Unknown Spectrum Research Articles

Supervised topic modeling for predicting molecular substructure from mass spectrometry

Small-molecule metabolites are principal actors in myriad phenomena across biochemistry and serve as an important source of biomarkers and drug candidates. Given a sample of unknown composition, identifying the metabolites present is difficult given the large number of small molecules both known and yet to be discovered. Even for biofluids such as human blood, building reliable ways of identifying biomarkers is challenging. A workhorse method for characterizing individual molecules in such untargeted metabolomics studies is tandem mass spectrometry (MS/MS). MS/MS spectra provide rich information about chemical composition. However, structural characterization from spectra corresponding to unknown molecules remains a bottleneck in metabolomics. Current methods often rely on matching to pre-existing databases in one form or another. Here we develop a preprocessing scheme and supervised topic modeling approach to identify modular groups of spectrum fragments and neutral losses corresponding to chemical substructures using labeled latent Dirichlet allocation (LLDA) to map spectrum features to known chemical structures. These structures appear in new unknown spectra and can be predicted. We find that LLDA is an interpretable and reliable method for structure prediction from MS/MS spectra. Specifically, the LLDA approach has the following advantages: (a) molecular topics are interpretable; (b) A practitioner can select any set of chemical structure labels relevant to their problem; (c ) LLDA performs well and can exceed the performance of other methods in predicting substructures in novel contexts.

On-chip spectrometers using stratified waveguide filters

We present an ultra-compact single-shot spectrometer on silicon platform for sparse spectrum reconstruction. It consists of 32 stratified waveguide filters (SWFs) with diverse transmission spectra for sampling the unknown spectrum of the input signal and a specially designed ultra-compact structure for splitting the incident signal into those 32 filters with low power imbalance. Each SWF has a footprint less than 1 µm × 30 µm, while the 1 × 32 splitter and 32 filters in total occupy an area of about 35 µm × 260 µm, which to the best of our knowledge, is the smallest footprint spectrometer realized on silicon photonic platform. Experimental characteristics of the fabricated spectrometer demonstrate a broad operating bandwidth of 180 nm centered at 1550 nm and narrowband peaks with 0.45 nm Full-Width-Half-Maximum (FWHM) can be clearly resolved. This concept can also be implemented using other material platforms for operation in optical spectral bands of interest for various applications.

Hammerstein system with a stochastic input of arbitrary/unknown autocorrelation – nonparametric estimator of the static nonlinear subsystem

This study proposes the first estimator in the open literature (to the present authors' best knowledge) to nonparametrically estimate a Hammerstein system's nonlinear static subsystem when excited by an input that is temporally self-correlated with an unknown spectrum, an unknown variance and an unknown mean (instead of the input as commonly presumed to be white and zero-mean). This proposed nonparametric estimator is analytically proved here to be asymptotically unbiased and pointwise consistent. The proposed estimate's associated finite-sample convergence rate is also derived analytically.

A wavelength-scale black phosphorus spectrometer

On-chip spectrometers with compact footprints are being extensively investigated owing to their promising future in critical applications such as sensing, surveillance and spectral imaging. Most existing miniaturized spectrometers use large arrays of photodetection elements to capture different spectral components of incident light, from which its spectrum is reconstructed. Here, we demonstrate a mid-infrared spectrometer in the 2–9 µm spectral range, utilizing a single tunable black phosphorus photodetector with an active area footprint of only 9 × 16 µm2, along with a unique spectral learning procedure. Such a single-detector spectrometer has a compact size at the scale of the operational wavelength. Leveraging the wavelength and bias-dependent responsivity matrix learned from the spectra of a tunable blackbody source, we reconstruct unknown spectra from their corresponding photoresponse vectors. Enabled by the strong Stark effect and the tunable light–matter interactions in black phosphorus, our single-detector spectrometer shows remarkable potential in the reconstruction of the spectra of both monochromatic and broadband light. Furthermore, its ultracompact structure that is free from bulky interferometers and gratings, together with its electrically reconfigurable nature, may open up pathways towards on-chip mid-infrared spectroscopy and spectral imaging. A single-photodetector spectrometer based on black phosphorus is demonstrated in the wavelength range from 2 to 9 μm. The footprint is 9 × 16 μm2. The spectrometer is free from bulky interferometers and gratings, and is electrically reconfigurable.

