Average Spectrum Research Articles

Stationary wavelet denoising of solid-state NMR spectra using multiple similar measurements

Accumulating several scans of free induction decays is always needed to improve the signal-to-noise ratio of NMR spectra, especially for the low gyromagnetic ratio solid-state NMR. In this study, we present a new denoising approach based on the correlations between multiple similar NMR spectra. Contrary to the simple averaging of multiple scans or denoising the final averaged spectrum, we propose a Wavelet-based Denoising technique for Multiple Similar scans(WDMS). Firstly, the stationary wavelet transform is applied to decompose every spectrum into approximation coefficients and detail coefficients. Then, the detail coefficients are multiplied by weights calculated based on Pearson's correlation coefficient and structural similarity index between approximation coefficients of different spectra. Finally, the average of these detailed components is used to denoise the spectra. The proposed method is carried on the assumption that noise between multiple spectra is uncorrelated while peak signal information is similar between different spectra, thus preserving the possibility of applying further processing to the data. As a demonstration, the standard wavelet denoise is applied to the WDMS-processed spectra, achieving a further increase in the S/N ratio. We confirm the reliability of the denoising approach based on multiple scans on 1D/2D solid-state MAS/static NMR spectra. In addition, we also show that this method can be used to deal with a single Car-Purcell-Meiboom-Gill (CPMG) echo train.

The EDIBLES survey

Context. Small linear carbon chain radicals such as C2 and C3 act as both the building blocks and dissociation fragments of larger carbonaceous species. Their rotational excitation traces the temperature and density of local environments. However, these homo-nuclear di- and triatomic species are only accessible through their electronic and vibrational features because they lack a permanent dipole moment, and high signal-to-noise ratio data are necessary as the result of their generally low abundances in the interstellar medium (ISM). Aims. In order to improve our understanding of small carbonaceous species in the ISM, we carried out a sensitive survey of C2 and C3 using the ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES) dataset. We also expanded our searches to C4, C5, and the 13C12C isotopologue in the most molecule-rich sightlines. Methods. We fitted synthetic spectra generated following a physical excitation model to the C2 (2-0) Phillips band to obtain the C2 column density (N) as well as the kinetic temperature (Tkin) and number density (n) of the host cloud. The C3 molecule was measured through its à − $ \tilde X$ (000-000) electronic origin band system. We simulated the excitation of this band with a double-temperature Boltzmann distribution. Results. We present the largest combined survey of C2 and C3 to date in which the individual transitions can be resolved. In total, we detected C2 in 51 velocity components along 40 sightlines, and C3 in 31 velocity components along 27 sightlines. Further analysis confirms the two molecules are detected in the same velocity components. We find a very good correlation between N(C2) and N(C3) with a Pearson correlation coefficient r = 0.93 and an average N(C2)/N(C3) ratio of 15.5± 1.4. A comparison with the behaviour of the C2 diffuse interstellar bands (DIBs) shows that there are no clear differences among sightlines with and without detections of C2 and C3. This is in direct contrast to the better-studied non-C2 DIBs, which have reduced strengths in molecule-rich environments, consistent with the idea that the C2 DIBs are indeed a distinguishable DIB family. We also identify, for the first time, the Q(2), Q(3), and Q(4) transitions of the 13C12C (2-0) Phillips band in the stacked average spectrum of molecule-rich sightlines, and estimate the isotopic ratio of carbon 12C/13C to be 79±8, consistent with literature results. At this stage it is not yet possible to identify these transitions in individual sightlines. Our search for the C4 and C5 optical bands was unsuccessful; even in stacked spectra no unambiguous identification could be made.

