Colorimetric Evaluation of a Reintegration via Spectral Imaging-Case Study: Nasrid Tiling Panel from the Alhambra of Granada (Spain).

Color reintegration is a restoration treatment that involves applying paint or colored plaster to an object of cultural heritage to facilitate its perception and understanding. This study examines the impact of lighting on the visual appearance of one such restored piece: a tiled skirting panel from the Nasrid period (1238-1492), permanently on display at the Museum of the Alhambra (Spain). Spectral images in the range of 380-1080 nm were obtained using a hyperspectral image scanner. CIELAB and CIEDE2000 color coordinates at each pixel were computed assuming the CIE 1931 standard colorimetric observer and considering ten relevant illuminants proposed by the International Commission on Illumination (CIE): D65 plus nine white LEDs. Four main hues (blue, green, yellow, and black) can be distinguished in the original and reintegrated areas. For each hue, mean color difference from the mean (MCDM), CIEDE2000 average distances, volumes, and overlapping volumes were computed in the CIELAB space by comparing the original and the reintegrated zones. The study reveals noticeable average color differences between the original and reintegrated areas within tiles: 6.0 and 4.7 CIEDE2000 units for the yellow and blue tiles (with MCDM values of 3.7 and 4.5 and 5.8 and 7.2, respectively), and 16.6 and 17.8 CIEDE2000 units for the black and green tiles (with MCDM values of 13.2 and 12.2 and 10.9 and 11.3, respectively). The overlapping volume of CIELAB clouds of points corresponding to the original and reintegrated areas ranges from 35% to 50%, indicating that these areas would be perceived as different by observers with normal color vision for all four tiles. However, average color differences between the original and reintegrated areas changed with the tested illuminants by less than 2.6 CIEDE2000 units. Our current methodology provides useful quantitative results for evaluation of the color appearance of a reintegrated area under different light sources, helping curators and museum professionals to choose optimal lighting.

Probing the cw-Laser-Induced Fluorescence Enhancement in CsPbBr3 Nanocrystal Thin Films: An Interplay between Photo and Thermal Activation.

Perovskite nanocrystals hold significant promise for a wide range of applications, including solar cells, LEDs, photocatalysts, humidity and temperature sensors, memory devices, and low-cost photodetectors. Such technological potential stems from their exceptional quantum efficiency and charge carrier conduction capability. Nevertheless, the underlying mechanisms of photoexcitation, such as phase segregation, annealing, and ionic diffusion, remain insufficiently understood. In this context, we harnessed hyperspectral fluorescence microspectroscopy to advance our comprehension of fluorescence enhancement triggered by UV continuous-wave (cw) laser irradiation of CsPbBr3 colloidal nanocrystal thin films. Initially, we explored the kinetics of fluorescence enhancement and observed that its efficiency (φph) correlates with the laser power (P), following the relationship φph = 7.7⟨P⟩0.47±0.02. Subsequently, we estimated the local temperature induced by the laser, utilizing the finite-difference method framework, and calculated the activation energy (Ea) required for fluorescence enhancement to occur. Our findings revealed a very low activation energy, Ea ∼ 9 kJ/mol. Moreover, we mapped the fluorescence photoenhancement by spatial scanning and real-time static mode to determine its microscale length. Below a laser power of 60 µW, the photothermal diffusion length exhibited nearly constant values of approximately (22 ± 5) µm, while a significant increase was observed at higher laser power levels. These results were ascribed to the formation of nanocrystal superclusters within the film, which involves the interparticle spacing reduction, creating the so-called quantum dot solid configuration along with laser-induced annealing for higher laser powers.

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants.

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

Recognition of breast cancer subtypes using FTIR hyperspectral data.

Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.

A Spatial-Spectral Classification Method Based on Deep Learning for Controlling Pelagic Fish Landings in Chile.

Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red-Green-Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.

