Types Of Spectral Features Research Articles

Spectral features of Fe and organic carbon in estimating low and moderate concentration of heavy metals in mangrove sediments across different regions and habitat types

Soil or sediment spectral reflectance do not directly reflect information of spectrally featureless heavy metals. With mangrove sediment samples collected in five regions and four habitat types, this study aimed to explore the spectral features related to spectrally active constituents (Fe and organic carbon (OC)) in estimating low and moderate concentrations of four heavy metals (Cr, Ni, Cu and Pb). A total of 27 modeling strategies using XGBoost (extreme gradient boosting) model were compared for estimating each heavy metal: three types of spectral features (Fe, OC and heavy metal) * three spectral transformation methods (Log(1/R), first derivative (FD) and continuous wavelet transform (CWT)) * three variable selection methods (genetic algorithm (GA), successive projection algorithm (SPA) and recursive feature elimination (RFE)). The results showed that the combination of CWT and RFE was recommended for heavy metal estimation, and the ranking of model accuracy was Cu > Pb > Ni > Cr. The optimal XGBoost model was found using spectral features related to Fe for Cr (RVal2(determination coefficient of independent validation) = 0.42, RMSE (root mean square error) = 0.32 and RPIQ (the ratio of performance to interquartile range) = 1.53) and Cu estimation (RVal2 = 0.87, RMSE = 0.19 and RPIQ = 2.20), spectral features related to OC for Ni estimation (RVal2 = 0.63, RMSE = 0.26 and RPIQ = 2.38) and spectral features related to Pb itself for Pb estimation (RVal2 = 0.82, RMSE = 0.23 and RPIQ = 2.59). Moreover, the XGBoost model transferability across different habitat types outperformed that across different regions and data ranges of Fe and OC. We conclude that the modeling strategy using spectral features related to spectrally active constituents has great potentials in quantifying low and moderate concentration of sediment heavy metals, and the samples with high Fe concentration (31.62–37.29 g kg−1) are recommended to improve estimation accuracy of low and moderate concentrations of heavy metals, which could provide sampling strategy development and early warning of heavy metal pollution in mangrove ecosystem.

Banana Fusarium Wilt Disease Detection by Supervised and Unsupervised Methods from UAV-Based Multispectral Imagery

Banana Fusarium wilt (BFW) is a devastating disease with no effective cure methods. Timely and effective detection of the disease and evaluation of its spreading trend will help farmers in making right decisions on plantation management. The main purpose of this study was to find the spectral features of the BFW-infected canopy and build the optimal BFW classification models for different stages of infection. A RedEdge-MX camera mounted on an unmanned aerial vehicle (UAV) was used to collect multispectral images of a banana plantation infected with BFW in July and August 2020. Three types of spectral features were used as the inputs of classification models, including three-visible-band images, five-multispectral-band images, and vegetation indices (VIs). Four supervised methods including Support Vector Machine (SVM), Random Forest (RF), Back Propagation Neural Networks (BPNN) and Logistic Regression (LR), and two unsupervised methods including Hotspot Analysis (HA) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) were adopted to detect the BFW-infected canopies. Comparing to the healthy canopies, the BFW-infected canopies had higher reflectance in the visible region, but lower reflectance in the NIR region. The classification results showed that most of the supervised and unsupervised methods reached excellent accuracies. Among all the supervised methods, RF based on the five-multispectral-band was considered as the optimal model, with higher overall accuracy (OA) of 97.28% and faster running time of 22 min. For the unsupervised methods, HA reached high and balanced OAs of more than 95% based on the selected VIs derived from the red and NIR band, especially for WDRVI, NDVI, and TDVI. By comprehensively evaluating the classification results of different metrics, the unsupervised method HA was recommended for BFW recognition, especially in the late stage of infection; the supervised method RF was recommended in the early stage of infection to reach a slightly higher accuracy. The results found in this study could give advice for banana plantation management and provide approaches for plant disease detection.

Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.

Prediction of Oleic Acid Content of Rapeseed Using Hyperspectral Technique

In order to detect the oleic acid content of rapeseed quickly and accurately, we propose, in this paper, an artificial BP neural networks based model for predicting oleic acid content by using rapeseed’s hyperspectral information. Four types of spectral features are selected for our investigation, namely multifractal index, sensitive band, trilateral parameters, and spectral index. Both univariate variable and multiple variables are considered as our model input. The result shows that the combined feature has higher precision and better stability than when using a single parameter. An interesting finding shows that the combined feature involving multifractal parameters can significantly improve the model performance. Taking the combined feature {MF-h(0), SB-DR574, SPI-NDSI(R575, R576)} as the model input, the constructed BP (back propagation) neural networks model has the highest precision, with the coefficient of determination (R2) 0.8753, root mean square error (RMSE) 1.0301, and relative error (RE) 1.047%. This result provides some experience for the rapid detection of rapeseed’s oleic acid content.

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods.

Rice diseases are major threats to rice yield and quality. Rapid and accurate detection of rice diseases is of great importance for precise disease prevention and treatment. Various spectroscopic techniques have been used to detect plant diseases. To rapidly and accurately detect three different rice diseases [leaf blight (Xanthomonas oryzae pv. Oryzae), rice blast (Pyricularia oryzae), and rice sheath blight (Rhizoctonia solani)], three spectroscopic techniques were applied, including visible/near-infrared hyperspectral imaging (HSI) spectra, mid-infrared spectroscopy (MIR), and laser-induced breakdown spectroscopy (LIBS). Three different levels of data fusion (raw data fusion, feature fusion, and decision fusion) fusing three different types of spectral features were adopted to categorize the diseases of rice. Principal component analysis (PCA) and autoencoder (AE) were used to extract features. Identification models based on each technique and different fusion levels were built using support vector machine (SVM), logistic regression (LR), and convolution neural network (CNN) models. Models based on HSI performed better than those based on MIR and LIBS, with the accuracy over 93% for the test set based on PCA features of HSI spectra. The performance of rice disease identification varied with different levels of fusion. The results showed that feature fusion and decision fusion could enhance identification performance. The overall results illustrated that the three techniques could be used to identify rice diseases, and data fusion strategies have great potential to be used for rice disease detection.

Diamonds Certify Themselves: Multivariate Statistical Provenance Analysis

The country or mine of origin is an important economic and societal issue inherent in the diamond industry. Consumers increasingly want to know the provenance of their diamonds to ensure their purchase does not support inhumane working conditions. Governments around the world reduce the flow of conflict diamonds via paper certificates through the Kimberley Process, a United Nations mandate. However, certificates can be subject to fraud and do not provide a failsafe solution to stopping the flow of illicit diamonds. A solution tied to the diamonds themselves that can withstand the cutting and manufacturing process is required. Here, we show that multivariate analysis of LIBS (laser-induced breakdown spectroscopy) diamond spectra predicts the mine of origin at greater than 95% accuracy, distinguishes between natural and synthetic stones, and distinguishes between synthetic stones manufactured in different laboratories by different methods. Two types of spectral features, elemental emission peaks and emission clusters from C-N and C-C molecules, are significant in the analysis, indicating that the provenance signal is contained in the carbon structure itself rather than in inclusions.

Quantifying Chlorophyll Fluorescence Parameters from Hyperspectral Reflectance at the Leaf Scale under Various Nitrogen Treatment Regimes in Winter Wheat

Chlorophyll fluorescence (ChlF) parameters, especially the quantum efficiency of photosystem II (PSII) in dark- and light-adapted conditions (Fv/Fm and Fv’/Fm’), have been used extensively to indicate photosynthetic activity, physiological function, as well as healthy and early stress conditions. Previous studies have demonstrated the potential of applying hyperspectral data for the detection of ChlF parameters in vegetation. However, the performance of spectral features that have been documented to estimate ChlF is not ideal and is poorly understood. In this study, ChlF parameters and leaf reflectance were collected in two field experiments involving various wheat cultivars, nitrogen (N) applications, and plant densities, during the growing seasons of 2014 to 2015 and 2015 to 2016. Three types of spectral features, including vegetation indices (VIs), red edge position (REP), and wavelet features, were used to quantify ChlF parameters Fv/Fm and Fv’/Fm’. The results indicated that traditional chlorophyll fluorescence vegetation indices (ChlF VIs), such as the curvature index (CUR) and D705/D722 were capable of detecting Fv/Fm and Fv’/Fm’ under various scenarios. However, the wavelet-based REP (WREP-S4) and the wavelet feature (WF) (704 nm, scale 4) yielded higher accuracy than other spectral features in calibration and validation datasets. Moreover, the bands used to calculate WREP-S4 and WF (704 nm, scale 4) were all centered in the red edge region (680 to 760 nm), which highlighted the role of the red edge region in tracking the change of active ChlF signal. Our results are supported by previous studies, which have shown that the red edge region is vital for estimating the chlorophyll content, and also the ChlF parameters. These findings could help to improve our understanding of the relationships among active ChlF signal and reflectance spectra.

