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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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This content will become publicly available on November 1, 2026
Portable AI-driven electrochemical aptasensor for real-time Staphylococcus aureus detection in food and beverages
A portable electrochemical aptasensor integrated with machine learning was developed for rapid and on-site detection of Staphylococcus aureus (S. aureus) in food and beverage samples. The aptasensor was fabricated using screen-printed carbon electrodes (SPCEs) modified with gold nanoparticles (AuNPs) and functionalized with an Iron-regulated Surface Determinant Protein A (IsdA)-specific aptamer for the detection of S. aureus. Approximately 2,000 cyclic voltammetry (CV) data points were collected for six different food and beverage matrices spiked with varying concentrations of S. aureus (1, 10, 500, and 1000 colony-forming unit (CFU)/mL). Each CV scan was repeated 10 times, linearly averaged, and baseline corrected before model input. Noise filtering and normalization were performed to ensure consistent feature representation across training and testing datasets. Machine learning models, including Convolutional Neural Networks (CNNs) and Transformer architectures, were applied to classify the samples. The CNN model demonstrated superior performance, with a test loss of 0.0402 and a test accuracy of 99.21%. In contrast, the Transformer model achieved a test loss of 0.2014 and an accuracy of 94.21%. To enhance usability, an Android application was developed using the Network Enabled Technologies (NET) framework, enabling real-time inference of bacterial concentration directly from CV data on mobile devices (e.g. smartphones). This system demonstrates potential for a rapid, accurate, and scalable solution for real-world food safety monitoring.
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- Award ID(s):
- 2130643
- PAR ID:
- 10657475
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Microchimica Acta
- Volume:
- 192
- Issue:
- 11
- ISSN:
- 0026-3672
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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