Evaluating the impact of the Central Chile Mega Drought on debris cover, broadband albedo, and surface drainage system of a Dry Andes glacier.

In recent years, Chile has experienced an extraordinary drought that has had significant impacts on both the livelihoods of people and the environment, including the Andean glaciers. This study focuses on analyzing the surface processes of Universidad Glacier, a benchmark glacier for the Dry Andes. Multiple remote sensing datasets are used alongside a novel spectral index designed for mapping of rock material located on the glacier's surface. Our findings highlight the precarious state of the glacier, which serves as a crucial water source for the region. The glacier exhibits locally varied debris accumulation and margin retreat. The most significant impacts are observed on the tongue and secondary accumulation cirques, with the latter at risk of disappearing. The debris cover on the tongue is expanding, reaching higher elevations, and is accompanied by glacier retreat, especially at higher altitudes. The equilibrium line is rapidly shifting upglacier, although the mid-season snow cover still frequently reaches the 2013 equilibrium line, even in 2020. Changes in stream density on the glacier tongue indicate an increased water supply in this area, likely due to enhanced melting of glacial ice. These observed processes align well with meteorological data obtained from reanalysis products. The behavior of dust and debris is influenced by precipitation amount, while the rate of retreat is linked to air temperature.

Label-free fluorescence microscopy: revisiting the opportunities with autofluorescent molecules and harmonic generations as biosensors and biomarkers for quantitative biology.

Over the past decade, the utilization of advanced fluorescence microscopy technologies has presented numerous opportunities to study or re-investigate autofluorescent molecules and harmonic generation signals as molecular biomarkers and biosensors for in vivo cell and tissue studies. The label-free approaches benefit from the endogenous fluorescent molecules within the cell and take advantage of their spectroscopy properties to address biological questions. Harmonic generation can be used as a tool to identify the occurrence of fibrillar or lipid deposits in tissues, by using second and third-harmonic generation microscopy. Combining autofluorescence with novel techniques and tools such as fluorescence lifetime imaging microscopy (FLIM) and hyperspectral imaging (HSI) with model-free analysis of phasor plots has revolutionized the understanding of molecular processes such as cellular metabolism. These tools provide quantitative information that is often hidden under classical intensity-based microscopy. In this short review, we aim to illustrate how some of these technologies and techniques may enable investigation without the need to add a foreign fluorescence molecule that can modify or affect the results. We address some of the most important autofluorescence molecules and their spectroscopic properties to illustrate the potential of these combined tools. We discuss using them as biomarkers and biosensors and, under the lens of this new technology, identify some of the challenges and potentials for future advances in the field.

Novel opportunities from bioimaging to understand the trafficking and maturation of intracellular pulmonary surfactant and its role in lung diseases.

Pulmonary surfactant (PS), a complex mixture of lipids and proteins, is essential for maintaining proper lung function. It reduces surface tension in the alveoli, preventing collapse during expiration and facilitating re-expansion during inspiration. Additionally, PS has crucial roles in the respiratory system's innate defense and immune regulation. Dysfunction of PS contributes to various respiratory diseases, including neonatal respiratory distress syndrome (NRDS), adult respiratory distress syndrome (ARDS), COVID-19-associated ARDS, and ventilator-induced lung injury (VILI), among others. Furthermore, PS alterations play a significant role in chronic lung diseases such as chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF). The intracellular stage involves storing and releasing a specialized subcellular organelle known as lamellar bodies (LB). The maturation of these organelles requires coordinated signaling to organize their intracellular organization in time and space. LB's intracellular maturation involves the lipid composition and critical processing of surfactant proteins to achieve proper functionality. Over a decade ago, the supramolecular organization of lamellar bodies was studied using electron microscopy. In recent years, novel bioimaging tools combining spectroscopy and microscopy have been utilized to investigate the in cellulo intracellular organization of lamellar bodies temporally and spatially. This short review provides an up-to-date understanding of intracellular LBs. Hyperspectral imaging and phasor analysis have allowed identifying specific transitions in LB's hydration, providing insights into their membrane dynamics and structure. A discussion and overview of the latest approaches that have contributed to a new comprehension of the trafficking and structure of lamellar bodies is presented.

