Diana Filipa Guimarães

Abstract
Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques by combining information gathered from distinct sources. In this manuscript, we explore such opportunities by focusing on two well-known spectral imaging techniques, namely laser-induced breakdown spectroscopy, and hyperspectral imaging, and explore the opportunities of collaborative sensing for a case study involving mineral identification. In specific, the work builds upon two distinct approaches: a traditional sensor fusion, where we strive to increase the information gathered by including information from the two modalities; and a knowledge distillation approach, where the Laser Induced Breakdown spectroscopy is used as an autonomous supervisor for hyperspectral imaging. Our results show the potential of both approaches in enhancing the performance over a single modality sensing system, highlighting, in particular, the advantages of the knowledge distillation framework in maximizing the potential benefits of using multiple techniques to build more interpretable models and paving for industrial applications.

Abstract
Laser-induced breakdown spectroscopy (LIBS) imaging has now a well-established position in the subject of spectral imaging, leveraging multi-element detection capabilities and fast acquisition rates to support applications both at academic and technological levels. In current applications, the standard processing pipeline to explore LIBS imaging data sets revolves around identifying an element that is suspected to exist within the sample and generating maps based on its characteristic emission lines. Such an approach requires some previous expert knowledge both on the technique and on the sample side, which hinders a wider and more transparent accessibility of the LIBS imaging technique by non-specialists. To address this issue, techniques based on visual analysis or peak finding algorithms are applied on the average or maximum spectrum, and may be employed for automatically identifying relevant spectral regions. Yet, maps containing relevant information may often be discarded due to low signal-to-noise ratios or interference with other elements. In this context, this work presents an agnostic processing pipeline based on a spatial information ratio metric that is computed in the Fourier space for each wavelength and that allows for the identification of relevant spectral ranges in LIBS. The results suggest a more robust and streamlined approach to feature extraction in LIBS imaging compared with traditional inspection of the spectra, which can introduce novel opportunities not only for spectral data analysis but also in the field of data compression.

Abstract
Circular economy policies and recycling play a pivotal role in fostering sustainable models for the wood industry capable of reducing the environmental impact of our consumption patterns. The production of Particleboard is a good example of industry that uses high quantities of recycled wood. However, it poses risks since wood often have contaminants that compromise compliance of safety standards. Thus, it is necessary to develop methodologies for rapid analysis of chemical contaminants in wood wastes that allow easy detection of these elements. In this work, the capability of Laser-induced breakdown spectroscopy (LIBS) to detect a set of heavy metals in wood samples was explored. Some advantages of this technique, such as portability, minimal to no sample preparation, and quick analysis are characteristics that make this method one of the most suitable for this purpose of analysis. In the majority of cases, the contamination comes from the pigments used in paints, varnishes, or coatings. Titanium (Ti) e.g. is a common element in white pigments and Chromium (Cr) in red and green pigments. To ensure the presence or absence of Cr and Ti, a set of 3 lines was analysed. The results revealed the presence of these elements and that 30% of the samples seem to be highly contaminated. The LIBS technique proved to be a powerful methodogy for decision-making purposes.

Abstract
The application of surface treatments to cork stoppers is presently a common practice in the wine industry, designed to achieve maximum performance and optimal costumer experience of premium products. Unfortunately, current coating techniques lack efficient process control tools, often resulting in faulty products being detected too late, already in use, compromising performance, product quality and mining consumer confidence. In this work a fully automated system equipped with machine vision and automatic feeding of corks, was coupled with an imaging LIBS setup and used to perform a benchmarking against conventional quality control methods. Results clearly demonstrate the capability of the new LIBS system to effectively evaluate in real time the quality of silicone-based surface coatings in cork stoppers, effectively working as a tool for process control providing a route for effective optimization.

Abstract
Recycling of post-consumer wood waste into wood-based panels may be hindered by the presence of physical and chemical impurities in the waste stream. Therefore greater attention should be given to assessing the quality of wood waste and in particular to heavy metals contamination. One of the elements that poses concern is Chromium (Cr), Cr compounds can be toxic, particularly hexavalent chromium (Cr(VI)), which is a known human carcinogen. Hence, screening for Cr in wood waste plays a pivotal role in enhancing recycling facility operations and mitigating contamination before final product incorporation. In this study, a Laser-Induced Breakdown Spectroscopy (LIBS) methodology was optimized for screening wood waste for Cr and validated by X-ray Fluorescence (XRF) measurements. LIBS spectral complexity and sample matrix effects challenges were addressed through careful selection of Cr lines and tailored data analysis algorithms. The results showed that LIBS imaging successfully provided a straightforward timely output revealing the contaminated wood samples, crucial for quick decision-making in production lines. © 2024 IEEE.