Spectral Transformation Research Articles

Identification and Removal of Pollen Spectral Interference in the Classification of Hazardous Substances Based on Excitation Emission Matrix Fluorescence Spectroscopy.

Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky-Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation-emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including Staphylococcus aureus, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols.

Generating high-resolution hyperspectral time series datasets based on unsupervised spatial-temporal-spectral fusion network incorporating a deep prior

Over the past decade, image fusion has emerged as an indispensable tool for surface monitoring due to its capability to reconstruct high-quality surface reflectance. While satisfactory progress has been made in the spatial-spectral and spatial-temporal fusion of remote sensing images, the fusion of spatial-temporal-spectral data remains a challenging task. This challenge arises from the presence of multiple nonlinear relationships caused by inconsistent temporal, spatial, and spectral resolutions. In this paper, we propose a novel approach to address the spatial-temporal-spectral fusion of remote sensing images using a triple discriminator Generative Adversarial Network called TDGAN. Our approach represents the first attempt at spatial-temporal-spectral fusion based on deep learning techniques. Specifically, TDGAN achieves unsupervised learning by incorporating a deep spectral transformation prior, explores the application performance of the Swin Transformer in spectral super-resolution for the first time. Additionally, three discriminators are designed based on the prior consistencies: phenological consistency for data acquired at different times and spatial-spectral consistency for data acquired at the same time. These discriminators effectively enhance the prediction accuracy of missing hyperspectral images. Extensive simulations and real experiments were conducted to evaluate the effectiveness of our proposed TDGAN. The results demonstrate its robustness and comparable performance to other state-of-the-art methods. The proposed approach provides high-quality hyperspectral datasets with high spatial and temporal resolutions, facilitating continuous land cover observations.

Exploring mid‐infrared spectral transfer functions for the prediction of multiple soil properties using a global dataset

AbstractInfrared spectroscopy is increasingly being adopted as a technology for soil analysis. However, laboratories worldwide are equipped with different infrared spectrometers, leading to variations that hinder the global application of soil spectroscopy. This study evaluates the transferability of soil spectra from a global dataset collected using four mid‐infrared spectrometers. To evaluate the efficacy of five spectral transfer functions (direct standardization, piecewise direct standardization, spectral space transformation [SST], principal components‐canonical correlation analysis [PC‐CCA], and domain‐invariant partial least square [DIPLS] regression), two datasets were used: dataset A (n = 224; standardized samples) was scanned using one primary spectrometer and three secondary spectrometers; dataset B (n = 1904; legacy samples) was scanned only using the primary spectrometer. The first set of chemometrics models was developed using dataset A to compare the performance of different spectrometers. The second set of models was developed using dataset B to evaluate the effectiveness of spectral transfer functions. Both models were developed using partial least squares regression. Spectral transfer functions developed using dataset A indicate that the PC‐CCA method was the best in converging spectra collected from four instruments into a similar space projected using Uniform Manifold Approximation and Projection. Spectral transfer did not result in consistent improvement in the prediction of soil properties compared to the direct use of spectra collected from different spectrometers. These findings carry significant implications for the utilization of legacy models, enabling laboratories to concentrate on acquiring new samples and spectral measurements using established protocols without the need for spectral transfer.

Coupled Vis-NIR spectroscopy with chemometrics strategy for soil organic carbon prediction in the Agro-pastoral Transitional zone of northwest China

Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg−1, 1.457 g kg−1, 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R2 of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg−1 and 1.967 g kg−1, and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China.

Modeling risk assessment of soil heavy metal pollution using partial least squares and fuzzy logic: A case study of a gully type coal-based solid waste dumpsite

Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and rapid monitoring, analysis, and assessment to control environmental risks associated with large CSW dumpsites. We constructed a new composite model (PLS-FL) that uses partial least squares regression (PLSR) and fuzzy logic inference (FLI) to accurately predict heavy metal concentrations in soils and assess pollution risk levels. The potential application of the PLS-FL was tested through a gully type CSW case study. We compared 20 modeling strategies using the PLS-FL: five types heavy metals (Cd, Zn, Pb, Cr and As) * four spectral transformation methods (first derivative (FD), second derivative (SD), reverse logarithm (RL), and continuum removal (CR)) * one variable selection method (competitive adaptive reweighted sampling (CARS)). The results showed that the combination of derivative transformation and CARS was recommended for estimation, with R2C > 0.80 and R2P > 0.50. When comparing the PLSR model with four traditional machine learning methods (Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), and KNN), the PLSR model demonstrated the highest average prediction accuracy. Additionally, the FLI process no longer relies on human perception and expert opinion, enhancing the model's objectivity and reliability. The evaluation results revealed that the heavy metal contamination areas of the CSW dumpsite are concentrated at the bottom of the gully, with more severe contamination in the north. Furthermore, a high-risk zone exists in the interim storage area for CSW to the east of the dump. These findings align with the initial detections at the sampling sites and highlight the need for targeted monitoring and control in these areas. The application of the model will empower regulators to quickly assess the overall situation of large-scale heavy metal pollution and provide scientific program and data support for continuous large-scale pollution risk monitoring and sustainable risk management.

Feasibility of Estimating Type and Abundance of Iron Ore Using Field Hyperspectral Radiometry and Spectral Transformation in Precambrian Banded Iron Formations of North Odisha, India

Feasibility of Estimating Type and Abundance of Iron Ore Using Field Hyperspectral Radiometry and Spectral Transformation in Precambrian Banded Iron Formations of North Odisha, India

Differentiation of mezcales from four agave species using FT-MIR and multivariate statistical analysis

Fourier Transform Mid-Infrared (FT-MIR) spectroscopy and multivariate statistical analysis were used to differentiate mezcales elaborated with four agave species. The FT-MIR data matrix was subjected to spectral transformations using first and second derivatives. The Partial Least Squares (PLS)-Discriminant Analysis (DA) with the matrix transformed by the first and second derivative allowed the differentiation of mezcales. While Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was more robust when it was analyzed with second-derivative data. Pairwise comparisons by OPLS-DA allowed mezcales to be correctly discriminated, mainly between Agave karwinskii and Agave potatorum (Q2 = 0.654 and p – value < 0.01; R2Y = 0.985 and p-value < 0.01) and between Agave angustifolia and Agave karwinskii (Q2 = 0.563 and p-value = 0.01; R2Y = 0.989 and p-value = 0.01). FT-MIR spectrophotometry and the PLS-Regression (PLS-R) were applied to predict the ethanol percentage (% v/v) of mezcales collected in 2022 based on the PLS-R model previously run on samples evaluated in 2021.

Dramatic Rebrightening of the Type-changing Stripped-envelope Supernova SN 2023aew

Multipeaked supernovae with precursors, dramatic light-curve rebrightenings, and spectral transformation are rare, but are being discovered in increasing numbers by modern night-sky transient surveys like the Zwicky Transient Facility. Here, we present the observations and analysis of SN 2023aew, which showed a dramatic increase in brightness following an initial luminous (−17.4 mag) and long (∼100 days) unusual first peak (possibly precursor). SN 2023aew was classified as a Type IIb supernova during the first peak but changed its type to resemble a stripped-envelope supernova (SESN) after the marked rebrightening. We present comparisons of SN 2023aew’s spectral evolution with SESN subtypes and argue that it is similar to SNe Ibc during its main peak. P-Cygni Balmer lines are present during the first peak, but vanish during the second peak’s photospheric phase, before Hα resurfaces again during the nebular phase. The nebular lines ([O i], [Ca ii], Mg i], Hα) exhibit a double-peaked structure that hints toward a clumpy or nonspherical ejecta. We analyze the second peak in the light curve of SN 2023aew and find it to be broader than that of normal SESNe as well as requiring a very high 56Ni mass to power the peak luminosity. We discuss the possible origins of SN 2023aew including an eruption scenario where a part of the envelope is ejected during the first peak and also powers the second peak of the light curve through interaction of the SN with the circumstellar medium.

The role of topography feedbacks in enrichment of heavy metal elements in terrace type region

Minerals, metallurgy, and other production activities will cause a large number of heavy metal elements to leak into the natural environment. A large number of heavy metal elements have been found in the farmland soil, where the adsorption of plants enhances the enrichment. Here, we have selected a region with three terraces to conduct a whole-area soil sample collection and satellite hyperspectral data processing study to explore the role of terrain in this enrichment process. Five spectral transformation methods and four feature enhancement algorithms were designed, and the content extraction model was established to quantitatively retrieve eight heavy metal elements. The results indicates that the three terraces are the source state, transition state, and stable state of heavy metals respectively with the decrease of elevation; The correlation coefficient of various heavy metal elements exceeds 0.92, and the enrichment pattern is consistent although slope and aspect have no significant correlation with the enrichment of heavy metal elements; Local Cd exceeds 30.00%, Hg exceeds 10 times, and As exceeds 48.30% according to the indicator provisions of Chinese national standard (GB 15618-2018). Such knowledge extends our understanding of the abundance, migration, and enrichment of heavy metals from the perspective of topography, which is crucial for pollution assessment and soil remediation.