Identification and visualization of environmental microplastics by Raman imaging based on hyperspectral unmixing coupled machine learning

Microplastics (MPs) are ubiquitous contaminants that have become an emerging pollutant of concern, potentially threatening human health and ecosystem environments. Although current detection methods can accurately identify various types of MPs, it remains necessary to develop non-destructive and rapid methods to meet growing demands for detection. Herein, we combine a hyperspectral unmixing method and machine learning to analyse Raman imaging data of environmental MPs. Five MPs types including poly(butylene adipate-co-terephthalate) (PBAT), poly(butylene succinate) (PBS), p-polyethylene (PE), polystyrene (PS) and polypropylene (PP) were visualized and identified. Individual or mixed pure or aged MPs along with environmental samples were analysed by Raman imaging. Alternating volume maximization (AVmax) combined with unconstrained least squares (UCLS) method estimated end members and abundance maps of each of the MPs in the samples. Pearson correlation coefficients (r) were used as the evaluation index; the results showed that there is a high similarity between the raw spectra and the average spectra calculated by AVmax. This indicates that Raman imaging based on machine learning and hyperspectral unmixing is a novel imaging analysis method that can directly identify and visualize MPs in the environment.

Studying the variations of the cyclotron line in Cen X-3 using Insight-HXMT

We investigate the cyclotron resonant scattering features (CRSFs) of the accreting X-ray pulsar Cen X-3 and significantly detect the 29 keV cyclotron line features in the hard X-ray averaged spectroscopy studies based on the recent Insight-HXMT observations in 2022, when Cen X-3 has X-ray luminosity LX>∼5×1037 erg s−1 in the bands of 2 – 60 keV. We do not find a harmonic line in the average spectra based on different continuum models. We showed that the CRSF energies have no correlation with time or luminosity in the average spectra. In addition, by performing a pulse phase-dependent spectral analysis, we revealed the fundamental line with the centroid energy ranging from 25 to 29 keV with a high significance over the spin phases. The evolution of the cyclotron line centroid energies over pulse phase is similar to the shape of pulse profiles, illustrating a positive correlation between the energy of CRSFs and the pulse phase flux.

Color characterization of infrared two-photon vision

Humans have the ability to perceive pulsed near-infrared (NIR) light as visible light with about half the wavelength through a process known as two-photon (2P) absorption. Although it has been known for several decades, color perception in 2P vision remains uncharacterized. In this study, we conducted color matching experiments between pulsed NIR light and continuous visible light. We investigated seven NIR wavelengths ranging from 880 to 1100 nm, along with three radiant power values at the pupil plane, varying from 10 to 30 µW. Through these experiments, we obtained chromatic coordinates, chromaticity diagrams, dominant wavelengths, and average spectra. We found a pronounced correlation between perceived hue and wavelength, with hues shifting from reddish purple at 880 nm to blue, green, and yellowish green at 1100 nm. Moreover, we observed a relationship between hue and power for the wavelengths closer to the visible end of the spectrum. This phenomenon appears to be a consequence of the intensity-dependent ratio between the single photon (1P) and 2P absorption efficiencies of the visual pigments.

Early Detection of Dicamba and 2,4-D Herbicide Drifting Injuries on Soybean with a New Spatial–Spectral Algorithm Based on LeafSpec, an Accurate Touch-Based Hyperspectral Leaf Scanner

In soybeans, off-target damage from the use of dicamba and 2,4-D herbicides for broadleaf weed control can significantly impact sensitive vegetation and crops. The early detection and assessment of such damage are critical for plant diagnostic labs and regulatory agencies to inform regulated usage policies. However, the existing technologies that calculate the average spectrum often struggle to detect and differentiate the damage caused by these herbicides, as they share a similar mode-of-action. In this study, a high-precision spatial and spectral imaging solution was tested for the early detection of dicamba and 2,4-D-induced damage in soybeans. A 2021 study was conducted using LeafSpec, a touch-based hyperspectral leaf scanner, to detect damage on soybean leaves. VIS-NIR (visible–near infrared) hyperspectral images were captured from 180 soybean plants exposed to nine different herbicide treatments at different intervals after spraying. Leaf damage was distinguished as early as 2 h after treatment (HAT) using pairwise partial least squares discriminant analysis (PLS-DA) models based on spectral data. Leaf color distribution, texture, and morphological features were analyzed to separate herbicide dosages. By fully exploiting the spatial and spectral information from high-resolution hyperspectral images, classification accuracy was improved from 57.4% to over 80% for all evaluation dates. This work demonstrates the potential and advantages of using spectral and spatial features of LeafSpec hyperspectral images for the early and accurate detection of herbicide damage in soybean plants.

Nondestructive detection of Pleurotus geesteranus strain degradation based on micro-hyperspectral imaging and machine learning.

