Evidence map›Paper›PMID 37050545›Full record

ArticleSensors (Basel, Switzerland)2023

Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns.

Iyll-Joon Doh, Diana Vanessa Sarria Zuniga, Sungho Shin, Robert E Pruitt, Bartek Rajwa, J Paul Robinson, Euiwon Bae

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Iyll-Joon DohApplied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0002-2003-5433
Diana Vanessa Sarria ZunigaDepartment of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USA.
Sungho ShinDepartment of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA.
Robert E PruittDepartment of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USA.
Bartek RajwaBindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0001-7540-8236
J Paul RobinsonDepartment of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA.
Euiwon BaeApplied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0001-6095-3223

Funding

United States Department of Agriculture 59-8072-1-002
6 · The paper itself

Abstract

The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.

Indexed as

BacteriaAlgorithmsBacterial Physiological PhenomenaElasticityLightbacterial colony phenotypingbacterial identificationelastic light scatteringhyperspectral imaginglight diffractionoptical sensingsupercontinuum laser

Identifiers

PMID37050545
PMCPMC10098818

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.