Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection.

Automated summary

A smart and portable electrochemical immunosensor with IoT-WiFi capability was developed for quantifying the SARS-CoV-2 spike protein, and machine learning features were integrated. The immunoenzymatic sensor utilizes monoclonal antibodies that are immobilized on gold nanoparticles functionalized screen-printed electrodes. The sensor exhibited remarkable specificity and was validated using a viral transfer medium commonly used for nasopharyngeal swab desorption. Machine learning algorithms were employed for data processing and analysis, with different support vector machine classifiers evaluated for their accuracy. The best classification model achieved a test accuracy of 97.3% for true positive/true negative sample classifications. Furthermore, the ML algorithm can be easily integrated into cloud-based portable WiFi devices. The immunosensor was successfully tested for whole virus detection using a replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein.