High Spectral Resolution Research Articles

Simultaneous Concentration and T2 Mapping of Brain Metabolites by Fast Multi-Echo Spectroscopic Imaging.

The purpose of this study was to produce metabolite-specific T2 and concentration maps in a clinically compatible time frame. A multi-TE 2D MR spectroscopic imaging (MRSI) experiment (multi-echo single-shot MRSI [MESS-MRSI]) deployed truncated and partially sampled multi-echo trains from single scans and was combined with simultaneous multiparametric model fitting. It was tested invivo for the brain in five healthy subjects. Cramér-Rao lower bounds (CRLB) were used as the measure of performance. The novel method was compared with (1) traditional multi-echo multi-shot (MEMS) MRSI and, as proof of concept, with (2) a truncated version of MEMS, which mimics the MESS acquisition (MESS-mocked) on the original fully sampled MEMS dataset. MESS-MRSI simultaneously yields concentration and T2 maps with a nominal voxel size of ~2 cm3 with a 16 × 16 FOV matrix in 7 min scan time. The estimated values not only align well with the equivalent mocked experiment but are also in good agreement with the traditional threefold longer MEMS acquisition. The MESS-MRSI scheme extends former findings for single-voxel MESS, with improvements in CRLB ranging from 17% to 45% for concentrations and 10% to 23% for T2s when compared to traditional MEMS. This finding suggests that concentrations and T2 times can be reliably estimated in a multi-echo spectroscopic imaging exam by trading off spectral resolution (for some of the acquired TEs) with a significant reduction in scan time, as long as (1) an appropriate bidimensional frequency-TE model is deployed and (2) one TE is sampled in full. Thus, high spectral resolution information can be injected to the partially sampled TEs during fitting by prior knowledge from the one fully sampled TE. Tissue-type and regional distributions of 16 metabolite concentrations align well with the literature, and T2 distributions for five major metabolites are described by region and tissue. The novel MRSI acquisition strategy, based on partially sampled single-shot multi-echo trains twinned to multiparametric fitting, is optimally suited to provide simultaneous 2D concentration and T2 maps in clinic-compatible scan times. MESS principles allow embedding advanced MRSI techniques to further improve speed, coverage, or resolution. Preliminary findings from a cohort of five subjects reveal correlations between T2 relaxation times and the relative fraction of gray/white matter, suggesting tissue-type-dependent microstructural changes.

Raman measurements using an acousto-optic tunable filter spatial heterodyne Raman spectrometer with an echelle-mirror structure

This study presents an acousto-optic tunable filter technology-based echelle-mirror spatial heterodyne Raman spectrometer (AOTF-EMSHRS) that integrates echelle gratings with mirror spatial heterodyne detection. The instrument uses the grating’s high resolution and the AOTF’s rapid wavelength selection capability to optimize the optical path through a mirror design. This results in a high signal-to-noise ratio (SNR), high spectral resolution, and broad wavelength range measurements. After calibration, the theoretical resolution reached 1.53 cm−1, with a single-order spectral detection range of 1574.3 cm−1. The spectral detection range spans from 100 cm−1 to 4397 cm−1. Experimentally, we detected and analyzed various organic and inorganic substances and solutions. We compared EMSHRS performances with and without the AOTF and found that the SNR of the AOTF-EMSHRS system has improved by at least twofold. Additionally, we measured solutions with different molar concentrations, various sulfate mixtures, and mixtures in transparent containers made from different materials, obtained comprehensive spectral information, and conducted detailed analyses. The AOTF-EMSHRS demonstrated excellent measurement performance during complex sample detection and analysis.

Comparative experimental validation of microwave hyperspectral atmospheric soundings in clear-sky conditions