Prediction of soil organic carbon in black soil based on a synergistic scheme from hyperspectral data: Combining fractional-order derivatives and three-dimensional spectral indices

Monitoring soil organic carbon (SOC) content is crucial for climate change mitigation and sustaining ecological balance. Despite the unparalleled advantages of hyperspectral data in capturing nuanced variations in soil properties through its high spectral resolution, effectively extracting useful features from numerous bands via spectral processing techniques remains a formidable challenge. This study proposes an integrated approach combining fractional-order derivative (FOD) technique and optimal band combination algorithm using ZY1-02D satellite hyperspectral data to estimate SOC in Northeast China’s Black soil region. Three modeling strategies were compared: (1) FOD-transformed reflectance (FOD spectra), (2) FOD spectra with traditional 2D spectral indices (FOD + 2D SI), and (3) FOD spectra with new 3D spectral indices (FOD + 3D SI). These strategies were implemented using the random forest model with the aim of the optimal SOC prediction. Results showed that the application of FOD technique for spectral transformation effectively addressed the challenges posed by overlapping peaks and baseline drift inherent in the original spectral reflectance. Additionally, FOD transformation enhanced subtle soil spectral features and yielded more pronounced spectral variations with increasing fractional order, as compared to the original spectral data and conventional integer-order derivatives (i.e., first and second-order derivatives). However, as the FOD order continued to increase beyond 1.4, the spectral curve exhibited amplified noise and distortion, thereby adversely impacting subsequent model development. The 3D spectral indices correlate more robustly with SOC than 2D indices. The model that combines 0.6-order FOD and 3D spectral indices achieved the best accuracy (R2 = 0.66, RMSE = 2.99 g/kg and MAE = 2.42 g/kg), significantly outperforming the models built by 0.6-order FOD spectra (R2 = 0.48, RMSE = 3.65 g kg−1, and MAE = 2.93 g kg−1) and 0.8-order FOD + 2D SI modeling strategy (R2 = 0.55, RMSE = 3.54 g kg−1, and MAE = 2.85 g kg−1). These findings indicated that FOD and 3D spectral indices exhibit superior synergistic performance in SOC prediction, demonstrating their feasibility and providing valuable insights for large-scale soil property prediction and mapping using satellite hyperspectral data.

2D‐Material‐Fused High‐Emittance Plasmo–Photonic Metasurfaces

AbstractTransition metal dichalcogenides (TMDC) are a family of atomic‐layer 2D materials (2dMs). Many researches have attempted to reinforce the unique properties by incorporating artificially designed nanostructures. Here, a highly photoluminescence (PL) enhancing platform is reported that is formed by combining high‐emittance plasmo–photonic metasurfaces with a large‐area 2dM, which is a continuous monolayer of TMDC of cm2 dimension. It is experimentally demonstrated that the 2dM‐PL intensity at room temperature is enhanced by more than 1030‐fold in comparison with that observed on the flat gold film regions. The enhancement is associated with spectral transformation indicating a resonant effect of the metasurfaces. It is moreover revealed that the confocal PL image originates from a coherent single‐mode emission that forms a photon number distribution obeying the Poisson probability distribution. Thus, the 2dM‐fused metasurfaces function as efficient coherent quantum light sources. PL decay‐time analysis also reveals the coexistence of two components in the enhanced PL. The fast component exhibits a lifetime of 18 ps whereas the slow component decays at 4.4 ns. Both components are most likely to undergo radiative processes, which indicates that metal‐induced PL quenching is suppressed on these 2dM‐fused metasurfaces. The long lifetime component is ascribed to an excitonic intervalley transition.

