Abstract
Optical trapping provides a way to isolate, manipulate, and probe a wide range of microscopic particles. Moreover, as particle dynamics are strongly affected by their shape and composition, optical tweezers can also be used to identify and classify particles, paving the way for multiple applications such as intelligent microfluidic devices for personalized medicine purposes, or integrated sensing for bioengineering. In this work, we explore the possibility of using properties of the forward scattered radiation of the optical trapping beam to analyze properties of the trapped specimen and deploy an autonomous classification algorithm. For this purpose, we process the signal in the Fourier domain and apply a dimensionality reduction technique using UMAP algorithms, before using the reduced number of features to feed standard machine learning algorithms such as K-nearest neighbors or random forests. Using a stratified 5-fold cross-validation procedure, our results show that the implemented classification strategy allows the identification of particle material with accuracies up to 80%, demonstrating the potential of using signal processing techniques to probe properties of optical trapped particles based on the forward scattered light. Furthermore, preliminary results of an autonomous implementation in a standard experimental optical tweezers setup show similar differentiation capabilities for real-time applications, thus opening some opportunities towards technological applications such as intelligent microfluidic devices and solutions for biochemical and biophysical sensing. © Published under licence by IOP Publishing Ltd.

Abstract
To automatically trap, manipulate and probe physical properties of micron-sized particles is a step of paramount importance for the development of intelligent and integrated optomicrofluidic devices. In this work, we aim at implementing an automatic classifier of micro-particles immersed in a fluid based on the concept of optical tweezers. We describe the automation steps of an experimental setup together with the implemented classification models using the forward scattered signal. The results show satisfactory accuracy around 80% for the identification of the type and size of particles using signals of 250 milliseconds of duration, which paves the path for future improvements towards real-time analysis of the trapped specimens.

Abstract
Optical trapping is a versatile and non-invasive technique for single particle manipulation. As such, it can be widely applied in the domains of particle identification and classification and thus used as a tool for monitoring physical and chemical processes. This creates an opportunity for integrating the method seamlessly into optofluidic chips, provided it can be automatized. Yet even though OT is well established in multiple scientific domains, a full stack approach to its integration into other technological devices is still lacking. This calls for solutions in tasks such as automatic trapping and signal analysis. In this manuscript, we describe the implementation of an algorithm seeking autonomous particle location and trapping. The methodology is based upon image-processing, allowing for particle location using real time image segmentation. A local thresholding algorithm is applied, followed by morphological techniques for closing shapes and excluding non-bounded regions - after which only the particles remain on the image. Once the centroid is identified, the stage is translated accordingly by piezo-electric actuators, followed by the laser activation. In this way, trapping is achieved, and one may proceed to analyze the forward scattered optical signal, after which a new particle inside the actuators range may be automatically trapped. This development, when compared with existent solutions involving holographic optical tweezers, allows for similar capabilities without using a spatial light modulator, thus dramatically reducing the setup costs of autonomous OT solutions. Therefore, when combined with particle classification techniques, this method is well suited for integration into possible optofluidic chips for autonomous sensing and monitoring of biochemical samples. © Published under licence by IOP Publishing Ltd.

Abstract
Analytical grade 'in vivo' plant metabolic quantification using spectroscopy is a key enabling technology for precision agriculture.Current methods such as PLS, ANN and LS-SVM are non-optimal for resolving spectral interference and matrix effects to provide similar results to the analytical chemistry laboratory. This research presents a new self-learning artificial intelligence (SL-AI) method based on the search of covariance modes. These isolate the different modes of interference present in spectral data, allowing the consistent quantification of constituents. A review of the state-of-the-art methods with the figures of merit mean absolute standard error percentage (MASEP) and Pearson correlation coefficient (R) is presented for comparison and discussion. 707 grapes were quantified for glucose, fructose, malic and tartaric acids in five wine-making and one table grape varieties, and used to benchmark the new method against the state-of-the-art methodologies: partial least squares, local partial least squares, artificial neural networks and least squares support vector machines. SL-AI provides consistent quantifications, whereas previous methods exhibit data-driven performance dependence. Pearson correlations of 0.93 to 0.99 and MASEP of 3.70% to 7.33% were obtained with the new methodology. Local partial least squares, the method with the best benchmarks from literature, achieved correlations of 0.81 to 0.94 and MASEP of 8.00% to 13.4%. The covariance mode isolates a particular interference, providing a direct relationship between spectral inference and constituent concentrations, consistent with the Beer-Lambert law. Such quantifies non-dominant absorbance constituents (e.g. sugars and acids), which is a significant step towards 'in vivo' plant physiology-based precision agriculture.

Abstract
Biogenic amines (BAs) are compounds found in a vast range of food products. In recent years, there has been a crescent awareness toward food safety, followed by an increase in food regulations. Long-period fiber gratings (LPFGs) coated with titanium dioxide (TiO2) were used to monitor the optical properties of a layer of poly(ethylene-co-vinyl acetate) (PEVA) doped with maleic anhydride (MA), which was polymerized on top of TiO2. This hydrophobic polymeric structure is permeable to BA, which causes a steady increase in its effective refractive index (RI) causing a wavelength shift in the coated LPFG attenuation band. LPFG wavelength shift was observed and measured for the monoamine tyramine (TYR), to the diamines, putrescine (PUT), cadaverine (CAD), histamine (HIS), and tryptamine (TRYP), and to the polyamines, spermidine (SPED), and spermine (SPEM). It was determined that, while PEVA-coated devices present a residual sensitivity to BA, the MA greatly increases it. In fact, for PEVA only coated LPFGs, the sensitivities of 1.45 +/- 0.11, 0.97 +/- 0.05, 0.46 +/- 0.08, and 0.94 +/- 0.09 nmM-1 for PUT, CAD, HIS, and TYR, respectively, were measured. However, for PEVA-doped MA-coated LPFGs, the sensitivities are 3.34 +/- 0.13, 3.06 +/- 0.11, 2.62 +/- 0.14, and 3.65 +/- 0.23 nmM-1 for PUT, CAD, HIS, and TYR, respectively. Thus, the RI of PEVA increases with BAs in- diffusion, and MA doping further enhances the PEVA sensitivity to BA. The proposed sensor is expected to play a part in the further development of a biosensor for the quantification of BA in real foodstuff, providing a methodology for quality control.

Abstract
In this paper, we study the implementation of a secure key distribution system based on an ultra-long fiber laser with a bi-directional erbium-doped fiber amplifier. The resilience of the system was tested against passive attacks from an eavesdropper. A similarity was observed in the spectra for both secure configurations of the system and no signature that would allow an eavesdropper to obtain the secure state of the system was observed during the state transitions.