Dataset Information

Detection and Classification of Bacterial Cells After Centrifugation and Filtration of Liquid Specimens Using Laser-Induced Breakdown Spectroscopy.

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.

SUBMITTER: Blanchette EJ

PROVIDER: S-EPMC9411782 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Detection and Classification of Bacterial Cells After Centrifugation and Filtration of Liquid Specimens Using Laser-Induced Breakdown Spectroscopy.

Blanchette Emma J EJ Sleiman Sydney C SC Arain Haiqa H Tieu Alayna A Clement Chloe L CL Howson Griffin C GC Tracey Emily A EA Malik Hadia H Marvin Jeremy C JC Rehse Steven J SJ

Applied spectroscopy 20220524 8

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. Summ ...[more]

PMID: 35608993

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Project description:The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.

Dataset Information

Detection and Classification of Bacterial Cells After Centrifugation and Filtration of Liquid Specimens Using Laser-Induced Breakdown Spectroscopy.

Publications

Detection and Classification of Bacterial Cells After Centrifugation and Filtration of Liquid Specimens Using Laser-Induced Breakdown Spectroscopy.

Similar Datasets

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