Dissertation
Hyperspectral Imaging: a Potential Novel Tool for Rapid Microbial Identification
Washington State University
Doctor of Philosophy (PhD), Washington State University
2023
DOI:
https://doi.org/10.7273/000005224
Abstract
Hyperspectral imaging (HSI) technology is a combination of conventional imaging and spectroscopic techniques, which has a unique ability to simultaneously acquire both the spatial and spectral data of a specimen. The HSI is a novel optical tool in food processing that has a great potential for rapidly identifying bacteria (and possibly other microorganisms) at the cellular and colony levels. This study evaluated the efficacy of the custom-assembled HSI system for the rapid identification of pathogens in dairy products (whole milk and whole milk powder) at the colony and cellular levels. The colony and cellular levels studies were designed as completely randomized with six replications. Three strains of Listeria monocytogenes, four strains of Escherichia coli O157: H7, one strain each of Big Six Shiga toxin-producing E. coli, three strains of Staphylococcus aureus, and ten serovars of Salmonella were used in this study. Pure cultures were streaked for isolation on respective selective media, and hyperspectral data (400-1,100 nm wavelength) at the colony and cellular levels were collected and stored as reference libraries. Whole milk and whole milk powder were artificially inoculated (<10 CFU/g or mL) with individual pathogenic strains/serovars. All milk and milk powder samples were enriched using Brain Heart Infusion (BHI) broth at 37°C for 24 h, streaked for isolation on the respective selective media, and hyperspectral data for individual pathogenic strains/serovars at the colony and cellular levels were acquired and treated as test samples data. The acquired colony or cellular images were imported into ENVI software, and three regions of interest were selected for each image to obtain hyperspectral data for reference libraries and test samples. Using the kNN classifier and cross-validation technique, overall classification accuracies of 90.38 and 34% were obtained for the colony and cellular level identification, respectively. The individual classification accuracies of pathogens in dairy products at the colony level varied between 77.5 to 100%, whereas the accuracy varied between 2.78 and 49.17% for the cellular level.
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Details
- Title
- Hyperspectral Imaging
- Creators
- Amninder Singh Sekhon
- Contributors
- Minto Michael (Advisor)Girish Ganjyal (Committee Member)Lakshmikantha Channaiah (Committee Member)Thuy Bernhard (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Food Science, School of
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 148
- Identifiers
- 99901019636001842
- Language
- English
- Resource Type
- Dissertation