Abstract. Accurate observations of atmospheric temperature and water vapor profiles are essential for weather forecasting and climate change detection. Hyperspectral radiance measurements afford a useful means to retrieve these thermodynamic variable fields by harnessing the rich information contained in the electromagnetic wave spectrum of the atmospheric radiation. In contrast to infrared radiometry, microwave radiometry can penetrate clouds, making it a valuable tool for all-sky thermodynamic retrievals. Recent advancements have led to the fabrication of a hyperspectral microwave radiometer: the High Spectral Resolution Airborne Microwave Sounder (HiSRAMS). This study utilizes HiSRAMS to retrieve atmospheric temperature and water vapor profiles under clear-sky conditions; this is an initial assessment of one of the first hyperspectral microwave radiometers, comparing the results to those from an infrared hyperspectrometer, the Atmospheric Emitted Radiance Interferometer (AERI). HiSRAMS and AERI measurements under different viewing geometries have been acquired and compared for atmospheric retrieval. When both instruments are placed on the ground to acquire zenith-pointing measurements, the infrared hyperspectral measurements exhibit higher information content and greater vertical resolution for temperature and water vapor retrievals than the microwave hyperspectral measurements. A synergistic fusion of HiSRAMS and AERI measurements from the air and ground is tested. This “sandwich” sounding of the atmosphere takes advantage of the complementary information contents of the two instruments and is found to notably improve retrieval accuracy.

Evidence of the Amino Acids Tyrosine and Phenylalanine in the Interstellar Material of IC348 in Perseus

We employed data from the Spitzer Space Telescope to investigate the presence of the aromatic amino acids tyrosine and phenylalanine in the interstellar gas of the young star cluster IC 348. Our analysis revealed emission lines in the observed spectrum that closely matched the strongest mid-infrared laboratory bands associated with tyrosine and phenylalanine in terms of wavelength and intensity. Through flux measurements, we estimated column densities along the line of sight toward the core of IC 348, ranging from 0.8–1.0 × 1011 cm−2. Additionally, these emission lines were evident in the combined spectra of more than 30 interstellar locations spanning various unrelated star-forming regions observed by Spitzer, indicating a widespread distribution of the molecules responsible for the emission throughout interstellar space. Prospective endeavors employing high spectral resolution mid-infrared searches for proteinogenic amino acids in protostars, protoplanetary disks, and the interstellar medium will play a pivotal role in elucidating the external origins of meteoritic amino acids and understanding the prebiotic conditions that laid the groundwork for life on early Earth.

Calibration of backscattering coefficients with coherent heterodyne lidar utilizing molecular scattering.

Coherent heterodyne lidars are typically used for windspeed and attenuated backscattering measurements. The lack of molecular backscattering detection capability has limited the calibrated backscattering measurements until recent advances in coherent lidar technology. In this work, the simultaneous detection of aerosol and molecular backscattering is demonstrated with coherent heterodyne lidar, and the results are compared with a state-of-the-art Raman lidar PollyXT as a reference in a long-range for the first time. The molecular scattering is measured up to 3 km altitude and the extinction of laser power is calculated based on the attenuation. The essential corrections for the molecular scattering data are described and discussed. The backscattering coefficients are calculated with simple high spectral resolution lidar algorithms and the low altitude overlap is calibrated with on-site ceilometer data. A successful high spectral resolution measurement within coherent heterodyne lidar enables the self-calibration of the measured data and removes guessing of the backscatter to extinction ratio and initial values for the typical inversion algorithms. The self-calibration also enables basic aerosol classification based on the measured backscatter to extinction ratios.

Glow Discharge Optical Emission Coded Aperture Spectroscopy.

Glow discharge optical emission spectrometry (GDOES) allows fast and simultaneous multielemental analysis directly from solids and depth profiling down to the nanometer scale, which is critical for thin-film (TF) characterization. Nevertheless, operating conditions for the best limits of detection (LODs) are compromised in lieu of the best sputtering crater shapes for depth resolution. In addition, the fast transient signals from ultra-TFs do not permit the optimal sampling statistics of bulk analysis such that LODs are further compromised. Furthermore, commercial GDOES instruments rely on a slit-based light dispersion that favors high spectral resolution at the expense of light throughput. Here, a new technique called glow discharge optical emission coded aperture spectrometry (GOCAS) is shown to allow both a higher spectral resolution and higher light throughput by using a coded aperture (CA) with multiple thin slits at the spectrograph's entrance to measure the convoluted spectra and compressed sensing (CS) algorithms to recover the deconvoluted spectra from the full field of view. The effects of CA characteristics on spectral reconstruction fidelity were studied and showed the best fidelity for smaller slits, 50% transmittance, and wider CA with a higher number of slits. In addition, Shearlet enhanced snapshot compressive imaging (SeSCI)GPU showed the best performance of the CS algorithms studied, including SeSCICPU, two-step iterative shrinkage/thresholding (TwIST), and alternating direction method of multipliers total variation minimization (ADMM-TV). Moreover, GOCAS is shown to be very robust against increasing detector Gaussian noise. Finally, standard reference materials are used to show up to ∼30× improved S/N and an order-of-magnitude improved LODs, at the fastest acquisition times (fraction of a ms), which has the potential to be transformative for depth profiling of nanostructured materials.