In the production of edible fungi, the use of degraded strains in cultivation incurs significant economic losses. Based on micro-hyperspectral imaging and machine learning, this study proposes an early, nondestructive method for detecting different degradation degrees of Pleurotus geesteranus strains. In this study, an undegraded strain and three different degradation-level strains were used. During the mycelium growth, 600 micro-hyperspectral images were obtained. Based on the average transmittance spectra of the region of interest (ROI) in the range of 400-1000 nm and images at feature bands, feature spectra and images were extracted using the successive projections algorithm (SPA) and the deep residual network (ResNet50), respectively. Different feature input combinations were utilized to establish support vector machine (SVM) classification models. Based on the results, the spectra-input-based model performed better than the image-input-based model, and feature extraction improved the classification results for both models. The feature-fusion-based SPA+ResNet50-SVM model was the best; the accuracy rate of the test set was up to 90.8%, which was better than the accuracy rates of SPA-SVM (83.3%) and ResNet50-SVM (80.8%). This study proposes a nondestructive method to detect the degradation of Pleurotus geesteranus strains, which could further inspire new methods for the phenotypic identification of edible fungi.

Analysis and comparison of machine learning methods for species identification utilizing ATR-FTIR spectroscopy

Accurate identification of insect species holds paramount significance in diverse fields as it facilitates a comprehensive understanding of their ecological habits, distribution range, and impact on both the environment and humans. While morphological characteristics have traditionally been employed for species identification, the utilization of empty pupariums for this purpose remains relatively limited. In this study, ATR-FTIR was employed to acquire spectral information from empty pupariums of five fly species, subjecting the data to spectral pre-processing to obtain average spectra for preliminary analysis. Subsequently, PCA and OPLS-DA were utilized for clustering and classification. Notably, two wavebands (3000–2800 cm−1 and 1800–1300 cm−1) were found to be significant in distinguishing A. grahami. Further, we established three machine learning models, including SVM, KNN, and RF, to analyze spectra from different waveband groups. The biological fingerprint region (1800–1300 cm−1) demonstrated a substantial advantage in identifying empty puparium species. Remarkably, the SVM model exhibited an impressive accuracy of 100 % in identifying all five fly species. This study represents the first instance of employing infrared spectroscopy and machine learning methods for identifying insect species using empty pupariums, providing a robust research foundation for future investigations in this area.

Similarity between Compact Extremely Red Objects Discovered with JWST in Cosmic Dawn and Blue-excess Dust-obscured Galaxies Known in Cosmic Noon

Spatially compact objects with extremely red color in the rest-frame optical to near-infrared (0.4–1 μm) and blue color in the rest-frame ultraviolet (UV; 0.2–0.4 μm) have been discovered at 5 < z < 9 using the James Webb Space Telescope (JWST). These extremely red objects (JWST-EROs) exhibit spectral energy distributions (SEDs) that are difficult to explain using a single component of either star-forming galaxies or quasars, leading to two-component models in which the blue UV and extremely red optical are explained using less-dusty and dusty spectra of galaxies or quasars, respectively. Here, we report the remarkable similarity in SEDs between JWST-EROs and blue-excess dust-obscured galaxies (BluDOGs) identified at 2 < z < 3. BluDOGs are a population of active galactic nuclei (AGNs) with black hole masses of ∼108–9 M ⊙, which are 1 order of magnitude larger than those in some JWST-EROs. The Eddington ratios of BluDOGs are 1 or higher, whereas those of JWST-EROs are in the range of 0.1–1. Therefore, JWST-EROs are less massive, less active, and more common counterparts in higher-z of BluDOGs in cosmic noon. Conversely, JWST-EROs have a significantly higher fraction of those with blue excess than DOGs. We present the average UV spectra of BluDOGs as a comparison to JWST-EROs and discuss a coherent evolutionary scenario for dusty AGN populations.