Deep learning in photoacoustic imaging: a review.

.Significance: Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding.Aim: We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities.Approach: Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI.Results: When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis.Conclusion: DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.

Integrated Silicon Fourier Transform Spectrometer with Broad Bandwidth and Ultra‐High Resolution

AbstractAn ultra‐high resolution Fourier transform spectrometer (FTS) realized in silicon photonic platform that can operate with broad band, narrow band as well as a combination of broad band and narrow band signals is reported. The ultra‐high resolution of the spectrometer is achieved by exploiting multiple techniques: a Michelson interferometer (MI) structure to increase the optical path delay (OPD), a hybrid waveguide design to reduce insertion loss, an optimized heater and air trenches to achieve higher thermal efficiency. Moreover, to further increase the OPD of the spectrometer to increase its resolution, a novel multiple interferometers approach is employed which combines balanced MI with N statically imbalanced MIs, thereby increasing the OPD of a single MI by factor of N + 1. An FTS spectrometer consisting of N = 2 such MIs is fabricated and experimentally characterized using unknown broad bandwidth input signal spectra of about 180 nm centered around 1550 nm, a narrow line laser input signal, and a combination of broad and narrow band signals demonstrating spectral resolution of about 0.16 nm.

An oblique projection modification technique (OPMT) for fast multispectral CT reconstruction

In x-ray multispectral (or photon-counting) computed tomography (MCT), the object of interest is scanned under multiple x-ray spectra, and it can acquire more information about the scanned object than conventional CT, in which only one x-ray spectrum is used. The obtained polychromatic projections are utilized to perform material-selective and energy-selective image reconstruction. Compared with the conventional single spectral CT, MCT has a superior material distinguishability. Therefore, it has wide potential applications in both medical and industrial areas. However, the nonlinearity and ill condition of the MCT problem make it difficult to get high-quality and fast convergence of images for existing MCT reconstruction algorithms. In this paper, we proposed an iterative reconstruction algorithm based on an oblique projection modification technique (OPMT) for fast basis material decomposition of MCT. In the case of geometric inconsistency, along the current x-ray path, the oblique projection modification direction not only relates to the polychromatic projection equation of the known spectrum, but it also comprehensively refers to the polychromatic projection equation information of the unknown spectra. Moreover, the ray-by-ray correction makes it applicable to geometrically consistent projection data. One feature of the proposed algorithm is its fast convergence speed. The OPMT considers the information from multiple polychromatic projection equations, which greatly speeds up the convergence of MCT reconstructed images. Another feature of the proposed algorithm is its high flexibility. The ray-by-ray correction will be suitable for any common MCT scanning mode. The proposed algorithm is validated with numerical experiments from both simulated and real data. Compared with the ASD-NC-POCS and E-ART algorithms, the proposed algorithm achieved high-quality reconstructed images while accelerating the convergence speed of them.

High Angular Resolution for 77GHz FMCW Radar via a Sparse Weighted Quadratic Minimization

