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Bacterial contamination in food-processing facilities is a critical issue that leads to outbreaks compromising the integrity of the food supply and public health. We developed a label-free and rapid electrochemical biosensor for Listeria monocytogenes detection using a new one-step simultaneous sonoelectrodeposition of platinum and chitosan (CHI/Pt) to create a biomimetic nanostructure that actuates under pH changes. The XPS analysis shows the effective co-deposition of chitosan and platinum on the electrode surface. This deposition was optimized to enhance the electroactive surface area by 11 times compared with a bare platinum–iridium electrode (p < 0.05). Electrochemical behavior during chitosan actuation (pH-stimulated osmotic swelling) was characterized with three different redox probes (positive, neutral, and negative charge) above and below the isoelectric point of chitosan. These results showed that using a negatively charged redox probe led to the highest electroactive surface area, corroborating previous studies of stimulus–response polymers on metal electrodes. Following this material characterization, CHI/Pt brushes were functionalized with aptamers selective for L. monocytogenes capture. These aptasensors were functional at concentrations up to 106 CFU/mL with no preconcentration nor extraneous reagent addition. Selectivity was assessed in the presence of other Gram-positive bacteria (Staphylococcus aureus) and with a food product (chicken broth). Actuation led to improved L. monocytogenes detection with a low limit of detection (33 CFU/10 mL in chicken broth). The aptasensor developed herein offers a simple fabrication procedure with only one-step deposition followed by functionalization and rapid L. monocytogenes detection, with 15 min bacteria capture and 2 min sensing.
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Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning
Abstract Wastewater-based epidemiology (WBE) is a powerful tool for monitoring community disease occurrence, but current methods for bacterial detection suffer from limited scalability, the need fora prioriknowledge of the target organism, and the high degree of genetic similarity between different strains of the same species. Here, we show that surface-enhanced Raman spectroscopy (SERS) can be a scalable, label-free method for detection of bacteria in wastewater. We preferentially enhance Raman signal from bacteria in wastewater using positively-charged plasmonic gold nanorods (AuNRs) that electrostatically bind to the bacterial surface. Transmission cryoelectron microscopy (cryoEM) confirms that AuNRs bind selectively to bacteria in this wastewater matrix. We spike the bacterial speciesStaphylococcus epidermidis, Staphylococcus aureus, Serratia marcescens, andEscerichia coliand AuNRs into filter-sterilized wastewater, varying the AuNR concentration to achieve maximum signal across all pathogens. We then collect 540 spectra from each species, and train a machine learning (ML) model to identify bacterial species in wastewater. For bacterial concentrations of 109cells/mL, we achieve an accuracy exceeding 85%. We also demonstrate that this system is effective at environmentally-realistic bacterial concentrations, with a limit of bacterial detection of 104cells/mL. These results are a key first step toward a label-free, high-throughput platform for bacterial WBE.
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- Award ID(s):
- 2233672
- PAR ID:
- 10541886
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Posted Content:
- https://doi.org/10.1101/2024.07.22.604506
