Spectral Information Research Articles

Stimulated X-ray emission spectroscopy.

We describe an emerging hard X-ray spectroscopy technique, stimulated X-ray emission spectroscopy (S-XES). S-XES has the potential to characterize the electronic structure of 3d transition metal complexes with spectral information currently not reachable and might lead to the development of new ultrafast X-ray sources with properties beyond the stateoftheart. S-XES has become possible with the emergence of X-ray free-electron lasers (XFELs) that provide intense femtosecond X-ray pulses that can be employed to generate a population inversion of core-hole excited states resulting in stimulated X-ray emission. We describe the instrumentation, the various types of S-XES, the potential applications, the experimental challenges, and the feasibility of applying S-XES to characterize dilute systems, including the Mn4Ca cluster in the oxygen evolving complex of photosystem II.

Research on a new multiple-screening method for laser-induced plasma spectroscopy utilizing Lorentz

In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566 % and 0.1024 %. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

Learning visual stimulus-evoked EEG manifold for neural image classification

Visual neural decoding, namely the ability to interpret external visual stimuli from patterns of brain activity, is a challenging task in neuroscience research. Recent studies have focused on characterizing patterns of activity across multiple neurons that can be described in terms of population-level features. In this study, we combine spatial, spectral, and temporal features to achieve neural manifold classification capable to characterize visual perception and to simulate the working memory activity in the human brain. We treat spatio-temporal and spectral information separately by means of custom deep learning architectures based on Riemann manifold and the two-dimensional EEG spectrogram representation. In addition, a CNN-based classification model is used to classify visual stimulus-evoked EEG signals while viewing the 11-class (i.e., all-black plus 0-9 digit images) MindBigData Visual MNIST dataset. The effectiveness of the proposed integration strategy is evaluated on the stimulus-evoked EEG signal classification task, achieving an overall accuracy of 86%, comparable to state-of-the-art benchmarks.

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.

Introducing hafnium to atomically small- and medium-sized tin clusters (HfSnn0/-/2- (n = 4–17)): A computational investigation of geometrical and growth behavior, spectral properties, electronic configuration and thermochemistry

The global and local minimum configurations of single Hf atom doped Sn clusters are conducted via density function theory (DFT) combined with artificial bee colony algorithm (ABCluster). Furthermore, DFT method is also used to systematically investigate on their structural growth evolution, spectral and electronic information, thermochemical properties following the size of tin clusters doped Hf atom. Structurally, the ground-state geometries of neutral, anion and di-anion are discovered that, from n = 4, the number of Sn atoms in cluster, HfSnn0/-/2- adsorb additional Sn atom on the prior architecture one by one until forming n = 17 for HfSnn-10/-, as well as forming n = 16 for HfSnn-12-. And for the HfSn110/- and HfSn102- as beginning the species veritably develop sealed architectures. The strongest vibrational modes of sealed nanoclusters are stretching modes of Hf atom with infrared actives and breathing modes of the Sn cage framework with Raman actives, respectively. The natural population analysis (NPA) elucidates the stronger relationship between the Hf atoms and the tin frameworks in sealed clusters than that in unsealed clusters. The results of thermochemical properties, molecular orbital shell (MOs), adaptive natural density partitioning (AdNDP) and ultraviolet visible absorption spectrum (UV–Vis) indicate that, the HfSn16 with high symmetry of Td exhibits thermochemical stability and optoelectronic properties, which is utilized potentially as zero-dimensional unit of self-assembling fluorescent nanomaterials.

Unsupervised Change Detection in HR Remote Sensing Imagery Based on Local Histogram Similarity and Progressive Otsu

Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolution remote sensing imagery. This method has three characteristics: (1) Extended center-symmetric local binary pattern (XCS-LBP) is used to extract image features to emphasize spatial context information in initial change detection. Then, spectral information is combined to improve the accuracy of change detection. (2) The local histogram distance of XCS-LBP features is used as the change vector to improve the expression of change information. (3) A progressive Otsu method is developed for threshold segmentation of the change vector to reduce the false detection rate. Four datasets with different landscape complexities and seven state-of-the-art unsupervised change detection methods were used to test the performance of the proposed method. Quantitative results showed that the proposed method reduced the false detection rate and improved the accuracy of the detection of land cover changes. The F1 score achieved by the proposed method reached 0.8688, 0.8867, 0.7725, and 0.6634, respectively, which are higher than the highest corresponding F1 score achieved by the benchmark methods (0.8533, 0.8549, 0.6545, and 0.5895, respectively).