Hydroxyl Lines and Moonlight: A High Spectral Resolution Investigation of Near-infrared Skylines from Maunakea to Guide Near-infrared Spectroscopic Surveys

Hydroxyl Lines and Moonlight: A High Spectral Resolution Investigation of Near-infrared Skylines from Maunakea to Guide Near-infrared Spectroscopic Surveys

Physical properties of circumnuclear ionising clusters III. Kinematics of gas and stars in NGC,7742

For this third paper in the series, we studied the kinematics of the ionised gas and stars, and calculated the dynamical masses of the circumnuclear star-forming regions in the ring of the face-on spiral NGC 7742. We used high spectral resolution data from the MEGARA instrument mounted on the Gran Telescopio Canarias (GTC) to measure the kinematical components of the nebular emission lines of selected HII regions and the stellar velocity dispersions from the CaT absorption lines that allow the derivation of the associated cluster virialised masses. The emission line profiles show two different kinematical components: a narrow one with a velocity dispersion of ∼ 10 km/s and a broad one with a velocity dispersion similar to the values found for the stellar absorption lines. The derived star cluster dynamical masses range from 2.5 times 10^6 to 10.0 times 10^7 M_⊙. The comparison of gas and stellar velocity dispersions suggests a scenario where the clusters have formed simultaneously in a first star formation episode with a fraction of the stellar evolution feedback remaining trapped in the cluster, subject to the same gravitational potential as the cluster stars. Between 0.15 and 7.07 % of the total dynamical mass of the cluster would have cooled down and formed a new, younger, population of stars, responsible for the ionisation of the gas currently observed.

Probing red supergiant atmospheres and winds with early-time, high-cadence, high-resolution type II supernova spectra

High-cadence high-resolution spectroscopic observations of infant Type II supernovae (SNe) represent an exquisite probe of the atmospheres and winds of exploding red-supergiant (RSG) stars. Using radiation hydrodynamics and radiative transfer calculations, we studied the gas and radiation properties during and after the phase of shock breakout, considering RSG star progenitors enshrouded within a circumstellar material (CSM) that varies in terms of the extent, density, and velocity profile. In all cases, the original, unadulterated CSM structure is probed only at the onset of shock breakout, seen in high-resolution spectra as narrow, often blueshifted emission components, possibly with an additional absorption trough. As the SN luminosity rises during breakout, radiative acceleration of the unshocked CSM starts, leading to a broadening of the ``narrow'' lines by several 100 (up to several 1000) depending on the CSM properties. This acceleration is at its maximum close to the shock, where the radiative flux is greater and thus typically masked by optical-depth effects. Generally, the narrow-line broadening is greater for more compact, tenuous CSM because of the proximity to the shock where the flux is born; it is smaller in the denser and more extended CSM. Narrow-line emission should show a broadening that slowly increases first (the line forms further out in the original wind), then sharply rises (the line forms in a region that is radiatively accelerated), before decreasing until late times (the line forms further away in regions more weakly accelerated). Radiative acceleration is expected to inhibit X-ray emission during the early (IIn) phase. Although high spectral resolution is critical at the earliest times to probe the original slow wind, the radiative acceleration and the associated line broadening may be captured with medium resolution. This would allow for a simultaneous view of narrow, Doppler-broadened line emission, as well as extended, electron-scattering broadened emission.

