Journal article
Preparation-free method for detecting Escherichia coli O157:H7 in the presence of spinach, spring lettuce mix, and ground beef particulates
Journal of food protection, v 70(11), pp 2651-2655
Nov 2007
PMID: 18044451
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We show the detection of 100 cells per ml of Escherichia coli O157:H7 in the presence of spinach, spring lettuce mix, and ground beef washes and particulate matter with piezoelectric-excited millimeter-sized cantilever (PEMC) sensors. The PEMC sensors (sensing area, 2 mm2) were immobilized with polyclonal antibody specific to E. coli O157:H7 (EC) and were exposed to 10 aqueous washes of locally purchased spinach, spring lettuce mix, and ground beef for testing if EC was present. Absence of resonance frequency shift indicated that EC was not present in the 30 samples tested. Following the last sample in each food matrix, 1,000 cells per ml of EC were spiked into the sample container, and resonance frequency change was monitored. The total resonance frequency change was 880 +/- 5, 1,875 +/- 8, and 1,417 +/- 4 Hz for spinach, spring lettuce mix, and ground beef, respectively. A mixture of the three food matrices spiked with 100 cells per ml of EC gave a sensor response of 260 +/- 15 Hz. The resonance frequency changes are approximately 40% lower than our previously reported study on ground beef. It is suggested that the reduction in sensitivity is due to differences in pathogen adherence to food matrices, which affects target binding to the sensor surface. We conclude that detection selectivity is conserved in the three food matrices examined and that the magnitude of sensor response is a function of the food matrix.
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Details
- Title
- Preparation-free method for detecting Escherichia coli O157:H7 in the presence of spinach, spring lettuce mix, and ground beef particulates
- Creators
- David Maraldo - Drexel UniversityRaj Mutharasan - Drexel University
- Publication Details
- Journal of food protection, v 70(11), pp 2651-2655
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000250744100031
- Scopus ID
- 2-s2.0-36048950726
- Other Identifier
- 991019170974704721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biotechnology & Applied Microbiology
- Food Science & Technology