang Seob Lee; Hyung Jae Lim; Kyungnam Kim; Yeon-Gyeong Park; Jae-Woo Yoo; Dongeun Yong
Journal:  Microbiol Spectr       Date:  2022-03-02

3.  Development of a Smartphone-Integrated Reflective Scatterometer for Bacterial Identification.

Authors:  Iyll-Joon Doh; Brianna Dowden; Valery Patsekin; Bartek Rajwa; J Paul Robinson; Euiwon Bae
Journal:  Sensors (Basel)       Date:  2022-03-30       Impact factor: 3.576

4.  Novel Perspectives on the Characterization of Species-Dependent Optical Signatures of Bacterial Colonies by Digital Holography.

Authors:  Igor Buzalewicz; Małgorzata Kujawińska; Wojciech Krauze; Halina Podbielska
Journal:  PLoS One       Date:  2016-03-04       Impact factor: 3.240

  4 in total

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