Estimating the chromium concentration of farmland soils in an arid zone from hyperspectral reflectance by using partial least squares regression methods

Heavy metal contamination of farmland soils is a severe concern worldwide.Chromium (Cr), a major environmental carcinogen, is a crucial indicator of farmland soil pollution. Compared with conventional chemical analysis, the hyperspectral analysis is superior in accurately and rapidly surveilling heavy metals in soils. However, various heavy metals, and types or geochemical traits of soils often affect the accuracy of hyperspectral estimation models, which makes guaranteeing the concentration estimation precision for a designated heavy metal in different regions considerably challenging. We here propose an optimal hyperspectral model for determining the Cr concentration of farmland soils first time in the Yanqi Basin of China's northwest arid zone. In total, 171 soil samples were gathered, and their Cr concentrations and relevant spectral reflectance data were determined. The best spectral transformation for measuring the Cr concentration was determined by using 12 transformation types of soil spectral curves, and characteristic wavebands were obtained. Then, by using random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR) models, we constructed hyperspectral estimation models for Cr concentration in farmland soils. The estimated results of these models were compared to identify the best estimation model. Results show that the spectral transformations allow an improvement in the association between the soil Cr concentration and spectra. The accuracy of PLSR was significantly higher than that of SVM and RF. Finally, the second-order differentiation (SD) combined with the PLSR model (R2 = 0.903, RMSE = 40.363, MAE = 30.631) was identified as the optimal model for estimating the Cr concentration of farmland soils in the investigated area. The study findings offer a technical reference for the hyperspectral assessment of the Cr concentration in arid zone farmland soils.

Spectral transformations and second kind polynomials associated with a hermitian linear functional

The aim of this paper is to analyze the relation between the Christoffel transformations in the framework of hermitian linear functionals and the associated polynomials of the second kind through of their associated Carathéodory formal power series. In particular, some results are given in the way of characterization of linear spectral transformations using Christoffel and Geronimus transformations. This gives a new example of rational spectral transformation that has not yet been studied in the literature.

A novel method in COPD diagnosing using respiratory signal generation based on CycleGAN and machine learning

Objective The main goal of this research is to use distinctive features in respiratory sounds for diagnosing Chronic Obstructive Pulmonary Disease (COPD). This study develops a classification method by utilizing inverse transforms to effectively identify COPD based on unique respiratory features while comparing the classification performance of various optimal algorithms. Method Respiratory sounds are divided into individual breathing cycles. In the data standardization and augmentation phase, the CycleGAN model enhances data diversity. Comprehensive analyses for these segments are then implemented using various Wavelet families and different spectral transformations representing characteristic signals. Advanced convolutional neural networks, including VGG16, ResNet50, and InceptionV3, are used for the classification task. Results The results of this study demonstrate the effectiveness of the mentioned method. Notably, the best-performing method utilizes Wavelet Bior1.3 after standardization in combination with InceptionV3, achieving a remarkable 99.75% F1-score, the gold standard for classification accuracy. Conclusion Inverse transformation techniques combined with deep learning models show significant accuracy in detecting COPD disease. These findings suggest the feasibility of early COPD diagnosis through AI-powered characterization of acoustic features. Motivation and Significance The motivation behind this research stems from the urgent need for early and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD). COPD is a respiratory disease that poses many difficulties when detected late, potentially causing severe harm to the patient’s quality of life and increasing the healthcare burden. Timely identification and intervention are crucial to reduce the progression of the disease and improve patient outcomes.

A COMPARATIVE STUDY OF SATELLITE IMAGE RESOLUTIONS FOR DETECTING PEST DAMAGE IN SUNFLOWER FIELDS

Abstract. The diversity of sensors in remote sensing allows for faster and easier detection of changes and issues across different scales, in contrast to conventional ground-based systems. One of the most important distinguishing features among these sensors is their varying resolutions, contributing to the versatility of remote sensing technologies across diverse environmental applications. In this study, the effectiveness of PlanetScope and Sentinel-2 satellite images with different image resolutions in detecting damage caused by a harmful insect (beet webworm moth - Loxostege sticticalis) in sunflower fields in Lüleburgaz district of Kırklareli in the Trace region was evaluated. Damage rates in sunflower fields were analyzed using various spectral indices (Enhanced Vegetation Index and Chlorophyll Index Green) and spectral transformation (Tasseled Cap Greenness) in conjunction with in situ data. Based on the spectral analysis, the satellite image dated 26 July, which showed the most severe damage, was used in the damage assessment analysis. The damaged areas were compared by classifying both satellite images with the Random Forest algorithm. According to the results of the classification accuracy assessment, PlanetScope satellite imagery showed the highest accuracy, with 90% overall accuracy and 84% Kappa statistics, making it a more suitable sensor choice for agricultural applications.

Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign.

This study systematically developed a deep transfer network for near-infrared spectrum detection using convolutional neural network modules as key components. Through meticulous evaluation, specific modules and structures suitable for constructing the near-infrared spectrum detection model were identified, ensuring its effectiveness. This study extensively analyzed the basic network components and explored three unsupervised domain adaptation structures, highlighting their applications in the nondestructive testing of wood. Additionally, five transfer networks were strategically redesigned to substantially enhance their performance. The experimental results showed that the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network outperformed the Direct Standardization, Piecewise Direct Standardization, and Spectral Space Transformation models. The coefficients of determination for the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network are 82.11% and 83.59%, respectively, with root mean square error prediction values of 12.237 and 11.582, respectively. These achievements represent considerable advancements toward the practical implementation of an efficient and reliable near-infrared spectrum detection system using a deep transfer network.

A note on zeros of orthogonal polynomials generated by canonical transformations

In this work, the behavior of zeros of orthogonal polynomials associated with canonical spectral transformations of weight functions on $[0,\infty)$ is studied. Namely, by means of standard techniques, we obtain interlacing properties for zeros associated with some particular cases of rational and Christoffel transformations

A ∂¯-Dressing Method for the Kundu-Nonlinear Schrödinger Equation

In this paper, we employed the ∂¯-dressing method to investigate the Kundu-nonlinear Schrödinger equation based on the local 2 × 2 matrix ∂¯ problem. The Lax spectrum problem is used to derive a singular spectral problem of time and space associated with a Kundu-NLS equation. The N-solitions of the Kundu-NLS equation were obtained based on the ∂¯ equation by choosing a special spectral transformation matrix, and a gradual analysis of the long-duration behavior of the equation was acquired. Subsequently, the one- and two-soliton solutions of Kundu-NLS equations were obtained explicitly. In optical fiber, due to the wide application of telecommunication and flow control routing systems, people are very interested in the propagation of femtosecond optical pulses, and a high-order, nonlinear Schrödinger equation is needed to build a model. In plasma physics, the soliton equation can predict the modulation instability of light waves in different media.

Long-time asymptotics for the coupled complex short-pulse equation with decaying initial data

We characterize the long-time asymptotic behavior of the solution of the initial value problem for the coupled complex short-pulse equation associated with the 4×4 matrix spectral problem. The spectral analysis of the 4×4 matrix spectral problem is very difficult because of the existence of energy-dependent potentials and the WKI type. The method we adopted is a combination of the inverse scattering transform and Deift-Zhou nonlinear steepest descent method. Starting from the Lax pair associated with the coupled complex short-pulse equation, we derive a basic Riemann-Hilbert problem by introducing some appropriate spectral function transformations, and reconstruct the potential parameterized from the solution of the basic Riemann-Hilbert problem via the asymptotic behavior of the spectral variable at k→0. We finally obtain the leading order asymptotic behavior of the solution of the coupled complex short-pulse equation through a series of Deift-Zhou contour deformations.

Exploring laboratory-based spectroscopy for estimating NPK content in the hutton soils of Syferkuil Farmlands, South Africa

This study seeks to identify the key wavelengths and spectral transformation techniques that may be used to accurately estimate soil nitrogen, phosphorus, and potassium (NPK) concentration in the Hutton soils. For this purpose, the reflectance spectra of 74 soil samples were measured in the laboratory, and the chemical properties of the soil were analysed. Pearson correlation (r), stepwise multiple linear regression (SMLR), and Partial Least Square Regression were used to model the relationships between the selected variables under study. The study findings revealed that moderate to weak correlations (r = −0.2 − 0.6) exist between wavelengths (400 − 2500 nm) and soil NPK content. Important wavelengths for accurately predicting Soil NPK content were found within visible (400 nm − 729nm), near-infrared (805 nm − 1200nm) and mid-infrared region (1375 nm to 2462 nm). The PLSR model coupled with different spectral transformations was found to be the optimal model for estimating soil NPK (RPD > 2). The introduction of the first and second derivate spectral transformation approach markedly improved all the models investigated in this study. The results retrieved from the study provide a high-accuracy prediction of soil nutrient contents, which is critical for soil fertility management and sustainable food production.