Abstract
Combining data from different sensing modalities has been a promising research topic for building better and more reliable data-driven models. In particular, it is known that multimodal spectral imaging can improve the analytical capabilities of standalone spectroscopy techniques through fusion, hyphenation, or knowledge distillation techniques. In this manuscript, we focus on the latter, exploring how one can increase the performance of a Laser-induced Breakdown Spectroscopy system for mineral classification problems using additional spectral imaging techniques. Specifically, focusing on a scenario where Raman spectroscopy delivers accurate mineral classification performance, we show how to deploy a knowledge distillation pipeline where Raman spectroscopy may act as an autonomous supervisor for LIBS. For a case study concerning a challenging Li-bearing mineral identification of spodumene and petalite, our results demonstrate the advantages of this method in improving the performance of a single-technique system. LIBS trained with labels obtained by Raman presents an enhanced classification performance. Furthermore, leveraging the interpretability of the model deployed, the workflow opens opportunities for the deployment of assisted feature discovery pipelines, which may impact future academic and industrial applications.

Abstract
Mineral identification is a challenging task in geological sciences, which often implies multiple analyses of the physical and chemical properties of the samples for an accurate result. This task is particularly critical for the mining industry, where proper and fast mineral identification may translate into major efficiency and performance gains, such as in the case of the lithium mining industry. In this study, a mineral identification algorithm optimized for analyzing lithium-bearing samples using Laser-induced breakdown spectroscopy (LIBS) imaging, is put to the test with a set of representative samples. The algorithm incorporates advanced spectral processing techniques-baseline removal, Gaussian filtering, and data normalization-alongside unsupervised clustering to generate interpretable classification maps and auxiliary charts. These enhancements facilitate rapid and precise labelling of mineral compositions, significantly improving the interpretability and interactivity of the user interface. Extensive testing on diverse mineral samples with varying complexities confirmed the algorithm's robustness and broad applicability. Challenges related to sample granulometry and LIBS resolution were identified, suggesting future directions for optimizing system resolution to enhance classification accuracy in complex mineral matrices. The integration of this advanced algorithm with LIBS technology holds the potential to accelerate the mineral evaluation, paving the way for more efficient and sustainable mineral exploration.

Abstract
Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

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.