Bearing fault diagnosis of rail vehicles traction motor based on adaptive periodic segment matrix and singular value decomposition

The early fault characteristics of rolling bearings are relatively weak and accompanied by large amounts of noise, resulting in the collected bearing vibration signal having an extremely low signal-to-noise ratio, hence creating challenges in diagnosing faults in rail vehicle bearings. This paper proposes a method based on an adaptive period segment matrix and singular value decomposition to extract fault information from bearing fault impulse signals, in response to the problem of unstable period recognition in existing rolling bearing fault diagnosis methods due to strong background noise. Firstly, a periodic segment estimation method based on the average envelope spectrum is proposed. By calculating the envelope spectrum of the vibration signal, a method is proposed for assessing period length and period constraint F in segments. Then this method is applied to pinpoint the fault impact peak and the position of the vibration signal. Utilizing the fault impact peak and the calculated period constraint F, a strict rank-1 adaptive period segment matrix (APSM) is introduced. Finally, singular value decomposition is performed on this matrix, the bearing impulse vibration signal can be reconstructed through the first maximum singular value. The simulation and experimental analysis results of rolling bearings signals demonstrate the performance of the proposed method, even under severe background noise conditions(−20 dB), and the calculated envelope spectrum signal-to-noise ratio further supports the adaptive periodic segment matrix has an excellent ability to identify and extract periodic components, thus validating its usefulness as a fault diagnosis approach for rolling bearings.

Visible and Near-infrared Spectral Properties of Martian Slope Streaks

Martian slope streaks are large surface features with lower albedo than their surroundings that are one of the few active geologic processes occurring on Mars today. We investigated the slope streaks’ spectral properties using images from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) at nine sites, including three sites with observations over multiple years to enable time-series analysis. For each individual slope streak within each image, we determined the average spectra of each streak and of the slope immediately adjacent to account for changes in geology across a CRISM image or within slopes containing streaks. We find a trend where the visible spectral continuum (0.4–0.6 μm) is strongly negative for the darkest slope streaks, and the spectral continuum increases as the slope streaks fade, consistent across all sites and over multiple observations of the same site. We do not find absorption at 1.4 or 1.9 μm associated with hydration. In some slope streaks, we see evidence of Fe-bearing minerals, but these signatures are also found in the streak-free slope directly adjacent, suggesting that the streaks have a similar mineralogy to their surroundings. The spectral changes that we see are most consistent with changes in the fine dust component and support a dry flow mechanism whereby slope streaks are formed by processes that trigger dust removal from the slope in particular locations, with the streaks slowly fading as dust resettles on the surface.

The Principal Component Analysis Filtering Method for an Unbiased Spectral Survey of Complex Organic Molecules

A variety of interstellar complex organic molecules (COMs) have been detected in various physical conditions. However, in the protostellar and protoplanetary environments, their complex kinematics make line profiles blend together and the line strength of weak lines weaker. In this paper, we utilize the principal component analysis technique to develop a filtering method that can extract COM spectra from the main kinematic component associated with COM emission and increase the signal-to-noise ratio (S/N) of spectra. This filtering method corrects non-Gaussian line profiles caused by the kinematics. For this development, we adopt the ALMA BAND 6 spectral survey data of V883 Ori, an eruptive young star with a Keplerian disk. A filter was, first, created using 34 strong and well-isolated COM lines and then applied to the entire spectral range of the data set. The first principal component (PC1) describes the most common emission structure of the selected lines, which is confined within the water sublimation radius (∼0.″3) in the Keplerian disk of V883 Ori. Using this PC1 filter, we extracted high-S/N kinematics-corrected spectra of V883 Ori over the entire spectral coverage of ∼50 GHz. The PC1-filtering method reduces the noise by a factor of ∼2 compared to the average spectra over the COM emission region. One important advantage of this PC1-filtering method over the previously developed matched-filtering method is the ability to preserve the original integrated intensities of COM lines.