Multiparameter Sensing of Oxygen and pH at Biological Interfaces via Hyperspectral Imaging of Luminescent Sensor Nanoparticles.

Chemical dynamics in biological samples are seldom stand-alone processes but represent the outcome of complicated cascades of interlinked reaction chains. In order to understand these processes and how they correlate, it is important to monitor several parameters simultaneously at high spatial and temporal resolution. Hyperspectral imaging is a promising tool for this, as it provides broad-range spectral information in each pixel, enabling the use of multiple luminescent indicator dyes, while simultaneously providing information on sample structures and optical properties. In this study, we first characterized pH- and O2-sensitive indicator dyes incorporated in different polymer matrices as optical sensor nanoparticles to provide a library for (hyperspectral) chemical imaging. We then demonstrate the successful combination of a pH-sensitive indicator dye (HPTS(DHA)3), an O2-sensitive indicator dye (PtTPTBPF), and two reference dyes (perylene and TFPP), incorporated in polymer nanoparticles for multiparameter chemical imaging of complex natural samples such as green algal biofilms (Chlorella sorokiniana) and seagrass leaves (Zostera marina) with high background fluorescence. We discuss the system-specific challenges and limitations of our approach and further optimization possibilities. Our study illustrates how multiparameter chemical imaging with hyperspectral read-out can now be applied on natural samples, enabling the alignment of several chemical parameters to sample structures.

Relationships of Fire Rate of Spread with Spectral and Geometric Features Derived from UAV-Based Photogrammetric Point Clouds

Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models.

Attention 3D central difference convolutional dense network for hyperspectral image classification.

Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

Saliency-Guided Sparse Low-Rank Tensor Approximation for Unsupervised Anomaly Detection of Hyperspectral Remote Sensing Images

Hyperspectral anomaly detection can separate sparse anomalies from the low-rank background component under an unsupervised behavior due to sufficient spectral information. Therefore, hyperspectral image anomaly detection technology has great application potential and value in public security and national defense. Currently, most existing models attempt to detect anomalous targets with a sparsity prior, without further considering the visual saliency of the targets themselves. To tackle this issue, this paper proposes a saliency-guided sparse low-rank tensor approximation model, called SSLR, to detect anomalous targets from hyperspectral remote sensing images in an unsupervised manner. Specifically, we first explore the saliency information of each pixel for regularizing the sparse anomaly matrix. We then suggest a three-directional tensor nuclear norm to obtain a low-rank background to characterize the background component. We solve the SSLR optimization problem by an efficient alternating direction method of multipliers framework. Experiments conducted on benchmark hyperspectral datasets demonstrate that the proposed SSLR outperforms some state-of-the-art anomaly detection methods.

An encoder–decoder network for land cover classification using a fusion of aerial images and photogrammetric point clouds

Land cover information is becoming more important in urban planning, change detection, and management. The fusion of point clouds and images increases the accuracy of land use classification by utilising the advantages of both modalities. Similar structures such as buildings and roads, low and high vegetation, and impervious and bare regions are not too much discriminative. Models fail to discriminate these classes leading to misclassifications, false detections, and unreliable land cover maps. Therefore, this research proposes the fusion of dense point clouds and multi-spectral images based on a dual-stream deep convolutional model by adding vegetation and elevation information to spectral information. To fuse both modalities' features, a dual-stream deep neural network based on Deeplabv3+ architecture is implemented. In addition, the Xception (Extreme Inception) model is considered as a backbone and feature extractor. The model performance is evaluated with F1-Score and Overall Accuracy. 93.4% Overall Accuracy and F1-Score are achieved after adding height and vegetation information to the model. Results indicate improvements in all indexes, meaning that data fusion with the proposed model outperforms the existing state-of-the-art models.

Improved Landsat Operational Land Imager (OLI) Cloud and Shadow Detection with the Learning Attention Network Algorithm (LANA)