Raman mapping for determination of TiO2 in different solid food samples by multivariate curve resolution with alternating least squares.

Titanium dioxide is a food additive commonly used as a white food coloring (E171). Its wide use by the food industry associated with the nanometric size distribution of the particles of this pigment has shown high genotoxicity associated with recurrent exposure by ingestion. Therefore, the use of E171 in food products has already been banned by some industries and in the European Union. Such banishment should soon be extended to other countries around the world, making it important to establish techniques for the efficient determination of TiO2 in different food products. The association between hyperspectral images and chemometric tools can be useful in this sense, aiming to enable the use of a single method for sample preparation and analysis of different types of food. Thus, the present work aims to evaluate the use of Raman mapping associated with the resolution of multivariate curves with alternating least squares (MCR-ALS) for the determination of titanium dioxide in solid food samples with different compositions, without the need to introduce specific sample preparation. The proposed method allowed for the first-time quantification of TiO2 in different food matrices without specific sample preparation, with a simple, rapid, accurate (93% of recovery), low detection limits (0.0111% m/m) and quantification (0.0370% m/m) and adequate linearity (r = 0.9990) and precise (standard deviation around 0.020-0.030% w/w) methodology. Such results highlight the potential use of Raman mapping associated with the MCR-ALS for quantification of the nano-TiO2 in commercial samples.

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms.

In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.

Dataset for the determination of phosphorus in soil through the analysis of hyperspectral images.

This article presents the capture protocol to acquire hyperspectral images, which can be used to quantify the concentration of total phosphorus in soil samples. 152 soil samples were prepared, and a hyperspectral cube made up of 145 images in the VIS-NIR bands, between 420 and 1000 nm, was obtained from each of them. The images obtained were taken with the Bayspec OCIF Series hyperspectral camera, in push-broom function, using a platform that includes an illumination system that offers a continuous spectrum in the range of interest. The samples were prepared with a soil from the Santander de Quilichao region, Cauca, Colombia, and mixed with known concentrations of P2O5 fertilizer, so that a total mass of 50 g was obtained. Each sample was deposited in a round black plastic container, 6 cm in diameter and a depth of 1 cm. The soil samples were analyzed in the laboratory to establish the concentration of total phosphorus. Therefore, the database is made up of the images associated with the hyperspectral cube of each sample, and four tables: the first describes the properties of the soil used to obtain the mixtures, the second the composition of the fertilizer used, the third describes the soil-fertilizer ratio to make up the samples, and the fourth was the laboratory analysis of the total phosphorus content of the analyzed samples.

Advanced Fluorescence Microscopy Methods to Study Dynamics of Fluorescent Proteins In Vivo.

Fluorescent proteins are standard tools for addressing biological questions in a cell biology laboratory. The genetic tagging of protein of interest with fluorescent proteins opens the opportunity to follow them in vivo and to understand their interactions and dynamics. In addition, the latest advances in optical microscopy image acquisition and processing allow us to study many cellular processes in vivo. Techniques such as fluorescence lifetime microscopy and hyperspectral imaging provide valuable tools for understanding fluorescent protein interactions and their photophysics. Finally, fluorescence fluctuation analysis opens the possibility to address questions of molecular diffusion, protein-protein interactions, and oligomerization, among others, yielding quantitative information on the subject of study. This chapter will cover some of the more important advances in cutting-edge technologies and methods that, combined with fluorescent proteins, open new frontiers for biological studies.