Combining the covariance matching criterion with sparse representation, much effort was devoted to improve the angular resolution. However, the requirement of hyperparameters or the high sidelobe makes them unsatisfactory in practical radar applications. In this paper, we propose a Sparse Capon-like Weighted Quadratic Estimator (SCWQE) with hyperparameter-free and apply it to a 77GHz Frequency Modulated Continuous Wave (FMCW) radar for the high-resolution Direction Of Arrival (DOA) estimation. Under the assumption of the uncorrelated sources, SCWQE is formulated by converting the covariance matching criterion to a quadratic function with respect to the unknown power spectrum, in which a symmetrical and non-diagonal matrix called as the Capon-like spectrum is embedded into the quadratic form. The consequent multiple weighting operation would further promote the sparsity because multiple and different weighting values are exerted on each element of the spatial power spectrum. This is fundamentally different from the traditional weighted approach that employs the diagonal weighting matrix and assigns a single weighting value to each element. Therefore, SCWQE could further eliminate the spurious peaks and sharpen the desired peaks. Numerical examples including simulations and actual data collected from a 77GHz FMCW radar sensor demonstrate that, the proposed SCWQE algorithm produces better accuracy of DOA estimation and higher angular resolution compared to some typical sparse recovery methods.

Neural Network-Based On-Chip Spectroscopy Using a Scalable Plasmonic Encoder.

Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral reconstruction framework using a compact and low-cost on-chip sensing scheme that is not constrained by many of the design trade-offs inherent to grating-based spectroscopy. The system employs a plasmonic spectral encoder chip containing 252 different tiles of nanohole arrays fabricated using a scalable and low-cost imprint lithography method, where each tile has a specific geometry and thus a specific optical transmission spectrum. The illumination spectrum of interest directly impinges upon the plasmonic encoder, and a CMOS image sensor captures the transmitted light without any lenses, gratings, or other optical components in between, making the entire hardware highly compact, lightweight, and field-portable. A trained neural network then reconstructs the unknown spectrum using the transmitted intensity information from the spectral encoder in a feed-forward and noniterative manner. Benefiting from the parallelization of neural networks, the average inference time per spectrum is ∼28 μs, which is much faster compared to other computational spectroscopy approaches. When blindly tested on 14 648 unseen spectra with varying complexity, our deep-learning based system identified 96.86% of the spectral peaks with an average peak localization error, bandwidth error, and height error of 0.19 nm, 0.18 nm, and 7.60%, respectively. This system is also highly tolerant to fabrication defects that may arise during the imprint lithography process, which further makes it ideal for applications that demand cost-effective, field-portable, and sensitive high-resolution spectroscopy tools.

Development of a Gamma-Ray Radionuclide Library for the Identification of Gamma Spectra

Since radionuclides have unique characteristic gamma-ray spectra, usually maintained as a set of (energy, counts/energy) ordered pairs, an explicit functional representation would be indisputably useful. In this paper, the Gamma Detector Response and Analysis Software has been used to simulate the gamma-ray spectra as it would be collected by an NaI detector for a set of 70 radionuclides. Gaussian radial basis function (RBF) networks that offer simple, closed-form expressions are then trained to represent each of these spectra. Hence, a library consisting of 70 RBF networks for the corresponding radionuclides has been built. The presence of these library-contained radionuclides in a given gamma-ray spectrum of an unknown source is identified by an algorithm that employs a linear combination of the library spectra to approximate the unknown spectrum. The combination coefficients are then determined by minimizing the squared deviation error function under convexity constraints.

SOS and HOS Performance for Processing Satellite Relayed Signal.(Dept.E)

Signal detection in the presence of additive Gaussian noise can be performed using higher order statistics. This paper deals with a processing of single and multiple emergency signals that relayed from the low polar orbit satellite using parametric spectral estimation techniques with Second and Higher Order Statistics. The detection algorithm based on the Second Order Statistics (SOS) using the parametric techniques, which are the Maximum Entropy Method (MEM), and MUltiple Signal Classification (MUSIC) is designed and examined in this paper. Simulation results illustrate successful performance of signal detection at low signal to noise ratio. These proposed methods suppress additive Gaussian noise of unknown power spectrum, detect and characterize the main properties of the transmitted signals.

