Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy
Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Journal of Food Protection |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0362028X24001807 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850173366074343424 |
|---|---|
| author | Nicharee Wisuthiphaet Huanle Zhang Xin Liu Nitin Nitin |
| author_facet | Nicharee Wisuthiphaet Huanle Zhang Xin Liu Nitin Nitin |
| author_sort | Nicharee Wisuthiphaet |
| collection | DOAJ |
| description | Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and Escherichia coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier (SVC) and Random Forest (RF), demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 h. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodology, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring. |
| format | Article |
| id | doaj-art-950afbc1e6734bea8efaa4c3075283f8 |
| institution | OA Journals |
| issn | 0362-028X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Food Protection |
| spelling | doaj-art-950afbc1e6734bea8efaa4c3075283f82025-08-20T02:19:51ZengElsevierJournal of Food Protection0362-028X2024-12-01871210039610.1016/j.jfp.2024.100396Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence SpectroscopyNicharee Wisuthiphaet0Huanle Zhang1Xin Liu2Nitin Nitin3Department of Biotechnology, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandSchool of Computer Science and Technology, Shandong University, Shandong, ChinaDepartment of Computer Science, University of California, Davis, Davis, California, United StatesDepartment of Food Science & Technology, University of California, Davis, Davis, California, United States; Department of Biological & Agricultural Engineering, University of California, Davis, Davis, California, United States; Corresponding author at: Department of Food Science & Technology, University of California, Davis, Davis, California, United States.Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and Escherichia coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier (SVC) and Random Forest (RF), demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 h. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodology, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.http://www.sciencedirect.com/science/article/pii/S0362028X24001807BacteriophageE. coli detectionExcitation-emission matrix fluorescence spectroscopyMachine learningT7 Phage |
| spellingShingle | Nicharee Wisuthiphaet Huanle Zhang Xin Liu Nitin Nitin Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy Journal of Food Protection Bacteriophage E. coli detection Excitation-emission matrix fluorescence spectroscopy Machine learning T7 Phage |
| title | Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy |
| title_full | Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy |
| title_fullStr | Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy |
| title_full_unstemmed | Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy |
| title_short | Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy |
| title_sort | detection of escherichia coli using bacteriophage t7 and analysis of excitation emission matrix fluorescence spectroscopy |
| topic | Bacteriophage E. coli detection Excitation-emission matrix fluorescence spectroscopy Machine learning T7 Phage |
| url | http://www.sciencedirect.com/science/article/pii/S0362028X24001807 |
| work_keys_str_mv | AT nichareewisuthiphaet detectionofescherichiacoliusingbacteriophaget7andanalysisofexcitationemissionmatrixfluorescencespectroscopy AT huanlezhang detectionofescherichiacoliusingbacteriophaget7andanalysisofexcitationemissionmatrixfluorescencespectroscopy AT xinliu detectionofescherichiacoliusingbacteriophaget7andanalysisofexcitationemissionmatrixfluorescencespectroscopy AT nitinnitin detectionofescherichiacoliusingbacteriophaget7andanalysisofexcitationemissionmatrixfluorescencespectroscopy |