The measurement of the analyte concentration in electrochemical biosensors traditionally requires costly laboratory equipment to obtain accurate results. Innovative portable solutions have recently proposed, but usually they lean on PC or smartphones for data elaboration and they exhibit poor resolution or portability and proprietary software. This paper presents a low-cost portable system, assembling an ad-hoc designed analog front-end and a development board equipped with a system-on-chip integrating a microcontroller and a Wi-Fi network processor. The wireless module enables the transmission of measurements directly to a cloud service for sharing device outcome with users (physicians, caregivers, etc.). In doing so, the system does not require customized software nor other devices involved in data acquisition. Further, when any internet connection is lost, the data are stored on board for subsequent transmission when a Wi-Fi connection is available. The noise output voltage spectrum has been characterized. Since the designed device is intended to be battery-powered to enhance portability, investigations about battery lifetime were carried out. Finally, data acquired with a conventional benchtop Autolab PGSTAT-204 electrochemical workstation are compared with the outcome of our developed device to validate the effectiveness of our proposal. To this end, we selected ferri/ferrocyanide as redox probe, obtaining the calibration curves for both platforms. The final outcomes are shown to be feasible, accurate and repeatable.

Recent advances in Internet-of-Things technology have opened the doors to new scenarios for biosensor applications. Flexibility, portability, and remote control and access are of utmost importance to move these devices to people’s homes or in a Point-of-Care context and rapidly share the results with users and their physicians. In this paper, an innovative portable device for both quantitative and semi-quantitative electrochemical analysis is presented. This device can operate autonomously without the need of relying on other devices (e.g., PC, tablets, or smartphones) thanks to built-in Wi-Fi connectivity. The developed hardware is integrated into a cloud-based platform, exploiting the cloud computational power to perform innovative algorithms for calibration (e.g., Machine Learning tools). Results and configurations can be accessed through a web page without the installation of dedicated APPs or software. The electrical input/output characteristic was measured with a dummy cell as a load, achieving excellent linearity. Furthermore, the device response to five different concentrations of potassium ferri/ferrocyanide redox probe was compared with a bench-top laboratory instrument. No difference in analytical sensitivity was found. Also, some examples of application-specific tests were set up to demonstrate the use in real-case scenarios. In addition, Support Vector Machine algorithm was applied to semi-quantitative analyses to classify the input samples into four classes, achieving an average accuracy of 98.23%. Finally, COVID-19 related tests are presented and discussed.

Point‐of‐care technologies need particularly stringent requirements, including portability, rapidity, affordability, and ease of use, enabling healthcare practitioners to perform reliable analyses in decentralized settings. Such diagnostic tests are usually based on bioassays that allow for the detection of clinically relevant biomarkers, namely proteins, antibodies and nucleic acids. In the context of electrochemical biosensors, the implementation of magnetic beads offers unique advantages stemming from their high surface‐to‐volume ratio, easy manipulation through magnetic fields and versatility in surface chemistries for bio‐conjugation. The present concept article highlights the advantages achieved through the implementation of magnetic beads in electrochemical binding assays, immunosensors and genosensors. These methods mostly exploit magnetic beads for immobilization purposes, allowing for the assembly of bio‐complexes in liquid suspensions under precise temperature and shaking control, as well as simplifying the manipulation steps to enable untrained personnel to perform analyses. In addition, magnetic beads can also take part in the detection mechanism, leading to innovative sensing strategies with great potential for point‐of‐care diagnostic applications.

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.

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.