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Biogenic amines (BAs) are well-known biomolecules, mostly for their toxic and carcinogenic effects. Commonly, they are used as an indicator of quality preservation in food and beverages since their presence in higher concentrations is associated with poor quality. With respect to BA's metabolic pathways, time plays a crucial factor in their formation. They are mainly formed by microbial decarboxylation of amino acids, which is closely related to food deterioration, therefore, making them unfit for human consumption. Pathogenic microorganisms grow in food without any noticeable change in odor, appearance, or taste, thus, they can reach toxic concentrations. The present review provides an overview of the most recent literature on BAs with special emphasis on food matrixes, including a description of the typical BA assay formats, along with its general structure, according to the biorecognition elements used (enzymes, nucleic acids, whole cells, and antibodies). The extensive and significant amount of research that has been done to the investigation of biorecognition elements, transducers, and their integration in biosensors, over the years has been reviewed.

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The popularity of deep learning methods has increased significantly, in no small part due to their impressive performance in several application scenarios. This paper focuses on recognising activities in an in-vehicle environment and measuring the impact that factors such as resolution, aspect ratio, field of view and framerate have on the performance of the model. The use of deep learning methodologies in recent years has increased the amount of data required to train and test the models. However, such data is often insufficient, unavailable, or lacks suitable properties. Publicly available action recognition datasets have been analysed, collected, and prepared to assess the classification results in such scenarios, which provides important guidance for use in a real-world setting. © 2021 IEEE.

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