Literature DB >> 21840701

Direct and sensitive detection of foodborne pathogens within fresh produce samples using a field-deployable handheld device.

David J You1, Kenneth J Geshell, Jeong-Yeol Yoon.   

Abstract

Direct and sensitive detection of foodborne pathogens from fresh produce samples was accomplished using a handheld lab-on-a-chip device, requiring little to no sample processing and enrichment steps for a near-real-time detection and truly field-deployable device. The detection of Escherichia coli K12 and O157:H7 in iceberg lettuce was achieved utilizing optimized Mie light scatter parameters with a latex particle immunoagglutination assay. The system exhibited good sensitivity, with a limit of detection of 10 CFU mL(-1) and an assay time of <6 min. Minimal pretreatment with no detrimental effects on assay sensitivity and reproducibility was accomplished with a simple and cost-effective KimWipes filter and disposable syringe. Mie simulations were used to determine the optimal parameters (particle size d, wavelength λ, and scatter angle θ) for the assay that maximize light scatter intensity of agglutinated latex microparticles and minimize light scatter intensity of the tissue fragments of iceberg lettuce, which were experimentally validated. This introduces a powerful method for detecting foodborne pathogens in fresh produce and other potential sample matrices. The integration of a multi-channel microfluidic chip allowed for differential detection of the agglutinated particles in the presence of the antigen, revealing a true field-deployable detection system with decreased assay time and improved robustness over comparable benchtop systems. Additionally, two sample preparation methods were evaluated through simulated field studies based on overall sensitivity, protocol complexity, and assay time. Preparation of the plant tissue sample by grinding resulted in a two-fold improvement in scatter intensity over washing, accompanied with a significant increase in assay time: ∼5 min (grinding) versus ∼1 min (washing). Specificity studies demonstrated binding of E. coli O157:H7 EDL933 to only O157:H7 antibody conjugated particles, with no cross-reactivity to K12. This suggests the adaptability of the system for use with a wide variety of pathogens, and the potential to detect in a variety of biological matrices with little to no sample pretreatment.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21840701     DOI: 10.1016/j.bios.2011.07.055

Source DB:  PubMed          Journal:  Biosens Bioelectron        ISSN: 0956-5663            Impact factor:   10.618


  7 in total

1.  In situ, dual-mode monitoring of organ-on-a-chip with smartphone-based fluorescence microscope.

Authors:  Soohee Cho; Argel Islas-Robles; Ariana M Nicolini; Terrence J Monks; Jeong-Yeol Yoon
Journal:  Biosens Bioelectron       Date:  2016-07-07       Impact factor: 10.618

2.  Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay.

Authors:  Colin J Potter; Yanmei Hu; Zhen Xiong; Jun Wang; Euan McLeod
Journal:  Lab Chip       Date:  2022-09-27       Impact factor: 7.517

3.  Integrated, DC voltage-driven nucleic acid diagnostic platform for real sample analysis: Detection of oral cancer.

Authors:  Zdenek Slouka; Satyajyoti Senapati; Sunny Shah; Robin Lawler; Zonggao Shi; M Sharon Stack; Hsueh-Chia Chang
Journal:  Talanta       Date:  2015-05-06       Impact factor: 6.057

4.  High-Speed Lens-Free Holographic Sensing of Protein Molecules Using Quantitative Agglutination Assays.

Authors:  Zhen Xiong; Colin J Potter; Euan McLeod
Journal:  ACS Sens       Date:  2021-02-15       Impact factor: 7.711

Review 5.  Lab-on-a-chip pathogen sensors for food safety.

Authors:  Jeong-Yeol Yoon; Bumsang Kim
Journal:  Sensors (Basel)       Date:  2012-08-06       Impact factor: 3.576

6.  Rapid and reagentless detection of microbial contamination within meat utilizing a smartphone-based biosensor.

Authors:  Pei-Shih Liang; Tu San Park; Jeong-Yeol Yoon
Journal:  Sci Rep       Date:  2014-08-05       Impact factor: 4.379

7.  Low-Cost 3D Printers Enable High-Quality and Automated Sample Preparation and Molecular Detection.

Authors:  Kamfai Chan; Mauricio Coen; Justin Hardick; Charlotte A Gaydos; Kah-Yat Wong; Clayton Smith; Scott A Wilson; Siva Praneeth Vayugundla; Season Wong
Journal:  PLoS One       Date:  2016-06-30       Impact factor: 3.240

  7 in total

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