Theoretical Aspects of the Cross Effect Enhancement of Nuclear Polarization under Static Dynamic Nuclear Polarization Conditions.

In this study, we perform quantum calculations of the spin dynamics of a small spin system that includes nine coupled electrons and one nucleus placed in an external magnetic field and exposed to microwave irradiation. This is an extension of a previous work in which we have demonstrated on a system of 11 coupled electron spins the dynamics of the electron polarizations composing the electron paramagnetic resonance (EPR) line during static dynamic nuclear polarization (DNP) experiments. There we have shown that the electron polarizations are determined by a spectral diffusion process, induced by the dipolar interaction and cross-relaxation. Additionally, we showed that a distinction had to be made between strong and weak dipolar-coupled systems relative to the inhomogeneity of the EPR line with only the first behaving according to the thermal mixing DNP (with two electron spin temperatures) description. The EPR spectra in the weak and strong dipolar interaction cases show different types of spectral features. In the extended spin system, we again make a distinction between weak and strong electron-electron interactions and show that the DNP spectra for the two cases are different in nature but that the DNP spectra can be derived in all cases from the EPR line shapes using the indirect cross effect.

Hydration of potassium iodide dimer studied by photoelectron spectroscopy and ab initio calculations.

We measured the photoelectron spectra of (KI)2-(H2O)n (n = 0-3) and conducted ab initio calculations on (KI)2-(H2O)n anions and their corresponding neutrals up to n = 6. Two types of spectral features are observed in the experimental spectra of (KI)2-(H2O) and (KI)2-(H2O)2, indicating that two types of isomers coexist, in which the high EBE feature corresponds to the hydrated chain-like (KI)2- while the low EBE feature corresponds to the hydrated pyramidal (KI)2-. In (KI)2-(H2O)3, the (KI)2- unit prefers a pyramidal configuration, and one of the K-I distances is elongated significantly, thus a K atom is firstly separated out from the (KI)2- unit. As for the neutrals, the bare (KI)2 has a rhombus structure, and the structures of (KI)2(H2O)n are evolved from the rhombus (KI)2 unit by the addition of H2O. When the number of water molecules reaches 4, the K-I distances have significant increment and one of the I atoms prefers to leave the (KI)2 unit. The comparison of (KI)2(H2O)n and (NaI)2(H2O)n indicates that it is slightly more difficult to pry apart (KI)2 than (NaI)2 via hydration, which is in agreement with the lower solubility of KI compared to that of NaI.

Localized electronic states at grain boundaries on the surface of graphene and graphite

Recent advances in large-scale synthesis of graphene and other 2D materials have underscored the importance of local defects such as dislocations and grain boundaries (GBs), and especially their tendency to alter the electronic properties of the material. Understanding how the polycrystalline morphology affects the electronic properties is crucial for the development of applications such as flexible electronics, energy harvesting devices or sensors. We here report on atomic scale characterization of several GBs and on the structural-dependence of the localized electronic states in their vicinity. Using low temperature scanning tunneling microscopy and spectroscopy, together with tight binding and ab initio numerical simulations we explore GBs on the surface of graphite and elucidate the interconnection between the local density of states and their atomic structure. We show that the electronic fingerprints of these GBs consist of pronounced resonances which, depending on the relative orientation of the adjacent crystallites, appear either on the electron side of the spectrum or as an electron-hole symmetric doublet close to the charge neutrality point. These two types of spectral features will impact very differently the transport properties allowing, in the asymmetric case to introduce transport anisotropy which could be utilized to design novel growth and fabrication strategies to control device performance.

