Abstract
The classification of the type of trapped particles is a crucial task for an efficient integration of optical-tweezers in intelligent microfluidic devices. In the recent years, the use of the temporal scattering signal of the trapped particle paved for the use of versatile optical fiber solutions for performing such tasks, a feature previously unavailable as most methods required conventional optical tweezer setups. This work presents a comprehensive comparison of performances achieved with two distinct implementations – i)optical fiber and ii)conventional optical tweezers – for the classification of the material of particles through the analysis of the scattering signal with machine learning algorithms. The results suggest that while micron-sized particles can be accurately classified using the forward scattering information in conventional optical tweezers, when equipped with a quadrant photodetector, the optical fiber tweezers solutions can easily surpass its performance using the back-scattered information if the laser is modulated. Together with the advantages of being simpler, less expensive and more versatile, the results presented suggest that optical fiber solutions can become a valuable tool for miniaturization and integration of intelligent microfluidic devices working towards nanoscopic scales. © 2021 The Authors

Abstract
This work introduces a method specially developed to produce a biorecognition element based on modified Stöber silica nanoparticles by the covalent immobilization of the human IgG. The sensing structure is based on long period fiber gratings (LPFG), specially developed to allow the interaction of the electromagnetic wave with the target analytes through its evanescent field. The surface was modified by the immobilization of the IgG-modified nanoparticles serving has recognition elements for specific target molecules. The resulting configuration was tested in the presence of anti-human IgG, recording the refractometric response of the modified LPFG in contact with different amounts of analyte. The selectivity of the sensor was also assessed.

Abstract
Over the years, the food industry’s concern to provide safe food that does not cause harm or illness to consumers has increased. The growing demand for the detection of compounds that can contaminate food is increasingly important. Hydrogen peroxide is frequently used as a substance to control the growth of microorganisms in milk, thus increasing its shelf life. Here, a strategy is presented for the detection of hydrogen peroxide as a milk adulterant, using a single shot membrane sensor. The lowest concentration measured with this technique was 0.002% w/w of H2O2 in semi-fat milk.

Abstract
In hydroponic cultivation, monitoring and quantification of nutrients is of paramount importance. Precision agriculture has an urgent need for measuring fertilization and plant nutrient uptake. Reliable, robust and accurate sensors for measuring nitrogen (N), phosphorus (P) and potassium (K) are regarded as critical in this process. It is vital to understand nutrients’ interference; thusly, a Hoagland fertilizer solution-based orthogonal experimental design was deployed. Concentration ranges were varied in a target analyte-independent style, as follows: [N] = [103.17–554.85] ppm; [P] = [15.06–515.35] ppm; [K] = [113.78–516.45] ppm, by dilution from individual stock solutions. Quantitative results for N and K, and qualitative results for P were obtained.

Abstract
This work addresses the use of hyperspectral imaging systems for remote detection of marine litter concentrations in oceanic environments. The work consisted on mounting an off-the-shelf hyperspectral imaging system (400-2500 nm) in two aerial platforms: manned and unmanned, and performing data acquisition to develop AI methods capable of detecting marine litter concentrations at the water surface. We performed the campaigns at Porto Pim Bay, Fail Island, Azores, resorting to artificial targets built using marine litter samples. During this work, we also developed a Convolutional Neural Network (CNN-3D), using spatial and spectral information to evaluate deep learning methods to detect marine litter in an automated manner. Results show over 84% overall accuracy (OA) in the detection and classification of the different types of marine litter samples present in the artificial targets.

Abstract