Proximal hyperspectral analysis in grape leaves for region and variety identification / Análise hiperespectral proximal em folhas de videiras para identificação de regiões e variedades

Reflectance measurements of plants of the same species can produce sets of data with differences between spectra, due to factors that can be external to the plant, like the environment where the plant grows, and to internal factors, for measurements of different varieties. This paper reports results of the analysis of radiometric measurements performed on leaves of vines of several grape varieties and on several sites. The objective of the research was, after the application of techniques of dimensionality reduction for the definition of the most relevant wavelengths, to evaluate four machine learning models applied to the observational sample aiming to discriminate classes of region and variety in vineyards. The tested machine learning classification models were Canonical Discrimination Analysis (CDA), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Machine (SVM). From the results, we reported that the LGBM model obtained better accuracy in spectral discrimination by region, with a value the 0.93, followed by the RF model. Regarding the discrimination between grape varieties, these two models also achieved better results, with accuracies of 0.88 and 0.89. The wavelengths more relevant for discrimination were at ultraviolet, followed by those at blue and green spectral regions. This research pointed toward the importance of defining the wavelengths more relevant to the characterization of the reflectance spectra of leaves of grape varieties and revealed the effective capability of discriminating vineyards by their region or grape variety, using machine learning models.

Medições de refletância de plantas da mesma espécie podem produzir conjuntos de dados com diferenças entre os espectros, devido a fatores que podem ser externos à planta, como o ambiente onde a planta cresce, e fatores internos, para medições com variedades de plantas. Este artigo reporta resultados da análise de medições por espectrorradiometria efetuadas em folhas de vinhas de variedades e em diferentes localidades. O objetivo desta pesquisa foi, após a aplicação de técnicas de redução de dimensionalidade para a definição dos comprimentos de onda mais relevantes, avaliar quatro modelos de aprendizado de máquina aplicados à amostra observacional visando discriminar classes de região e variedade. Os modelos de classificação de aprendizado de máquina testados foram Canonical Discrimination Analysis (CDA), Light Gradient Boosting Machine (LGBM), Random Forest (RF) e Support Vector Machine (SVM). A partir dos resultados, relatamos que o modelo LGBM obteve melhor acurácia na discriminação espectral por região, com valor de 0,93, seguido pelo modelo RF. Relativamente à discriminação entre castas, estes dois modelos também obtiveram melhores resultados, com acurácias de 0,88 e 0,89. Os comprimentos de onda mais importantes para as discriminações procuradas estiveram na região do ultravioleta, seguidos do azul e do verde. Este trabalho aponta para a importância de detectar os comprimentos de onda mais relevantes para a caracterização dos espectros de reflectância das folhas de variedades de vinhas, e revela a capacidade efetiva de discriminar vinhedos por suas regiões ou variedades, usando modelos de aprendizado de máquina.

Quantitative melanoma diagnosis using spectral phasor analysis of hyperspectral imaging from label-free slices.

Introduction: Melanoma diagnosis traditionally relies on microscopic examination of hematoxylin and eosin (H&E) slides by dermatopathologists to search for specific architectural and cytological features. Unfortunately, no single molecular marker exists to reliably differentiate melanoma from benign lesions such as nevi. This study explored the potential of autofluorescent molecules within tissues to provide molecular fingerprints indicative of degenerated melanocytes in melanoma. Methods: Using hyperspectral imaging (HSI) and spectral phasor analysis, we investigated autofluorescence patterns in melanoma compared to intradermal nevi. Using UV excitation and a commercial spectral confocal microscope, we acquired label-free HSI data from the whole-slice samples. Results: Our findings revealed distinct spectral phasor distributions between melanoma and intradermal nevi, with melanoma displaying a broader phasor phase distribution, signifying a more heterogeneous autofluorescence pattern. Notably, longer wavelengths associated with larger phases correlated with regions identified as melanoma by expert dermatopathologists using H&E staining. Quantitative analysis of phase and modulation histograms within the phasor clusters of five melanomas (with Breslow thicknesses ranging from 0.5 mm to 6 mm) and five intradermal nevi consistently highlighted differences between the two groups. We further demonstrated the potential for the discrimination of several melanocytic lesions using center-of-mass comparisons of phase and modulation variables. Remarkably, modulation versus phase center of mass comparisons revealed strong statistical significance among the groups. Additionally, we identified the molecular endogenous markers responsible for tissue autofluorescence, including collagen, elastin, NADH, FAD, and melanin. In melanoma, autofluorescence is characterized by a higher phase contribution, indicating an increase in FAD and melanin in melanocyte nests. In contrast, NADH, elastin, and collagen dominate the autofluorescence of the nevus. Discussion: This work underscores the potential of autofluorescence and HSI-phasor analysis as valuable tools for quantifying tissue molecular fingerprints, thereby supporting more effective and quantitative melanoma diagnosis.

