Abstract
In this work, X-ray fluorescence (XRF) and Laser-induced breakdown spectroscopy (LIBS) analyses were applied to samples of quartz, montebrasite, and turquoise hydrothermal veins in the Argemela Tin Mine (Central Portugal). Montebrasite (LiAl(PO4)(OH,F)) is potentially the main ore mineral; with its alteration, lithium (Li) can disseminate into other minerals. A hand sample was cut and analyzed by XRF and LIBS for several elements of interest including Cu, P, Al, Si, and Li. Although XRF cannot measure Li, results from its analysis are effective for distinguishing turquoise from montebrasite. LIBS analysis complemented this study, making it possible to conclude that turquoise does not contain any significant Li in its structure. The difference in spot size between the techniques (5 mm vs. 300 mu m for XRF and LIBS, respectively) resulted in a poorer performance by XRF in accurately identifying mixed minerals. A thin section was petrographically characterized and mapped using LIBS. The mapping results demonstrate the possibility of the successful identification of minerals and their alterations on a thin section. The results of XRF analysis and LIBS mapping in petrographic sections demonstrate the efficacy of these methods as tools for element and mineral identification, which can be important in exploration and mining phases, complementing more traditional techniques.

Abstract
Laser-induced breakdown spectroscopy (LIBS) is a technique that leverages atomic emission towards element identification and quantification. While the potential of the technology is vast, it still struggles with obstacles such as the variability of the technique. In recent years, several methods have exploited modifications to the standard implementation to work around this problem, mostly focused on the laser side to increase the signal-to-noise ratio of the emission. In this paper, we explore the effect of pulse duration on the detected signal intensity using a tunable LIBS system that consists of a versatile fiber laser, capable of emitting square-shaped pulses with a duration ranging from 10 to 100 ns. Our results show that, by tuning the duration of the pulse, it is possible to increase the signal-to-noise ratio of relevant elemental emission lines, an effect that we relate with the computed plasma temperature and associated density for the ion species. Despite the limitations of the work due to the low-resolution and small range of the spectrometer used, the preliminary results pave an interesting path towards the design of controllable LIBS systems that can be tailored to increase the signal-to-noise ratio and thus be useful for the deployment of more sensitive instruments both for qualitative and quantitative purposes. © 2022, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
This paper focuses on predicting predawn leaf water potential through a self-learning artificial intelligence (SL-AI) algorithm, a novel spectral processing algorithm that is based on the search for covariance modes, providing a direct relationship between spectral information and plant constituents. The SL-AI algorithm was applied in a dataset containing 847 observations obtained with a handheld hyperspectral spectroradiometer (400 -1010 nm), structured as: three grapevine cultivars (Touriga Nacional, Touriga Franca and Tinta Barroca), collected in three years (2014, 2015 and 2017), in two test sites in the renowned Douro Wine Region, northeast of Portugal. The Psi(pd) SL-AI quantification was tested both in regressive (R-2 = 0.97, MAPE = 18.30%) and classification (three classes; overall accuracy = 86.27%) approaches, where the radiation absorption spectrum zones of the chlorophylls, xanthophyll and water were identified along the vegetative growth cycle. The dataset was also tested with Artificial Neural Networks with Principal Component Analysis (ANN-PCA) and Partial Least Square (PLS), which presented worse performance when compared to SL-AI in the regressive (ANN-PCA - R-2 = 0.85, MAPE = 43.64%; PLS - R-2 = 0.94, MAPE = 28.76%) and classification (ANN-PCA - overall accuracy: 72.37%; PLS - overall accuracy: 73.79%) approaches. The Psi(pd) modelled with SL-AI demonstrated, through hyperspectral reflectance, a cause-effect of the grapevine's hydric status with the absorbance of bands related to chlorophyll, xanthophylls and water. This cause-effect interaction could be explored to identify cultivars and cultural practices, hydric, heating and lighting stresses.

Abstract
Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV-VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325-1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis.

Abstract
Total white blood cells count is an important diagnostic parameter in both human and veterinary medicines. State-of-the-art is performed by flow cytometry combined with light scattering or impedance measurements. Spectroscopy point-of-care has the advantages of miniaturization, low sampling, and real-time hemogram analysis. While white blood cells are in low proportions, while red blood cells and bilirubin dominate spectral information, complicating detection in blood. We performed a feasibility study for the direct detection of white blood cells counts in canine blood by visible-near infrared spectroscopy for veterinary applications, benchmarking current chemometrics techniques (similarity, global and local partial least squares, artificial neural networks and least-squares support vector machines) with self-learning artificial intelligence, introducing data augmentation to overcome the hurdle of knowledge representativity. White blood cells count information is present in the recorded spectra, allowing significant discrimination and equivalence between hemogram and spectra principal component scores. Chemometrics methods correlate white blood cells count to spectral features but with lower accuracy. Self-Learning Artificial Intelligence has the highest correlation (0.8478) and a small standard error of 6.92 x 10(9) cells/L, corresponding to a mean absolute percentage error of 25.37%. Such allows the accurate diagnosis of white blood cells in the range of values of the reference interval (5.6 to 17.8 x 10(9) cells/L) and above. This research is an important step toward the existence of a miniaturized spectral point-of-care hemogram analyzer.

Abstract
Shelf-life is defined as the time that a product is acceptable and meets the consumers expectations regarding food quality. It is the result of the conjunction of all services in production, distribution, and consumption. Shelf-life dating is one of the most difficult tasks in food engineering. Market pressure has lead to the implementation of shelf-life by sensory analyses, which may not reflect the full quality spectra. Moreover, traditional methods for shelf-life dating and small-scale distribution chain tests cannot reproduce in a laboratory the real conditions of storage, distribution, and consumption on food quality. Today, food engineers are facing the challenges to monitor, diagnose, and control the quality and safety of food products. The advent of nanotechnology, multivariate sensors, information systems, and complex systems will revolutionize the way we manage, distribute, and consume foods. The informed consumer demands foods, under the legal standards, at low cost, high standards of nutritional, sensory, and health benefits. To accommodate the new paradigms, we herein present a critical review of shelf-life dating approaches with special emphasis in computational systems and future trends on complex systems methodologies applied to the prediction of food quality and safety.