Surface analysis insight note: An example of a cluster analysis of spectra from an X‐ray photoelectron spectroscopy image

Identification of similar and dissimilar spectra is an important part of analyzing X‐ray photoelectron spectroscopy (XPS) images. Cluster analysis (CA) is a commonly used exploratory data analysis (EDA) method that groups similar spectra in a data set. CA can be performed in either an agglomerative fashion, for example, using Ward's method, which involves successively linking together/clustering the most similar spectra in a data set, or in a divisive fashion, for example, using the K‐means approach, which involves partitioning all the data into a specified number of clusters. In this note, we show the application of CA to an XPS image dataset. The use of Ward's method identified two major clusters in the image, where one of the clusters appeared as two subclusters. The K‐means image based on two clusters agrees well with previous analyses of the same image. The average spectra corresponding to clusters helped confirm the assignments made by the CA algorithms, as did a multivariate curve resolution (MCR) analysis of the interior region identified in our cluster analysis. “Elbow” plots can help determine the number of clusters to keep in K‐means clustering. The combination of the agglomerative and divisive forms of CA, where the first informs the second, can be effective in revealing the structures of XPS image datasets. The Procrustean bed is a metaphor for overfitting and underfitting in EDA.

Evaluation of the spatial self-shielding impact for TRISO-based nuclear fuel depletion

Reactor physics analyses of nuclear cores with nuclear fuel concepts containing tristructural isotropic (TRISO) particles, such as pebbles or compact fuel elements, rely on various degrees of simplification to keep these highly heterogeneous problems computationally tractable. One such limitation regards the level of spatial discretization employed during burnup calculations, where traditionally only a limited number of spatial zones are modeled at the full core level and assume that the spectrum is constant within the fuel elements and TRISO particles in this depletion zone. This type of assumption neglects the impact of spatial self-shielding effect within the kernels (microscale level) as well as within the compact or pebbles (mesoscale level). The Monte Carlo code Serpent 2 contains many relevant features for efficiently modeling this type of geometry, including a collision-based domain decomposition intended for very large burnup calculations, which we leveraged for this work to quantify the impact of capturing neutron flux variations occurring at the micro- and mesoscale level on a series of high-temperature gas-cooled reactor fuel element depletion problems. While spatial self-shielding is observed at both scales, with differences from a volume-averaged burnup of ±7% within the kernels and ±2% between TRISO particles within the fuel element, the conjugated effect on nuclide inventories and multiplication factor are negligible, hence confirming that assuming a single average spectrum value may be sufficient for most applications.

Contrastive Alveolar/Retroflex Phonemes in Singapore Mandarin Bilinguals: Comprehension Rates for Articulations in Different Accents, and Acoustic Analysis of Productions.

The standard Beijing variety of Mandarin has a clear alveolar-retroflex contrast for phonemes featuring voiceless sibilant frication (i.e., /s/, /ʂ/, /ʈs/, /ʈʂ/, /ʈsʰ/, /ʈʂʰ/). However, some studies show that varieties in the 'outer circle', such in Taiwan, have a reduced contrast for these speech sounds via a process known as 'deretroflexion'. The variety of Mandarin spoken in Singapore is also considered as 'outer circle', as it exhibits influences from Min Nan varieties. We investigated how bilinguals of Singapore Mandarin and English perceive and produce speech tokens in minimal pairs differing only in the alveolar/retroflex place of articulation. In all, 50 participants took part in two tasks. In Task 1, participants performed a lexical identification task for minimal pairs differing only the alveolar/retroflex place of articulation, as spoken by native speakers of two varieties: Beijing Mandarin and Singapore Mandarin. No difference in comprehension of the words was observed between the two varieties indicating that both varieties contain sufficient acoustic information for discrimination. In Task 2, participants read aloud from the list of minimal pairs while their voices were recorded. Acoustic analysis revealed that the phonemes do indeed differ acoustically in terms of center of gravity of the frication and in an alternative measure: long-term averaged spectra. The magnitude of this difference appears to be smaller than previously reported differences for the Beijing variety. These findings show that although some deretroflexion is evident in the speech of bilinguals of the Singaporean variety of Mandarin, it does not translate to ambiguity in the speech signal.

Generalized maximum entropy methods as limits of the average spectrum method

Generalized maximum entropy methods as limits of the average spectrum method

Rapid measurement of sardine freshness by using visible and near infrared spectroscopy

Freshness is an important attribute of sardine quality, which measurement for control purposes needs simple and rapid methods. This study shows the suitability of PCA of the averaged sardine VISNIR spectra to predict sardine freshness. The sample was composed of a relatively small number of sardines, the proposed method being suitable for routine analysis. The results of PLS tests indicated this method can be useful to confirm a sardine freshness class defined according to PCA results. The tests with PLS-DA and clustering indicated the practical interest of these last methods is lower, even though being in good agreement with those from PCA and PLS.