Measurement and simulation of the new liquid organic scintillator response to fast neutrons

Liquid organic scintillators are important devices for measurements of neutron radiation. This work aims to develop and optimize the composition of liquid organic scintillators so it can be used for fast neutron spectrometry. As the neutron radiation is usually accompanied with γ ray radiation, it is important to have quality γ/n discrimination. The new cocktail for house made liquid organic scintillator is prepared and studied with intention of being able to separate gamma and neutron for neutron energies above 0.5 MeV while keeping lower constraints on practical use (e.g., sealing because of oxygen) than commercial liquid scintillators. In preceding work the composition of liquid scintillators was optimized. Two twocomponent scintillators were selected for further studies. Solvent DIPN (Di-iso-propyl-naphthalene Mixed Isomers) is selected for both. First is mixed with luminophore PYR (1-Phenyl-3-(2,4,6-trimethyl-phenyl)-2-pyrazoline) of concentration 5 g/l. Second is mixed with luminophore THIO (2,5-Bis(5-tert-butyl-benzoxazol-2-yl)thiophene) of concentration 5 g/l. In this work the response of scintillator to monoenergetic beam of neutrons was measured for multiple neutron energies at PTB in Braunschweig. The two parameter spectrometric system NGA-01 is used to analyze the energy and discrimination characteristics. 137 Cs and 60 Co are used as radiation sources for calibration with pure γ rays. Then the response of scintillator for same neutron energies was simulated using GEANT4. The dissipated energy in the scintillator in response to monoenergetic neutrons is obtained. Both, measured and simulated responses are compared. Functional dependence for yield of recoiled products is estimated. It is seen that main recoil product hydrogen proton is well observed in both. From the edge of proton response one can assume the yield for given neutron energy. The recoiled carbon ion (from elastic collision) is on the other side difficult to observe in measured results but clearly seen in dissipated energy plots. It suggests that yield of carbon ion is very small relatively to proton yield. These results will serve as basis for response function evaluation of scintillator which is necessary for evaluation of unknown neutron spectra from measurements with scintillator.

Unlocking the decoding of unknown magnetic nanobarcode signatures.

Magnetic nanowires (MNWs) rank among the most promising multifunctional magnetic nanomaterials for nanobarcoding applications owing to their safety, nontoxicity, and remote decoding using a single magnetic excitation source. Until recently, coercivity and saturation magnetization have been proposed as encoding parameters. Herein, backward remanence magnetization (BRM) is used to decode unknown remanence spectra of MNWs-based nanobarcodes. A simple and fast expectation algorithm is proposed to decode the unknown remanence spectra with a success rate of 86% even though the MNWs have similar coercivities, which cannot be accomplished by other decoding schemes. Our experimental approach and analytical analysis open a promising direction towards reliably decoding magnetic nanobarcodes to expand their capabilities for security and labeling applications.

Calibration methods to circumvent unknown component spectra for quantitative in situ Raman monitoring of co-polymerization reactions

PLS, MMSR, and BTEM-MLR were successfully applied to Raman monitoring of co-polymerization.

NO<sub>2</sub> gas detection based on standard sample regression algorithm and cavity enhanced spectroscopy

Cavity-enhanced absorption spectroscopy is a highly sensitive trace gas measurement technology, and the algorithm for retrieving gas concentrations is critical. The absorption cross-section is traditionally used to retrieve the concentration. However, the absorption cross-section used in the fitting process is affected not only by the response function of the instrument and the light source, but also by temperature and pressure. The uncertainty of the absorption cross-section will influence the accuracy of the result. Therefore, in order to eliminate the measurement error introduced by the uncertainty of the absorption cross-section and the instrument response function, a concentration regression algorithm based on the absorption spectrum of the standard sample is proposed. The process of concentration inversion is optimized. The absorption spectrum of standard gas is used to fit the unknown spectrum. In order to verify the effectiveness of the algorithm, the incoherent cavity enhanced absorption spectroscopy (IBBCEAS) system based on a blue light-emitting diode (LED) operating at 440 nm is established to analyze the absorption spectrum of NO<sub>2</sub>; and the fitting effect, measurement accuracy and stability are compared with the counter parts from the traditional absorption cross-section method. In the experiment, the measured reflectance of the cavity mirror is 99.915%. Compared with the conventional absorption cross-section regression algorithm, the standard sample regression algorithm proposed in this paper shows good results, in which the measurement accuracy is increased by about quadruple. The Allan deviation shows that a detection limit of 5.3 ppb can be achieved at an integration time of 360 s. Finally, the performance of the experimental system is evaluated by measuring the NO<sub>2</sub> with different concentrations prepared by standard samples. The result shows good agreement with the theoretical value, which indicates that the improved spectral analysis algorithm is feasible and reliable for gas detection. This method can be used not only to measure NO<sub>2</sub>, but also to detect other gases, which shows great potential applications in monitoring the industrial emissions, atmospheric chemistry and exhaled breath analysis.

