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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. 

Microbial foodborne pathogens present significant challenges to public health and the food industry, requiring rapid and accurate detection methods to prevent infections and ensure food safety. Conventional single biosensing techniques often exhibit limitations in terms of sensitivity, specificity, and rapidity. In response, there has been a growing interest in multimodal biosensing approaches that combine multiple sensing techniques to enhance the efficacy, accuracy, and precision in detecting these pathogens. This review investigates the current state of multimodal biosensing technologies and their potential applications within the food industry. Various multimodal biosensing platforms, such as opto-electrochemical, optical nanomaterial, multiple nanomaterial-based systems, hybrid biosensing microfluidics, and microfabrication techniques are discussed. The review provides an in-depth analysis of the advantages, challenges, and future prospects of multimodal biosensing for foodborne pathogens, emphasizing its transformative potential for food safety and public health. This comprehensive analysis aims to contribute to the development of innovative strategies for combating foodborne infections and ensuring the reliability of the global food supply chain.