Nonlinear memristive computational spectrometer

In the domain of spectroscopy, miniaturization efforts often face significant challenges, particularly in achieving high spectral resolution and precise construction. Here, we introduce a computational spectrometer powered by a nonlinear photonic memristor with a WSe2 homojunction. This approach overcomes traditional limitations, such as constrained Fermi level tunability, persistent dark current, and limited photoresponse dimensionality through dynamic energy band modulation driven by palladium (Pd) ion migration. The critical role of Pd ion migration is thoroughly supported by first-principles calculations, numerical simulations, and experimental verification, demonstrating its effectiveness in enhancing device performance. Additionally, we integrate this dynamic modulation with a specialized nonlinear neural network tailored to address the memristor’s inherent nonlinear photoresponse. This combination enables our spectrometer to achieve an exceptional peak wavelength accuracy of 0.18 nm and a spectral resolution of 2 nm within the 630–640 nm range. This development marks a significant advancement in the creation of compact, high-efficiency spectroscopic instruments and offers a versatile platform for applications across diverse material systems.

Applications of Drone for Crop Disease Detection and Monitoring: A Review

Crop diseases are one of the major threats to global food production. The different crop diseases result in significant yield losses, where their effective monitoring and accurate early identification techniques are considered crucial to ensure stable and reliable crop productivity and food security. Restricting and managing the disease's spread and lowering the cost of pesticides require effective plant pathogen monitoring and detection. If not used in the early stages of pathogenesis, traditional techniques such as molecular and serological methods—which are frequently employed for plant disease detection—are frequently ineffective. Conversely, drone-based remote sensing methods are highly successful in quickly detecting plant diseases in their early stages. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Drones have many potential uses in agriculture, including reducing manual labor and increasing productivity. Recent advances in drones and deep learning-based computer vision algorithms to identify crop diseases, providing early warning thereby allowing farmers to prevent costly crop failures and improve food production.

A fast region of interest algorithm for efficient data compression and improved peak detection in high-resolution mass spectrometry.

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is commonly used for identification of compounds in complex samples due to the high chromatographic and mass spectral resolution provided. In subsequent data processing workflows, it is imperative to preserve this resolution to fully exploit the data. "Region of interest" (ROI) algorithms were introduced as a better alternative to equidistant binning for compressing HRMS data because they better preserve the mass spectral resolution. In this paper, we present a new ROI algorithm that improves on the selection of contiguous m/z traces, amongst others by introducing the concept of chromatographic filter, allows for an automated approach to optimise the admissible mass-to-charge deviation (δm/z) and can be used to match ROIs across multiple samples. The algorithm was tested on a LC-HRMS dataset comprised of 21 replicate injections of a wastewater effluent extract and assessed on its ability to correctly retrieve the ROI's relative to 57 compounds and match them across all injections. In summary, it achieved a ten-fold compression rate in on-disk storage at a noise threshold of 200 counts, and the median ROI length matched the observed chromatographic peak width (12-23 points). Correct ROI matching with a mass accuracy of 9ppm was observed for 52 compounds across all 21 injections with only one compound split between two adjacent m/z traces in six runs. Overall, the new algorithm performed favourably compared to the ROI algorithm currently used in the well-established ROI-MCR (multivariate curve resolution) workflow for deconvolution of HRMS chromatographic data.

Enhanced spectral resolution in mid-infrared dual-comb spectroscopy via synchronous offset frequency tuning.

Mid-infrared dual-comb spectroscopy offers significant advantages by combining the high sensitivity of mid-infrared spectroscopy with the high spectral resolution and rapid acquisition of the dual-comb method. However, its effective resolution, constrained by the inherent comb line spacing, hinders its ability to resolve narrow absorption features, common in critical applications such as sub-Doppler spectroscopy, low-pressure gas analysis, and construction of the atmospheric profile. To address this challenge, we present a synchronous offset frequency tuning method for the mid-infrared dual-comb system to improve effective resolution far beyond comb line spacing. In our system, the mid-infrared dual-comb source is generated from a near-infrared dual-comb source and a continuous-wave pump laser via difference frequency generation in a single periodically poled lithium niobate bulk. By adjusting the phase-lock frequency of the pump laser to one of the near-infrared combs, we synchronously tune the offset frequencies of both mid-infrared combs without changing the near-infrared dual-comb source. We demonstrated that this method enabled the high resolution of overlapped spectral lines of ethane around 3000 cm-1, achieving a uniform spectral sampling interval of 10 MHz in the interleaved spectrum and a 25-fold enhancement in effective resolution. This approach allows for sub-MHz spectral resolution in mid-infrared dual-comb spectroscopy without any modifications to the data acquisition system, offering possibilities for high-precision spectral analysis.