Magnetic Nanowires toward Authentication

AbstractMagnetic nanowires (MNWs) have been shown to be promising candidates for nanobarcoding and biolabeling over decades. However, surprisingly, they have never been realized successfully for these applications due to the lack of a reliable encoding strategy that allows reliable decoding of readout signatures. Here, a novel encoding/decoding approach is reported by tailoring the MNWs remanence spectrum that makes decoding the magnetic nanobarcodes feasible even though there is no prior knowledge regarding the readout remanence spectrum. The remanence spectra of several MNWs is tuned by varying their composition, dimensions, and arrangements to generate several distinct nanobarcodes. Furthermore, a simple automated decoding algorithm is illustrated to identify and verify the type and number of MNWs in unknown remanence spectra. The experimental and analytical approaches show that the remanence spectrum is a strong probe for reliable encoding/decoding because it employs three authentication factors that significantly enhance their security performance in nanobarcoding and biolabeling.

A probabilistic class-modelling method based on prediction bands for functional spectral data: Methodological approach and application to near-infrared spectroscopy

Class-modelling methods aim to predict the conformity of new unknown samples with a single target class, using statistical decision rules built exclusively with objects of that class. This article introduces a novel class-modelling method for spectral data. The method uses the concept of β%-prediction band for functional data to classify spectra. The band is defined by an upper and a lower limiting spectra which delimit critical trajectories for β% of future spectra of the target class. It is constructed in three main steps: firstly, a naïve bootstrap sample of calibration spectra is projected onto a parsimonious principal component (PC) basis and their scores are estimated. The posterior predictive distribution of the scores on each PC is estimated using a Bayesian zero-mean normal model. This procedure is repeated on naïve bootstrap estimations of the PCs to obtain the predictive distribution of the scores. These enable to account for all modelling uncertainties including the random deviation of scores from their zero-mean on each PC, uncertainty in the variance of scores (eigenvalue) on each PC, and uncertainty in the PC estimations. Secondly, the predicted scores are back-transformed to the original signal scale to obtain the predictive distribution of future spectra. Thirdly, the predicted spectra are ranked to select the β% most central ones as typical set, whose ranges of variation are used to construct the simultaneous limits of the band. Once the band is constructed, reconstructions of future unknown test spectra by bootstrap PC models are projected onto it, and the extent to which they overlap with it is used to decide their acceptance or rejection. The statistical properties and classification performances of the proposed prediction band are evaluated on real near-infrared datasets and compared to the well-known soft-independent modelling of class analogy (SIMCA) model. The results of the evaluation provide evidence that the proposed prediction band possesses satisfactory predictive performances. It even outperforms the SIMCA while offering attractive advantages like risk-management and straightforward physical interpretability of outlyingness patterns of tested spectra.