Landsat cloud and cloud shadow detection has a long heritage based on the application of empirical spectral tests to single image pixels, including the Landsat product Fmask algorithm, which uses spectral tests applied to optical and thermal bands to detect clouds and uses the sun-sensor-cloud geometry to detect shadows. Since the Fmask was developed, convolutional neural network (CNN) algorithms, and in particular U-Net algorithms (a type of CNN with a U-shaped network structure), have been developed and are applied to pixels in square patches to take advantage of both spatial and spectral information. The purpose of this study was to develop and assess a new U-Net algorithm that classifies Landsat 8/9 Operational Land Imager (OLI) pixels with higher accuracy than the Fmask algorithm. The algorithm, termed the Learning Attention Network Algorithm (LANA), is a form of U-Net but with an additional attention mechanism (a type of network structure) that, unlike conventional U-Net, uses more spatial pixel information across each image patch. The LANA was trained using 16,861 512 × 512 30 m pixel annotated Landsat 8 OLI patches extracted from 27 images and 69 image subsets that are publicly available and have been used by others for cloud mask algorithm development and assessment. The annotated data were manually refined to improve the annotation and were supplemented with another four annotated images selected to include clear, completely cloudy, and developed land images. The LANA classifies image pixels as either clear, thin cloud, cloud, or cloud shadow. To evaluate the classification accuracy, five annotated Landsat 8 OLI images (composed of >205 million 30 m pixels) were classified, and the results compared with the Fmask and a publicly available U-Net model (U-Net Wieland). The LANA had a 78% overall classification accuracy considering cloud, thin cloud, cloud shadow, and clear classes. As the LANA, Fmask, and U-Net Wieland algorithms have different class legends, their classification results were harmonized to the same three common classes: cloud, cloud shadow, and clear. Considering these three classes, the LANA had the highest (89%) overall accuracy, followed by Fmask (86%), and then U-Net Wieland (85%). The LANA had the highest F1-scores for cloud (0.92), cloud shadow (0.57), and clear (0.89), and the other two algorithms had lower F1-scores, particularly for cloud (Fmask 0.90, U-Net Wieland 0.88) and cloud shadow (Fmask 0.45, U-Net Wieland 0.52). In addition, a time-series evaluation was undertaken to examine the prevalence of undetected clouds and cloud shadows (i.e., omission errors). The band-specific temporal smoothness index (TSIλ) was applied to a year of Landsat 8 OLI surface reflectance observations after discarding pixel observations labelled as cloud or cloud shadow. This was undertaken independently at each gridded pixel location in four 5000 × 5000 30 m pixel Landsat analysis-ready data (ARD) tiles. The TSIλ results broadly reflected the classification accuracy results and indicated that the LANA had the smallest cloud and cloud shadow omission errors, whereas the Fmask had the greatest cloud omission error and the second greatest cloud shadow omission error. Detailed visual examination, true color image examples and classification results are included and confirm these findings. The TSIλ results also highlight the need for algorithm developers to undertake product quality assessment in addition to accuracy assessment. The LANA model, training and evaluation data, and application codes are publicly available for other researchers.

“Flower power”: How flowering affects spectral diversity metrics and their relationship with plant diversity

Biodiversity monitoring is constrained by cost- and labour-intensive field sampling methods. Increasing evidence suggests that remotely sensed spectral diversity (SD) is linked to plant diversity, holding promise for monitoring applications. However, studies testing such a relationship reported conflicting findings, especially in challenging ecosystems such as grasslands, due to their variety and high temporal dynamism. It follows that a thorough investigation of the key factors influencing these relationships, such as the metrics applied (i.e., continuous, categorical) and phenology (e.g., flowering), is necessary. The present study aims to assess the effect of flowering on the applicability of six different SD metrics for plant diversity monitoring at the local scale and to investigate how spatial resolution affects the results. Taxonomic diversity was calculated based on data collected in 159 plots of 1.5 m × 1.5 m with experimental mesic grassland communities. Spectral information was collected using a UAV-borne sensor measuring reflectance across six bands in the visible and near-infrared range at ∼2 cm spatial resolution. Our results showed that, in the presence of flowering, the relationship between SD and plant diversity is significant and positive only when SD is calculated using categorical metrics. Despite the observed significance, the variance explained by the models was very low, with no evident differences when resampling spectral data to coarser pixel sizes. Such findings suggest that new insights into the possible confounding effects on the SD-plant diversity relationship in grassland communities are needed to use SD for monitoring purposes.

Advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection

In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in maintaining and preserving the integrity of wind turbine blades. Timely and accurate detection and classification of blade faults, encompassing issues such as cracks, erosion, and ice buildup, are imperative to uphold wind turbines' ongoing efficiency and safety. This study introduces an inventive approach that amalgamates hyperspectral imaging and 3D Convolutional Neural Networks (CNNs) to augment the precision and efficiency of wind turbine blade fault detection and classification. Hyperspectral imaging is harnessed to capture comprehensive spectral information from blade surfaces, facilitating exact fault identification. The process is streamlined through Incremental Principal Component Analysis (IPCA), reducing data dimensions while maintaining integrity. The 3D CNN model demonstrates remarkable performance, achieving high accuracy in detecting all fault categories in full-band hyperspectral images. The model retains high accuracy even with dimensionality reduction to 20 spectral bands. The reduced processing time of the 20-band image enhances the practicality of real-world applications, thereby reducing downtime and maintenance expenditures. This research represents a significant advancement in wind turbine blade inspection, contributing to the sustainability and dependability of wind energy systems and furthering the cause of a cleaner and more sustainable energy future as part of the broader fight against climate change.

