Hyperspectral Microscope Imaging Methods to Classify Gram-Positive and Gram-Negative Foodborne Pathogenic Bacteria

Park, Bosoon; Seo, Yong Weon; Yoon, Seung-Chul; Hinton, Arthur; Windham, William R.; Lawrence, Kurt C.

doi:10.13031/trans.58.10832

Cited by 16 publications

(7 citation statements)

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“…Hyperspectral imaging (HSI) technology combines spectroscopy and imaging as a reliable nondestructive technique for bacterial colony counting, and detection and identification on various food, inert surfaces, or clinical specimens ( Gowen et al., 2015 ; Park et al., 2015 ; Bonah et al., 2019 ; Lu et al., 2020 ; Soni et al., 2022 ). Lights with small wavelength bandwidth from visible to near-infrared (Vis-NIR) are often used to generate a complete spatiospectral map of a colony for pathogen detection and identification.…”

Section: Direct Methods Of Detection and Identification Of Bacterial ...mentioning

confidence: 99%

“…Likewise, agar media that are opaque, such as blood agar plate, Baird Parker agar, and media with certain chromogens do not permit laser penetration thus unsuitable for use ( Banada et al., 2007 ; Alsulami et al., 2018 ). In such situations, a laser-based backscattering device, hyperspectral imaging ( Park et al., 2015 ), or Raman scattering ( Stöckel et al., 2016 ) system can be used to overcome the limitations of forward scattering platforms, such as BARDOT. In addition, variation in agar concentration (above or below the recommended concentration of 1.5% w/w), and media formulations can affect the scatter signatures produced by both elastic or inelastic scatterometers, thus these parameters must be controlled to acquire reproducible scatter signatures ( Bae et al., 2009 ; Banada et al., 2009 ; Bae et al., 2011a ; Assaf et al., 2014 ; Mlynáriková et al., 2015 ).…”

Section: Limitations and Alternative Solutions For Colony Scattering ...mentioning

confidence: 99%

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Petri-plate, bacteria, and laser optical scattering sensor

Bhunia

Singh

Parker

et al. 2022

Front. Cell. Infect. Microbiol.

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Classical microbiology has paved the path forward for the development of modern biotechnology and microbial biosensing platforms. Microbial culturing and isolation using the Petri plate revolutionized the field of microbiology. In 1887, Julius Richard Petri invented possibly the most important tool in microbiology, the Petri plate, which continues to have a profound impact not only on reliably isolating, identifying, and studying microorganisms but also manipulating a microbe to study gene expression, virulence properties, antibiotic resistance, and production of drugs, enzymes, and foods. Before the recent advances in gene sequencing, microbial identification for diagnosis relied upon the hierarchal testing of a pure culture isolate. Direct detection and identification of isolated bacterial colonies on a Petri plate with a sensing device has the potential for revolutionizing further development in microbiology including gene sequencing, pathogenicity study, antibiotic susceptibility testing , and for characterizing industrially beneficial traits. An optical scattering sensor designated BARDOT (bacterial rapid detection using optical scattering technology) that uses a red-diode laser, developed at the beginning of the 21st century at Purdue University, some 220 years after the Petri-plate discovery can identify and study bacteria directly on the plate as a diagnostic tool akin to Raman scattering and hyperspectral imaging systems for application in clinical and food microbiology laboratories.

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Section: Direct Methods Of Detection and Identification Of Bacterial ...mentioning

confidence: 99%

Section: Limitations and Alternative Solutions For Colony Scattering ...mentioning

confidence: 99%

Petri-plate, bacteria, and laser optical scattering sensor

Bhunia

Singh

Parker

et al. 2022

Front. Cell. Infect. Microbiol.

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“…Park et al showed that Gram-positive cell walls of S. aureus differed from the Gram-negative cell walls of Salmonella. 23 Here, CA portioned the individual samples into clusters and DA verified the accuracy of the clustering. The unsupervised classification of CA may be possible due in part to the spectral differences influenced by the diversity of backscatter signal caused by the unique profiles of polar protein structures found in the cell walls.…”

Section: 21mentioning

confidence: 97%

Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image

Eady

Park

2018

J. Spectral Imaging

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Salmonella is a leading cause of foodborne illness. Traditional detection methods require lengthy incubation periods or expensive reagent kits. Hyperspectral microscope images (HMIs) have been previously investigated as a method for early and rapid detection of bacteria by using a spectral signature that is unique to the organism. Previous HMI use with bacteria has consisted of supervised classification with hypercubes collected for single culture images isolated from highly selective growth media. In order to move forward with HMI as a detection tool in the food industry, unsupervised classification of bacteria cells in mixed culture HMIs was investigated. Four foodborne bacteria cultures, S. Typhimurium (ST) E. coli (Ec), S. aureus (Sa) and L. innocua (Li) were combined in seven different culture combinations with HMIs collected between 450 nm and 800 nm. A k-means divisive cluster analysis (CA) was implemented and mixed culture image sets were found to contain between two and four clusters. CA cluster accuracy was obtained by assigning a dummy variable of the proposed CA classification, then carrying out a discriminant analysis. From the mixed culture HMIs, 700 bacteria cells were classified and accuracies were between 91.92% and 100%, with six of the seven HMI sets resulting in > 97% accuracies. A distance measure between clusters was applied to identify unknown clusters based on single culture reference samples of the four bacteria used. Results showed that the CA has potential for unsupervised classification of bacteria cells, but the distance metric was not an adequate method for identifying the unknown cluster based on reference spectra, potentially due to the collinearity amongst bacteria spectra.

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“…5 Using this technique, gram-positive and gram-negative bacteria from chicken carcass rinsate can be classified with 99% accuracy. 6 Currently, ARS researchers are developing a database by collecting spectral signatures from many bacterial samples and food matrices to generate a spectral library "fingerprint" for each microorganism and allow the identification of unknown samples by a HMI technique.…”

Section: Hyperspectral Microscope Imaging Technologymentioning

confidence: 99%