Evolution of RamanGandG′(2D) modes in folded graphene layers

Bernal- and non-Bernal-stacked graphene layers have been systematically studied by Raman imaging and spectroscopy. Two dominant Raman modes, G and G' (or 2D) of folded graphene layers exhibit three types of spectral features when interlayer lattice mismatches, defined by a rotational angle varies. Among these folded graphene layers, the most interesting one is the folded graphene layers that present an extremely strong G mode enhanced by a twist-induced Van Hove singularity. The evolution of Raman G and G' modes of such folded graphene layers are probed by changing the excitation photon energies. For the first time, doublet splitting of the G' mode in folded double-layer (1 + 1) and of the G mode in folded tetra-layer (2 + 2) graphene are clearly observed and discussed. The G' mode splitting in folded double-layer graphene is attributed to the coexistence of inner and outer scattering processes and the trigonal warping effect as well as further downwards bending of the inner dispersion branch at visible excitation energy. While the two peaks of the G mode in folded tetra-layer graphene are assigned to Raman-active mode (E2g) and lattice mismatch activated infrared-active mode (E1u), which is further verified by the temperature-dependent Raman measurements. Our study provides a summary and thorough understanding of Raman spectra of Bernal- and non-Bernal-stacked graphene layers and further demonstrates the versatility of Raman spectroscopy for exploiting electronic band structures of graphene layers.

Estimation of phytoplankton size fractions based on spectral features of remote sensing ocean color data

AbstractThrough its influence on the structure of pelagic ecosystems, phytoplankton size distribution (pico‐, nano‐, and micro‐plankton) is believed to play a key role in “the biological pump.” In this paper, an algorithm is proposed to estimate phytoplankton size fractions (PSF) for micro‐, nano‐, and pico‐plankton (fm,fn, andfp, respectively) from the spectral features of remote‐sensing data. From remote‐sensing reflectance spectrum (Rrs(λ)), the algorithm constructs four types of spectral features: a normalizedRrs(λ), band ratios, continuum‐removed spectra, and spectral curvatures. Using support vector machine recursive feature elimination, the algorithm ranks the constructed spectral features andRrs(λ) according to their sensitivities to PSF which is then regressed against the sensitive spectral features through support vector regression. The algorithm is validated with (1) simulatedRrs(λ) and PSF, and (2)Rrs(λ) obtained by Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) and PSF determined from High‐Performance Liquid Chromatography (HPLC) pigments. The validation results show the overall effectiveness of the algorithm in estimating PSF, withR2of (1) 0.938 (fm) for the simulated SeaWiFS data set; and (2) 0.617 (fm), 0.475 (fn), and 0.587 (fp) for the SeaWiFS satellite data set. The validation results also indicate that continuum‐removed spectra and spectral curvatures are the dominant spectral features sensitive to PSF with their wavelengths mainly centered on the pigment‐absorption domain. Global spatial distributions offm,fn, andfpwere mapped with monthly SeaWiFS images. Overall, their biogeographical distributions are consistent with our current understanding that pico‐plankton account for a large proportion of total phytoplankton biomass in oligotrophic regions, nano‐plankton in transitional areas, and micro‐plankton in high‐productivity regions.

Observation of EUV spectra from gadolinium and neodymium ions in the Large Helical Device

We have observed extreme ultraviolet (EUV) spectra from highly charged gadolinium and neodymium ions in optically thin plasmas produced in the Large Helical Device at the National Institute for Fusion Science. Time evolutions of EUV spectra in the 6–9 nm region following injection of a solid pellet into a hydrogen plasma were measured by a grazing incidence spectrometer with a frame rate of 0.2 s. Three different types of spectral features have been observed depending on the electron temperature. Discrete spectral lines of Ni- and Cu-like gadolinium and neodymium ions were identified when the peak electron temperature was around 2 keV, while some lines of Ag-like gadolinium ions were found in the plasma with temperatures below 240 eV when an unusual hollow plasma was formed. Under the intermediate temperature, a quasi-continuum spectral feature arising from an unresolved transition array from open 4p/4d subshell ions is predominant.