Hyperspectral Microscopy Technology to Detect Syrups Adulteration of Endemic Guindo Santo and Quillay Honey Using Machine-Learning Tools.

Honey adulteration is a common practice that affects food quality and sale prices, and certifying the origin of the honey using non-destructive methods is critical. Guindo Santo and Quillay are fundamental for the honey production of Biobío and the Ñuble region in Chile. Furthermore, Guindo Santo only exists in this area of the world. Therefore, certifying honey of this species is crucial for beekeeper communities-mostly natives-to give them advantages and competitiveness in the global market. To solve this necessity, we present a system for detecting adulterated endemic honey that combines different artificial intelligence networks with a confocal optical microscope and a tunable optical filter for hyperspectral data acquisition. Honey samples artificially adulterated with syrups at concentrations undetectable to the naked eye were used for validating different artificial intelligence models. Comparing Linear discriminant analysis (LDA), Support vector machine (SVM), and Neural Network (NN), we reach the best average accuracy value with SVM of 93% for all classes in both kinds of honey. We hope these results will be the starting point of a method for honey certification in Chile in an automated way and with high precision.

Continuous Monitoring of Suspended Particulate Matter in Tropical Inland Waters by High-Frequency, Above-Water Radiometry.

Water and sediment discharges can change rapidly, and low-frequency measurement devices might not be sufficient to elucidate existing dynamics. As such, above-water radiometry might enhance monitoring of suspended particulate matter (SPM) dynamics in inland waters. However, it has been barely applied for continuous monitoring, especially under partially cloudy sky conditions. In this study, an in situ, high-frequency (30 s timestep), above-water radiometric dataset, collected over 18 days in a tropical reservoir, is analyzed for the purpose of continuous monitoring of SPM concentration. Different modalities to retrieve reflectance spectra, as well as SPM inversion algorithms, were applied and evaluated. We propose a sequence of processing that achieved an average unsigned percent difference (UPD) of 10.4% during cloudy conditions and 4.6% during clear-sky conditions for Rrs (665 nm), compared to the respective UPD values of 88.23% and 13.17% when using a simple calculation approach. SPM retrieval methods were also evaluated and, depending on the methods used, we show that the coefficient of variation (CV) of the SPM concentration varied from 69.5% down to 2.7% when using a semi-analytical approach. As such, the proposed processing approach is effective at reducing unwanted variability in the resulting SPM concentration assessed from above-water radiometry, and our work paves the way towards the use of this noninvasive technique for high-frequency monitoring of SPM concentrations in streams and lakes.

Effective Methods Based on Distinct Learning Principles for the Analysis of Hyperspectral Images to Detect Black Sigatoka Disease.

