The standard rapid approach for the diagnosis of coronavirus disease 2019 (COVID-19) is the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA. The detection of specific anti-SARS-CoV-2 immunoglobulins is crucial for screening people who have been exposed to the virus, whether or not they presented symptoms. Recent publications report different methods for the detection of specific IgGs, IgMs, and IgAs against SARS-CoV-2; these methods mainly detect immunoglobulins in the serum using conventional techniques such as rapid lateral flow tests or enzyme-linked immunosorbent assay (ELISA). In this article, we report the production of recombinant SARS-CoV-2 spike protein and the development of a rapid, reliable, cost-effective test, capable of detecting immunoglobulins in serum and saliva samples. This method is based on interferometric optical detection. The results obtained using this method and those obtained using ELISA were compared. Owing to its low cost and simplicity, this test can be used periodically for the early detection, surveillance, detection of immunity, and control of the spread of COVID-19.

In this work, we review the technology of vertically interrogated optical biosensors from the point of view of engineering. Vertical sensors present several advantages in the fabrication processes and in the light coupling systems, compared with other interferometric sensors. Four different interrelated aspects of the design are identified and described: sensing cell design, optical techniques used in the interrogation, fabrication processes, fluidics, and biofunctionalization of the sensing surface. The designer of a vertical sensor should decide carefully which solution to adopt on each aspect to finally integrating all the components in a single platform. Complexity, cost, and reliability of this platform will be determined by the decisions taken on each of the design process. We focus in the research and experience acquired by our group during last years on the field of optical biosensors.

Apple is one of the most produced fruit crops in the world. Recent advances in Artificial Intelligence and the Internet of Things can reduce production costs and improve crop quality by providing prompt detection of dangerous parasites. This paper presents an effective solution to automate the detection of the Codling Moths. The system takes pictures of trapped insects in the orchard, analyzes them through a DNN algorithm, and sends alarms to the farmer in case of a positive detection. The system is fully autonomous and can operate unattended for the entire crop season. Detection reports are used for optimizing the treatment with chemicals only when threats are identified. The prototype is designed with an embedded platform powered by a small solar panel to achieve an energy-neutral balance.