Gradient High-Q Dielectric Metasurfaces for Broadband Sensing and Control of Vibrational Light-Matter Coupling.

Surface-enhanced infrared absorption spectroscopy (SEIRA) has emerged as a powerful technique for ultrasensitive chemical-specific analysis. SEIRA can be realized by employing metasurfaces that can enhance light-matter interactions in the spectral bands of molecular vibrations. Increasing sample complexity emphasizes the need for metasurfaces that can operate simultaneously at different spectral bands, both accessing rich spectral information over a broad band, and resolving subtle differences in the absorption fingerprints through narrow-band resonances. Here, a novel concept of resonance-gradient metasurfaces is introduced, where the required spectral selectivity is achieved via local high-quality-factor (high-Q) resonances, while the continuous coverage of a broad band is enabled by the gradual adjustment of the unit-cell dimensions along the planar structure. The highly tailorable design of the gradient metasurfaces provides flexibility for shaping the spectral sampling density to match the relevant bands of target analytes while keeping a compact device footprint. The versatility of the gradient metasurfaces is demonstrated through several sensing scenarios, including polymer mixture deconvolution, detecting a multistep bioassay, and identification of the onset of vibrational strong coupling regime. The proposed gradient-resonance platform significantly contributes to the rapidly evolving landscape of nonlocal metasurfaces, enabling applications in molecular detection and analysis of fundamental light-matter interaction phenomena.

Rapid and cost‐effective assessment of nutrients in pistachio (Pistacia vera L.) leaves through Fourier transform near‐infrared spectroscopy (FT‐NIRS)

AbstractBackgroundPistachio nuts provide many health benefits in human diet. Nutrient levels in plant leaves and fertilizer schedules are determined based on traditional soil and leaf chemical analyses. However, these methods require additional labor, time, and cost, which is why most farmers do not prefer them and cannot detect nutrient deficiencies in time. Fast, easy‐to‐use and low‐cost nutrient level assessment techniques are needed.AimThis study aims to explore the viability of near‐infrared reflectance spectroscopy (NIRS) as a fast, user friendly, and cost effective technique for evluating the major macro‐ and micronutrient contents of dried and ground pistachio leaf sample.MethodsThe feasibility of NIRS for estimating nutrient contents was investigated by analyzing samples from 200 pistachio trees. Dried and ground pistachio leaves were subjected to NIRS analysis. PLSR (Partial least square regression) analyses were performed to develop nutrient content prediction models using spectral information of samples.ResultsIt was found that the NIRS system had a very good potential to estimate the K, Ca, Cu, and Mg contents of the leaf samples (R2 > 0.80). It was also found that Fe and Mn concentrations could also be estimated with good accuracy (R2 = 0.70–0.80). However, the NIRS system did not give good results for the prediction of N, P, and Zn (R2 < 0.40).ConclusionIn conclusion, the NIRS system can be used to quickly, easily, and economically assess the K, Ca, Cu, Mg, Fe, and Mn contents of dried and ground pistachio leaves. This technique has the potential to improve nutrient management practices in pistachio farming within a sustainable and environmentally conscious framework. Fourier transform NIRS (FT‐NIRS) can provide valuable insights by complementing rather than replacing traditional chemical analysis. Laboratory analysis is still required for definitive nutrient content information, but FT‐NIRS can significantly reduce the reliance on such analysis, thereby mitigating the environmental impact caused by the excessive use of chemical fertilizers and minimizing the health risks to laboratory staff. In addition, the rapid information‐gathering capabilities of the FT‐NIRS can be emphasized.

On-Chip Quantum Information Processing with Distinguishable Photons.

Multiphoton interference is at the heart of photonic quantum technologies. Arrays of integrated cavities can support bright sources of single photons with high purity and small footprint, but the inevitable spectral distinguishability between photons generated from nonidentical cavities is an obstacle to scaling. In principle, this problem can be alleviated by measuring photons with high timing resolution, which erases spectral information through the time-energy uncertainty relation. Here, we experimentally demonstrate that detection can be implemented with a temporal resolution sufficient to interfere photons detuned on the scales necessary for cavity-based integrated photon sources. By increasing the effective timing resolution of the system from 200 to 20ps, we observe a 20% increase in the visibility of quantum interference between independent photons from integrated microring resonator sources that are detuned by 6.8GHz. We go on to show how time-resolved detection of nonideal photons can be used to improve the fidelity of an entangling operation and to mitigate the reduction of computational complexity in boson sampling experiments. These results pave the way for photonic quantum information processing with many photon sources without the need for active alignment.

