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.

Innovative and highly performing smart voltammetric immunosensors for rapid and effective serological tests aimed at the determination of SARS-CoV-2 antibodies were developed and validated in human serum matrix. Two immunosensors were developed for the determination of immunoglobulins directed against either the nucleocapsid or the spike viral antigen proteins. The immunosensors were realized using disposable screen-printed electrodes modified with nanostructured materials for the immobilization of the antigens. Fast quantitative detection was achieved, with analysis duration being around 1 h. Signal readout was carried out through a smart, compact and battery-powered potentiostat, based on a Wi-Fi protocol and devised for the Internet of Things (IoT) paradigm. This device is used for the acquisition, storage and sharing of clinical data. Outstanding immunosensors’ sensitivity, specificity and accuracy (100%) were assessed, according to the diagnostic guidelines for epidemiological data. The overall performance of the sensing devices, combined with the portability of the IoT-based device, enables their suitability as a high-throughput diagnostic tool. Both of the immunosensors were validated using clinical human serum specimens from SARS-CoV-2 infected patients, provided by IRCCS Ospedale San Raffaele.

Vitamin C, an antioxidant in most fruits and vegetables, is highly sensitive to heat, pH, metals, light, and oxidation, making it a key marker for nutrient degradation in thermal processing. Research aimed at improving processing methods to maximize vitamin C retention is usually limited to expensive laboratory equipment, which does not reflect real-world conditions in the food industry. On the other hand, traditional methods are not suitable for on-line monitoring. This paper proposes bridging the gap in liquid food processing with a voltammetric sensor based on poly(3,4-ethylenedioxythiophene)-modified screen-printed carbon electrodes. The sensor showed excellent repeatability, with intra-sensor RSD below 5% and inter-sensor RSD below 10% at 250 mg/L of ascorbic acid. Detection and quantification limits were 0.7 and 2.1 mg/L, respectively. Trueness assessment in commercial orange juice with a declared vitamin C content yielded a recovery rate of 94 ± 1%. Selectivity tests with citric acid at concentrations equal to and 20 times higher than that of ascorbic acid showed no significant interference. Shelf-life studies confirmed the stability of the sensor for at least two months. This nanocomposite-based approach balances performance and cost with simple preparation, affordable materials, and a stable coating that allows long-term storage in uncontrolled environments.