Abstract
Process Analytical Technology (PAT) has been increasingly used in the pharmaceutical industry to monitor essential parameters in real-time during pharmaceutical processes. The concentration of Active Pharmaceutical Ingredients (APIs), such as paracetamol, is one of these parameters, and controlling its variations allows for optimization of the production process. In this study, a refractometric sensor, implemented by an interrogation system based on an Erbium-Doped Fiber Ring Cavity (EDFRC), was presented and experimentally demonstrated. The Cavity Ring proposed included a 1 x 3 coupler. One port of the coupler was used to increase the optical power of the system through a Fiber Bragg Grating (FBG), and the other two ports were used as sensing head and reference. The sensor detected variations of paracetamol concentration with a sensitivity of [(-1.00 +/- 0.05) x 10(-3)] nW/(g/kg) and a resolution of 5.53 g/kg. The results demonstrate the potential of this technology as a possible non-invasive PAT tool.

Abstract
Imaging the spatial distribution of chemical elements at a sample surface is a common application of laserinduced breakdown spectroscopy with vast scientific and technological applications. Yet, typical imaging solutions only explore the creation of two-dimensional maps, which can limit the interpretability of the results and further diagnostics in three-dimensional settings. Within this context, this work explores the combination of spectral imaging techniques and photogrammetry to deploy a versatile solution for the creation of threedimensional spectral imaging models. First, by making use of a numerical algorithm that is able to match features in the spectral image with those of the three-dimensional model, we show how to match the mesh from distinct sensor modalities. Then, we describe a possible visualization workflow, making use of dedicated photogrammetry and visualization software to easily deploy interactive models. Overall, the results demonstrate the versatility of our approach and pave for the development of novel spectral imaging diagnostic strategies that are able to deliver better qualitative analysis and insight in the three-dimensional space.

Abstract
Water vapor sorption is a powerful tool for the analysis of cement paste, one of the most used substances by mankind. The monitoring of cementitious materials is fundamental for the improvement of infrastructure resilience, which has a deep impact on the economy, the environment, and on society. In this work, a multimode fiber was embedded in cement paste for real-time monitoring of cement paste water vapor sorption. Changes in the reflected light intensity due to the build-up of water in the cement paste's pores were exploited for this purpose. The sample was 7-day moist cured, and the relative humidity was controlled between 8.9% and 97.6%. Reflected light intensity was converted into a specific surface area of cement paste (133 m(2)/g) and thickness of water through the Brunauer-Emmett-Teller (BET) method and into a pore size distribution through the Barret-Joyner-Halenda (BJH) method. The results achieved through reflected light intensity agree with those found in the literature, validating the usage of this setup for the monitoring of water vapor sorption, breaking away from standard gravimetric measurements.

Abstract
Biochemical-chemical sensing with plasmonic sensors is widely performed by tracking the responses of surface plasmonic resonance peaks to changes in the medium. Interestingly, consistent sensitivity and resolution improvements have been demonstrated for gold nanoparticles by analyzing other spectral features, such as spectral inflection points or peak curvatures. Nevertheless, such studies were only conducted on planar platforms and were restricted to gold nanoparticles. In this work, such methodologies are explored and expanded to plasmonic optical fibers. Thus, we study-experimentally and theoretically-the optical responses of optical fiber-doped gold or silver nanospheres and optical fibers coated with continuous gold or silver thin films. Both experimental and numerical results are analyzed with differentiation methods, using total variation regularization to effectively minimize noise amplification propagation. Consistent resolution improvements of up to 2.2x for both types of plasmonic fibers are found, demonstrating that deploying such analysis with any plasmonic optical fiber sensors can lead to sensing resolution improvements.

Abstract
Extreme Learning Machines (ELMs) are a versatile Machine Learning (ML) algorithm that features as the main advantage the possibility of a seamless implementation with physical systems. Yet, despite the success of the physical implementations of ELMs, there is still a lack of fundamental understanding in regard to their optical implementations. In this context, this work makes use of an optical complex media and wavefront shaping techniques to implement a versatile optical ELM playground to gain a deeper insight into these machines. In particular, we present experimental evidences on the correlation between the effective dimensionality of the hidden space and its generalization capability, thus bringing the inner workings of optical ELMs under a new light and opening paths toward future technological implementations of similar principles.

Abstract
Laser-induced breakdown spectroscopy is a spectroscopic technique that allows for fast elemental mapping of heterogeneous samples. Yet, detailed maps need high-resolution sampling grids, which can turn the task into a time-consuming process and can increase sample damage. In this work, we present the implementation of an imaged-based intelligent mesh algorithm that makes use of superpixel segmentation to optimize elemental mapping processes. Our results show that the approach can increase the elemental mapping resolution and decrease acquisition times, fostering opportunities for applications that benefit from minimal sample damage such as heritage analysis, or timely analysis such as industrial applications. © 2022 The Author(s)