A research on the effect of plasma spectrum collection device on LIBS spectral intensity

Only a small amount of spectral information is collected because the collection solid angle of the optical fiber probe and lens is very limited when collecting spectral information. To overcome this limitation, this study presents a novel method for acquiring plasma spectral information from various spatial directions. A parabolic-shaped plasma spectral collection device (PSCD) is employed to effectively collect more spectral information into the spectrometer, thereby enhancing the overall spectral intensity. The research objects in this study were soil samples containing different concentrations of heavy metals Pb, Cr, and Cd. The results indicate that the PSCD significantly enhances the spectral signal, with an enhancement rate of up to 45%. Moreover, the signal-to-noise ratio also increases by as much as 36%. Simultaneously, when compared to the absence of a device, it is found that there is no significant variation in plasma temperature when the PSCD is utilized. This observation eliminates the impact of the spatial effect caused by the PSCD on the spectral intensity. Consequently, a concentration-spectral intensity relationship curve is established under the PSCD. The results revealed that the linear fitting R 2 for Pb, Cr, and Cd increased by 0.011, 0.001, and 0.054, respectively. Additionally, the limit of detection (LOD) decreased by 0.361 ppm, 0.901 ppm, and 0.602 ppm, respectively. These findings indicate that the spectral enhancement rate elevates with the increase in heavy metal concentration. Hence, the PSCD can effectively enhance the spectral intensity and reduce the detection limit of heavy metals in soil.

QuadWave1D: An optimized quadratic formulation for spectral prediction of coastal waves

Spectral information of coastal waves and the associated statistical parameters (e.g., the significant wave height and mean wave period) over large spatial scales is essential for many applications (e.g., coastal safety assessments, coastal management and developments, etc.). This demand explains the necessity for accurate yet effective models. A well-known efficient modelling approach is the quadratic approach (often referred to as frequency-domain models, weakly nonlinear mild-slope models, amplitude models, etc.). The efficiency of this approach is achieved through modelling reduction of the original governing equations (e.g., Euler equations). Most significantly, wave nonlinearity is described solely by a single quadratic mode-coupling term. Therefore, doubts arise with regard to the predictive capabilities of the quadratic approach to reliably describe the nonlinear development of waves in the coastal environment where nonlinearity is typically significant. This study attempts to push the limit of the prediction capabilities of nonlinear coastal waves based on the quadratic approach. To this end, an optimization process is proposed, striving to extract the quadratic formulation which describes most adequately nonlinear wave developments over water depths and bathymetrical structures which characterize the coastal environment. The outcome is the model QuadWave1D: a fully dispersive quadratic model for coastal wave prediction in one-dimension. Based on a wide set of examples (including monochromatic, bichromatic and irregular wave conditions) and comparing to other representative quadratic formulations, it is found that QuadWave1D presents superior predictive capabilities of both the sea-swell components and the infragravity field.

Improving SSC detection accuracy of cherry tomatoes by feature synergy and complementary spectral bands combination

The visible/near-infrared (VIS/NIR) spectroscopy technique has been extensively employed for the non-destructive detection of soluble solids content (SSC) in fruit. However, some existing algorithms and modeling optimization methods for improving the detection accuracy of SSC may be applicable to specific conditions and not universal. To break through this bottleneck problem, we propose a potentially universal strategy based on feature synergy and spectral bands combination of multiple detection modes. Firstly, compared with the commonly used halogen lamp, the xenon lamp was utilized to cover the ultraviolet (UV) range and match the characteristic information of different sugars (glucose, fructose, and sucrose). Furthermore, the spectral fusion method was employed to combine the reflectance and transmittance spectra to improve the cherry tomatoes SSC prediction accuracy with comprehensive data in the UV/VIS/NIR region. Finally, the best results were achieved by the partial least square regression (PLSR) model with spectral bands fusion, yielding the results of R2p, RMSEP, and RPD as 0.9653, 0.1998%, and 5.31, respectively. The same conclusion can also be verified when predicting the SSC in strawberries. Overall, this strategy is potentially universal to improve the prediction accuracy of SSC in fruit by matching light source spectra with characteristic absorption, as well as utilizing comprehensive spectral information in the 200–1100 nm range, providing valuable insights for the practical application of nondestructive detection of fruit internal quality.