Object-based image analysis and machine learning for mapping cashew plantations in Ariyalur district, Tamil Nadu

An object-based image analysis (OBIA) approach provides a comprehensive method for delineating homogeneous segments based on spectral characteristics, geometry, and spatial imagery structures. The present study utilizes OBIA and machine learning (ML) techniques to map cashew plantations in Ariyalur district of Tamil Nadu, India. Sentinel-2 Multi-Spectral Instrument (MSI) imagery, acquired during the 2023 Kharif season, was employed as the primary data source due to its high spatial and spectral resolution, suitable for detailed land cover mapping. The OBIA methodology involved multiresolution segmentation using eCognition software to delineate homogeneous image objects based on spectral, spatial, and contextual characteristics. Machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), were evaluated to improve classification accuracy. The SVM demonstrated the best superior performance, achieving an overall accuracy of 92.1% and a kappa coefficient of 0.85. The results underscore the effectiveness of machine learning techniques in conjunction with object-based image analysis (OBIA) for precise cashew plantation mapping while contributing to improved land use/land cover mapping, agricultural resource management, and sustainable development within the region.

Cross-Attention-Based High Spatial-Temporal Resolution Fusion of Sentinel-2 and Sentinel-3 Data for Ocean Water Quality Assessment

Current marine research that leverages remote sensing data urgently requires gridded data of high spatial and temporal resolution. However, such high-quality data is often lacking due to the inherent physical and technical constraints of sensors. A necessary trade-off therefore exists between spatial, temporal, and spectral resolution in satellite remote sensing technology: increasing spatial resolution often reduces the coverage area, thereby diminishing temporal resolution. This manuscript introduces an innovative remote sensing image fusion algorithm that combines Sentinel-2 (high spatial resolution) and Sentinel-3 (relatively high spectral and temporal resolution) satellite data. The algorithm, based on a cross-attention mechanism and referred to as the Cross-Attention Spatio-Temporal Spectral Fusion (CASTSF) model, accounts for variations in spectral channels, spatial resolution, and temporal phase among different sensor images. The proposed method enables the fusion of atmospherically corrected ocean remote sensing reflectance products (Level 2 OSR), yielding high-resolution spatial data at 10 m resolution with a temporal frequency of 1–2 days. Subsequently, the algorithm generates chlorophyll-a concentration remote sensing products characterized by enhanced spatial and temporal fidelity. A comparative analysis against existing chlorophyll-a concentration products demonstrates the robustness and effectiveness of the proposed approach, highlighting its potential for advancing remote sensing applications.

Effects of functional type and angular configuration on reflectance anisotropy of aquatic vegetation in ultra-high resolution hyperspectral imagery

ABSTRACT Imaging spectroscopy and lightweight unmanned aerial vehicles (UAVs) have revolutionized remote sensing of vegetation, by providing high spectral and spatial resolution data. Comprehensive wetland monitoring should be based on reliable mapping of biophysical aquatic vegetation parameters, which in turn requires low bias in measured spectra, e.g. minimization of reflectance anisotropy. This study aims to quantitatively investigate how sensor scan direction and solar zenith angle (SZA) affect vegetation spectra over two dominant aquatic plants, Phragmites australis and Nuphar lutea, representing different functional types (i.e. emergent and floating) with divergent canopy structure and affinity with water, using data collected with a hyperspectral (400–1000 nm range) push-broom sensor mounted on a UAV. Anisotropy factor (ANIF), mean reflectance scatterplots and regression analysis were used for assessing the magnitude of spectral anisotropy at both pseudo-leaf and canopy scales. Our findings showed more accentuated anisotropic behaviour in the visible than near-infrared domain at SZA < 60° as the scan direction approaches that of solar principal plane. The increase in reflectance in near-forward direction affects the spectra of floating-leaved N. lutea at both leaf and canopy scales, suggesting the presence of water (as a film over leaves or as canopy background) as a likely anisotropy driver. Backward hotspot is evident in canopy spectra of emergent P. australis, with driving mechanisms similar to terrestrial species (i.e. shadow-hiding). At SZA > 60°, reflectance anisotropy appears to be negligible, independent of functional type and scan direction. This research underscores the importance of considering sun-target-sensor geometry in remote sensing studies on aquatic vegetation.

