Author ORCID Identifier
Steven J. Rehse : https://orcid.org/0000-0003-1173-773X
Document Type
Article
Publication Date
2022
Publication Title
Applied Spectroscopy
Volume
76
Issue
8
First Page
894
Keywords
Laser-induced breakdown spectroscopy, LIBS, bacteria, filtration medium, centrifugation, discriminant function analysis, artificial neural network, partial least squares discriminant analysis
Last Page
904
Abstract
Five species of bacteria including Escherichia coli, Mycobacterium smegmatis, Pseudomonas aeruginosa, Staphylococcus epidermidis, and Enterobacter cloacae were deposited from suspensions of various titers onto disposable nitrocellulose filter media for analysis by laser-induced breakdown spectroscopy (LIBS). Bacteria were concentrated and isolated in the center of the filter media during centrifugation using a simple and convenient sample preparation step. Summing all the single-shot LIBS spectra acquired from a given bacterial deposition provided perfectly sensitive and specific discrimination from sterile water control specimens in a partial least squares discriminant analysis (PLS-DA). Use of the single-shot spectra provided only a 0.87 and 0.72 sensitivity and specificity, respectively. To increase the statistical validity of chemometric analyses, a library of pseudodata was created by adding Gaussian noise to the measured intensity of every emission line in an averaged spectrum of each bacterium. The normally distributed pseudodata, consisting of 4995 spectra, were used to compare the performance of the PLS-DA with a discriminant function analysis (DFA) and an artificial neural network (ANN). For the highly similar bacterial data, no algorithm showed significantly superior performance, although the PLS-DA performed least accurately with a classification error of 0.21 compared to 0.16 and 0.17 for ANN and DFA, respectively. Single-shot LIBS spectra from all of the bacterial species were classified in a DFA model tested with a tenfold cross-validation. Classification errors ranging from 20% to 31% were measured due to repeatability limitations in the single-shot data.
DOI
10.1177/00037028221092789
Funding Reference Number
RGPIN/ 05842-2017
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Blanchette, Emma J.; Sleiman, Sydney C.; Arain, Haiqa; Tieu, Alayna; Clement, Chloe L.; Howson, Griffin C.; Tracey, Emily A.; Malik, Hadia; Marvin, Jeremy C.; and Rehse, Steven J.. (2022). Detection and Classification of Bacterial Cells After Centrifugation and Filtration of Liquid Specimens Using Laser-Induced Breakdown Spectroscopy. Applied Spectroscopy, 76 (8), 894-904.
https://scholar.uwindsor.ca/physicspub/202