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Bacteria detection in real samples often involves long and tedious methodologies such as culture enrichment, biochemical screening, and serological confirmation. In this context, the development of biosensors and quick assays for bacteria detection appears as fast growing fields. However, a detailed study of reports in these areas reveals the existence of important differences in bacteria storage, handling, and detection conditions, indicating that authors do not take advantage of the well-established procedures existing for classical techniques such as enzyme-linked immunosorbent assay (ELISA). In the current work, we exploit standard ELISA methodology to identify and study diverse parameters that can be critical along the different steps of bacteria detection and sensing. Among others, we studied in detail the effect of the bacterial strain used and the presence of detergent and glycerol in assay performance, as well as the effects of heat inactivation or storing conditions, on bacteria integrity and thus detectability. Finally, we describe the use of "ready-to-use" frozen bacterial pellets as an excellent alternative to the use of daily prepared fresh cultures during assay optimization and preparation of calibration standards. The results presented are also supported by an extensive bibliography search, giving shape to an important compilation of information that will be useful to authors working in a variety of methodologies and sensing formats.
Bacteria, Biosensor development, Escherichia coli, Enzyme-Linked Immunosorbent Assay, ELISA assay, Biosensing Techniques, Bacteria detection
Bacteria, Biosensor development, Escherichia coli, Enzyme-Linked Immunosorbent Assay, ELISA assay, Biosensing Techniques, Bacteria detection
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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