Reagent-free Hyperspectral Diagnosis of SARS-CoV-2 Infection in Saliva Samples

Abstract Rapid, reagent-free pathogen-agnostic diagnostics that can be performed at the point of need are vital for preparedness against future outbreaks. Yet, many current strategies are pathogen-specific and require several reagents. We present hyperspectral sensing, using light to non-invasively measure the composition of several molecules to form a spectral signature, to overcome these barriers. To generate these spectral signatures, we present the ProSpectral™ V1, a novel, miniaturized hyperspectral platform with high spectral resolution with two mini-spectrometers. Furthermore, we developed state-of-the-art ML pipelines for near real-time analysis of spectral signatures in saliva samples. We found that we could accurately identify SARS-CoV-2 infection status in double-blinded saliva samples and demonstrate 100% accuracy on a hold out test dataset. To our knowledge, this establishes the fastest hyperspectral diagnostic platform and in a small form factor, and executable with liquid samples, without ligands or reagents, all while maintaining PCR level specificity and sensitivity

Little iLocater: paving the way for iLocater

ABSTRACT Diffraction-limited radial-velocity instruments offer a pathway towards improved precision and stability, and the exploration of new parameter spaces at high spatial and spectral resolution. However, achieving the necessary performance requires careful instrument design and considerable on-sky testing. We describe the design and construction of ‘Little iLocater’ (Lili), a compact spectrograph that has been used to validate the performance of the front-end fibre-injection system of the iLocater spectrograph. We present the design, assembly, and performance using on-sky data obtained at the Large Binocular Telescope (LBT), including extraction of spectra from standard stars, testing of the atmospheric dispersion corrector to elevations of 40°, and spatially resolved spectra from close companion systems. These results show the front-end fibre-injection system is performing as expected and is indicative of iLocater’s capabilities once installed at the LBT.

A Robust Algorithm for Estimating Total Suspended Solids (TSS) Using Sentinel-2: Case Study in Coastal Waters of Teluk Awur, Jepara, Indonesia

Total suspended solids (TSS) is an important parameter of water quality, so regular monitoring is necessary to prevent further marine pollution due to TSS. Remote sensing is one of the most effective and efficient methods to monitor TSS with cost-effective operations. The Sentinel-2 satellite is freely available to users with high spectral and spatial resolution (10m, 20m, 60m). Dynamic changes in coastal waters and their characteristics cause TSS retrieval algorithms built from available imagery having less optimal results in other water regions. This research aims to develop an empirical TSS algorithm model that specifically applies to the coastal waters of Teluk Awur, Jepara. The algorithm was developed using an empirical method through correlation between spectral values of Sentinel-2 imagery and in situ TSS values. Water sampling was conducted at 110 stations with a depth of 0.5 m on 22 July 2023 simultaneously collocated with Sentinel-2 image recording. Half of the data was used for algorithm tuning and the other half used for validation. The best regression analysis is found in the red band (B4) and the model is linear. The relatively good performance is shown by the coefficient of determination (R²) of 0.45, RMSE (3.40 mg.L-1), and MAPE (10.76%). The resulting algorithmic model was TSS (mg.L-1) =817.213*(B4)-0.959. This study shows that Sentinel-2 MSI images for TSS retrieval in the coastal waters of Teluk Awur could be applicable and the red band (B4) can be used for mapping TSS concentrations in the surrounding study area.

Sensing of gas mixtures via nonlinear interferometry

Abstract We demonstrate the use of a nonlinear interferometer with bulk lithium niobate crystal for sensing of gas mixtures over broadband mid-infrared (mid-IR) range of wavelengths. Our method utilizes spontaneous parametric down-conversion to generate correlated photon pairs in the visible and mid-IR ranges. We show that by leveraging the phase-matching properties of lithium niobate crystal, it is possible to access the ‘fingerprint’ region of greenhouse gases in the mid-IR range by detection of correlated visible light signal. Our technique enables simultaneous detection of gas mixtures with high spectral resolution and fast readout without the need for specialized mid-IR equipment. The experimental results show its applicability to molecular gas sensing, paving the way for new advancements in environmental monitoring.