Spectral similarity algorithm-based image classification for oil spill mapping of hyperspectral datasets

In remote sensing, the compositional information of part of the earth’s surface is statistically evaluated by comparing known field or library spectra with the unknown image spectra, known as spectral matching or spectral similarity analysis. In this research, hybrid spectral similarity algorithms developed based on chi-square distance (CHI or χ2) are used to retrieve useful information from the Hyperion hyperspectral oil spill image covering the area near Liaodong Bay of the Bohai Sea, China. In order to evaluate the discriminability of spectral similarity algorithms, a pixel-level matching is carried out between the reference vectors, viz. Oil Slick (O), Sheen (H), Sea Water (S) and Ship Track (T), collected visually from known areas in the image. The hybrid spectral similarity algorithms are statistically assessed for their performance using the spectral discriminatory measures (i) relative spectral discriminatory power (RSDPW), (ii) relative spectral discriminatory probability (RSDPB) and (iii) relative spectral discriminatory entropy (RSDE). Additionally, the selected hybrid algorithms are used on the Hyperion image subset to perform a pixel-based classification. Classification results revealed that the CHI-based hybrid algorithms performed better than all other hybrid spectral similarity methods. Therefore, the CHI-based hybrid algorithms demonstrated their superior spectral discrimination capacity to classify marine spectral classes for oil spill mapping from the hyperspectral dataset.

Quantitative K-Cl-S chemistry in thermochemical conversion processes using in situ optical diagnostics

The sulfation of gas-phase KOH and KCl was investigated in both oxidizing and reducing atmospheres at temperatures of 1120 K, 1260 K, 1390 K, and 1550 K. Well-defined gas environments were generated in a laminar flame burner fuelled with CH4/air/O2/N2. Atomized K2CO3 and KCl water solution fog and SO2 were introduced into the hot gas as sources of potassium, chlorine, and sulfur, respectively. The in situ concentrations of KOH, KCl, and OH radicals were measured using broadband UV absorption spectroscopy, and the concentration of K atom was measured using TDLAS at 769.9 nm and 404.4 nm. The nucleated and condensed K2SO4 aerosols were visualized as illuminated by a green laser sheet. With SO2 addition, reduced concentrations of KOH, KCl, and K atom were measured in the hot gas. The sulfation was more significant for the low temperature cases. KOH was sulfated more rapidly than KCl. K2SO4 aerosols, formed by homogeneous nucleation, were observed at temperatures below 1260 K. At 1390 K, no aerosols were formed, indicating that the consumed KOH was transformed into gaseous KHSO4 or K2SO4. K atoms formed in the hot flue gas with KOH addition enhanced the consumption of OH radicals except at the high-temperature case at 1550 K. At 1120 K and 1260 K, the sulfation of KOH with SO2 seeding reduced the concentration of K atom, resulting in less OH radical consumption. Studies were also conducted in a hot reducing environment at 1140 K, with the flame at an equivalence ratio of 1.31. Similar to the observation in the oxidizing atmosphere, the concentrations of KOH and K atom decreased dramatically with SO2 seeding. An unknown absorption spectrum observed was attributed to UV absorption by KOSO. The experimental results were used to evaluate a detailed K-Cl-S reaction mechanism, and a reasonable agreement was obtained.

New detrital zircon age data reveal the location of the Rheic suture in the Mid-German Crystalline Zone (Spessart and Odenwald Crystalline Complexes)

New detrital zircon age data of metasedimentary rocks from the Spessart and Odenwald basement (Mid-German Crystalline Zone, Variscides) revealed results, which are not compatible with current tectonic models. The previously proposed existence of a tectonic window, which includes lower plate Rhenohercynian rocks within the upper plate (Saxothuringian Zone) in the Spessart and Odenwald basement, does not agree with the results of this study. This leads to a new course of the Rheic suture and, therefore, a non-coherence of the two major fault zones in these complexes. The southern Spessart Crystalline Complex accommodates the Rheic suture, which is most likely explained by a southward displacement of a whole segment of the Mid-German Crystalline Zone. This displacement might extend over the Ruhla Crystalline Complex. A hitherto unknown age spectrum of a unit in the Bollsteiner Odenwald was found, which might indicate a sliver of unidentified material in the Variscan collision zone. An unknown magmatic age of 350 Ma is reported for igneous intrusions in the Saxothuringian Zone and can be used as an additional indicator to distinguish between Laurussia- and Gondwana-related rocks.