Identification of geographical origin of semen ziziphi spinosae based on hyperspectral imaging combined with convolutional neural networks

The accurate identification of geographical origin is one of the main challenges in quality assessment of semen ziziphi spinosae. This paper proposed resnet50-based and shuffle-based convolutional neural network (CNN) models (CNN-resnet and CNN-shuffle for hyperspectral image input, as well as CNN-resnet1D and CNN-shuffle1D for spectra input) to discriminate semen ziziphi spinosae from five geographical origins. Specifically, minimum noise fraction combined with mask was adopted to remove background pixels. Savitzky-Golay smoothing, followed by standard normal variate, was applied to denoise in pixel-wise. Mahalanobis distances were calculated based on the average spectra to identify and exclude outliers. Partial least squares discriminant analysis and support vector machine were also adopted to compare with the four CNN networks, particular in model accuracy, computing time, and parameters. Overall, CNN-shuffle was the best, with accuracy (0.902) comparable to CNN-resnet and in less time (0.348 s) and with fewer parameters (5607). Feature wavelengths were selected based on the learned weight coefficients in self-defined layer of CNN-resnet1D and CNN-shuffle1D models. They were related to lipid and protein, of which contents were statistically analyzed. Both lipids and proteins were key parameters, and lipids played a greater role in the identification. Spinosin as a quality marker was measured via standard HPLC method, which was comparably discussed with the hyperspectral imaging (HSI). The potential application of HSI with CNN in quality control of semen ziziphi spinosae within the modern system of mass production was higher compared with HPLC.

Methods for Averaging Spectral Line Data

The ideal spectral averaging method depends on one’s science goals and the available information about one’s data. Including low-quality data in the average can decrease the signal-to-noise ratio (S/N), which may necessitate an optimization method or a consideration of different weighting schemes. Here, we explore a variety of spectral averaging methods. We investigate the use of three weighting schemes during averaging: weighting by the signal divided by the variance (“intensity-noise weighting”), weighting by the inverse of the variance (“noise weighting”), and uniform weighting. Whereas for intensity-noise weighting the S/N is maximized when all spectra are averaged, for noise and uniform weighting we find that averaging the 35%–45% of spectra with the highest S/N results in the highest S/N average spectrum. With this intensity cutoff, the average spectrum with noise or uniform weighting has ∼95% of the intensity of the spectrum created from intensity-noise weighting. We apply our spectral averaging methods to GBT Diffuse Ionized Gas hydrogen radio recombination line data to determine the ionic abundance ratio, y +, and discuss future applications of the methodology.

Formant and Voice Source Characteristics of Vowels in Chinese National Singing and Bel Canto. A Pilot Study

There have been numerous reports on the acoustic characteristics of singers' vowel articulation and phonation, and these studies cover many phonetic dimensions, such as fundamental frequency (F0), intensity, formant frequency, and voice quality. Taking the three representative vowels (/a/, /i/, /u/) in Chinese National Singing and Bel Canto as the research object, the present study investigates the differences and associations in vowel articulation and phonation between Chinese National Singing and Bel Canto using acoustic measures, for example, F0, formant frequency, long-term average spectrum (LTAS). The relationship between F0 and formant indicates that F1 is proportional to F0, in which the female has a significant variation in vowel /a/. Compared with the male, the formant structure of the female singing voice differs significantly from that of the speech voice. Regarding the relationship between intensity and formant, LTAS shows that the Chinese National Singing tenor and Bel Canto baritone have the singer's formant cluster when singing vowels, while the two sopranos do not. The systematic changes of formant frequencies with voice source are observed. (i) F1 of the female vowel /a/ has undergone a significant tuning change in the register transition, reflecting the characteristics of singing genres. (ii) Female singers utilize the intrinsic pitch of vowels when adopting the register transition strategy. This finding can be assumed to facilitate understanding the theory of intrinsic vowel pitch and revise Sundberg's hypothesis that F1 rises with F0. A non-linear relationship exists between F1 and F0, which adds to the non-linear interaction of the formant and vocal source. (iii) The singer's formant is affected by voice classification, gender, and singing genres.