Current chemical methods used to control plant diseases cause a negative impact on the environment and increase production costs. Accurate and early detection is vital for designing effective protection strategies for crops. We evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging. We discuss the learning features of the techniques used, which will help researchers improve their understanding of the required data conditions and identify a method suitable for their research needs. A set of hyperspectral images of banana leaves inoculated with a conidial suspension of black sigatoka fungus (Pseudocercospora fijiensis) was used to train and validate machine learning models. Support vector machine (SVM), multilayer perceptron (MLP), neural networks, N-way partial least square-discriminant analysis (NPLS-DA), and partial least square-penalized logistic regression (PLS-PLR) were selected due to their high predictive power. The metrics of AUC, precision, sensitivity, prediction, and F1 were used for the models' evaluation. The experimental results show that the PLS-PLR, SVM, and MLP models allow for the successful detection of black sigatoka disease with high accuracy, which positions them as robust and highly reliable HSI classification methods for the early detection of plant disease and can be used to assess chemical and biological control of phytopathogens.

Rapid Quantification Method for Yield, Calorimetric Energy and Chlorophyll a Fluorescence Parameters in Nicotiana tabacum L. Using Vis-NIR-SWIR Hyperspectroscopy.

High-throughput and large-scale data are part of a new era of plant remote sensing science. Quantification of the yield, energetic content, and chlorophyll a fluorescence (ChlF) remains laborious and is of great interest to physiologists and photobiologists. We propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR) as a tool to estimate biomass production, calorimetric energy content and chlorophyll a fluorescence parameters. The results showed the presence of typical inflections associated with chemical and structural components present in plants, enabling us to obtain PLSR models with R2P and RPDP values greater than >0.82 and 3.33, respectively. The most important wavelengths were well distributed into 400 (violet), 440 (blue), 550 (green), 670 (red), 700−750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm operating ranges of the spectrum. Thus, we report a methodology to simultaneously determine fifteen attributes (i.e., yield (biomass), ΔH°area, ΔH°mass, Fv/Fm, Fv'/Fm', ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI(abs), D.F.) with high accuracy and precision and with excellent predictive capacity for most of them. These results are promising for plant physiology studies and will provide a better understanding of photosystem dynamics in tobacco plants when a large number of samples must be evaluated within a short period and with remote acquisition data.

2D Thermal Maps Using Hyperspectral Scanning of Single Upconverting Microcrystals: Experimental Artifacts and Image Processing.

Whereas lanthanide-based upconverting particles are promising candidates for several micro- and nanothermometry applications, understanding spatially varying effects related to their internal dynamics and interactions with the environment near the surface remains challenging. To separate the bulk from the surface response, this work proposes and performs hyperspectral sample-scanning experiments to obtain spatially resolved thermometric measurements on single microparticles of NaYF4: Yb3+,Er3+. Our results showed that the particle's thermometric response depends on the excitation laser incidence position, which may directly affect the temperature readout. Furthermore, it was noticed that even minor temperature changes (<1 K) caused by room temperature variations at the spectrometer CCD sensor used to record the luminescence signal may significantly modify the measurements. This work also provides some suggestions for building 2D thermal maps that shall be helpful for understanding surface-related effects in micro- and nanothermometers using hyperspectral techniques. Therefore, the results presented herein may impact applications of lanthanide-based nanothermometers, as in the understanding of energy-transfer processes inside systems such as nanoelectronic devices or living cells.

Data reduction by randomization subsampling for the study of large hyperspectral datasets.

Large amount of information in hyperspectral images (HSI) generally makes their analysis (e.g., principal component analysis, PCA) time consuming and often requires a lot of random access memory (RAM) and high computing power. This is particularly problematic for analysis of large images, containing millions of pixels, which can be created by augmenting series of single images (e.g., in time series analysis). This tutorial explores how data reduction can be used to analyze time series hyperspectral images much faster without losing crucial analytical information. Two of the most common data reduction methods have been chosen from the recent research. The first one uses a simple randomization method called randomized sub-sampling PCA (RSPCA). The second implies a more robust randomization method based on local-rank approximations (rPCA). This manuscript exposes the major benefits and drawbacks of both methods with the spirit of being as didactical as possible for a reader. A comprehensive comparison is made considering the amount of information retained by the PCA models at different compression degrees and the performance time. Extrapolation is also made to the case where the effect of time and any other factor are to be studied simultaneously.