EUV spectra from highly charged tin ions observed in low density plasmas in LHD

In order to meet the increasing needs for experimental databases of tin (Sn) extreme ultraviolet (EUV) spectra relevant to the next generation EUV lithography, we have measured EUV spectra from highly charged tin ions in low density plasmas produced in the Large Helical Device (LHD) at the National Institute for Fusion Science. A small amount of tin was introduced into a background high temperature and low density hydrogen plasma by injecting a pellet. The EUV spectra were monitored by a grazing incidence spectrometer whose wavelength resolution is about 0.01 nm. Two different types of spectral features were found in the LHD depending on the discharge condition. The well known dense spectral structure around 13.5 nm is measured when the plasma is rapidly cooled and approaching radiative collapse after the pellet injection, while the sparse spectrum with several unidentified discrete lines from 13.8-14.6 nm is observed if the plasma is cooled more slowly and higher temperature is maintained for a while. According to comparison with other charge-separated experimental data, the dominant charge states in the former case are Sn11+-Sn14+. The latter case may be explained by considering the spectral lines from charge states higher than Sn19+.

Probing matrix isolated SiO molecular clusters by X-ray absorption spectroscopy

TheK absorption edge of Si in matrix isolated SiO molecular clusters was studied for various dilutionsR(Ar/SiO). The spectra from the clusters at different dilutions show a dramatic evolution in their relative intensity. Two types of spectral features from Si atoms present in the clusters could be detected in the spectra. The first is due to the Si atoms which are tetrahedrally coordinated to Si and O atoms responsible for the presence of different Si oxidation states. The Si+, Si3+ and Si4+ oxidation states in SiO clusters and in bulk were readily identified. The Si2+ oxidation state whose abundance in SiO clusters is predicted by structural considerations is not clearly observed. The second type of feature, whose intensity decreases with the dilution R, can be attributed to the presence of Si atoms possessing unsaturated bonds. In order to explain this the clustering of 4–6 SiO molecules is simulated to yield different tetrahedral bonding microstructures in which the well coordinated as well as under coordinated Si atoms are present. The behaviour of the spectral features resulting from these microstructures is discussed.

Spatial Emission Properties of a Surface-Wave-Sustained Plasma (Surfatron) in Helium

Spatially resolved measurements of plasma-emission sources can greatly aid in their application to atomic-emission spectrometry. In this study, detailed two-dimensional emission profiles of a plasma sustained by electromagnetic surface waves from a surfatron have been measured with an imaging spectrometer. In helium, and under the operating conditions described, the plasma is found to possess a “doughnut” shape much like the inductively coupled plasma (ICP) in argon. Emission from the following types of spectral features was found to be confined to the plasma annulus: (1) He(I) lines; (2) molecular bands; (3) background continuum; (4) difficult-to-excite transitions from nonmetallic analytes. In contrast, two-dimensional profiles for metallic analytes (introduced in the form of an aerosol from a glass-frit nebulizer) show localized maxima along the central axis of the plasma. Spatially resolved measurements have suggested ways in which the surfatron can be employed to advantage for atomic emission spectrometry. Possible excitation mechanisms to account for the spatial properties of the surfatron are also offered.

HF‐enhanced ion and plasma line spectra with two pumps

Spectra of the HF‐enhanced ion line and upshifted and downshifted plasma lines were obtained by a multiple‐pulse technique (9 pulses) and by a technique using pulse‐to‐pulse correlation (512 pulses) with a short (about 1 ms) interpulse period for high frequency resolution. Observations using the multiple‐pulse technique suggest the presence of two different types of spectral features. One type has a spectral width typically greater than 1 kHz, and another type a width much less than 400 Hz, the frequency resolution of the multiple‐pulse technique used. Observations using pulses with a short interpulse period show the width of the narrow spectral features to be in the 20‐ to 60‐Hz range. The results are in good qualitative agreement with predictions of the linear theory of parametric instabilities excited by two pumps. Observations of the narrow spectral features can be used to measure the line‐of‐sight component of ionospheric drift velocities of the electron gas with an accuracy of about 2 m/s.