Spectral Deconvolution Method Research Articles

Characterization and classification of pathogenic bacteria using native fluorescence and spectral deconvolution.

Identification and classification of pathogenic bacterial strains is of current interest for the early treatment of diseases. In this work, protein fluorescence from eight different pathogenic bacterial strains were characterized using steady state and time resolved fluorescence spectroscopy. The spectral deconvolution method was also employed to decompose the emission contribution from different intrinsic fluorophores and extracted various key parameters, such as intensity, emission maxima, emission line width of the fluorophores, and optical redox ratio. The change in average lifetime values across different bacterial strains exhibits good statistical significance (p ≤ 0.01). The variations in the photophysical characteristics of bacterial strains are due to the different conformational states of the proteins. The stepwise multiple linear discriminate analysis of fluorescence emission spectra at 280 nm excitation across eight different bacterial strains classifies the original groups and cross validated group with 100% and 99.5% accuracy, respectively.

Iron depletion in mineral dust grains from Saturn’s main rings

ABSTRACT During the Grand Finale orbits, Cassini’s Cosmic Dust Analyzer (CDA) recorded in situ mass spectra of ice and mineral nanodust grains ejected from Saturn’s main rings falling into the planet’s atmosphere. We present a compositional analysis of the mineral dust fraction employing a spectral deconvolution method to determine the elemental composition of these grains. The results indicate a relatively homogenous composition of exclusively Mg-rich silicates, with Mg, Si, and Ca close to CI chondritic abundances but a significant depletion in Fe and only traces of organic material at best. The Fe depletion becomes even more pronounced when compared to Fe-rich interplanetary dust particles encountered by CDA in the Saturnian system, which are assumed to contaminate and darken the main rings over time. We discuss potential explanations for the depletion, from which we favour compositional alteration of the infalling dust grains by impact-triggered chemistry in combination with dynamical selection effects and instrumental bias as the most plausible ones. This might cause an accumulation of Fe in the main rings over time, most likely in the form of oxides.

A comprehensive guide for measuring total vanadium concentration and state of charge of vanadium electrolytes using UV–Visible spectroscopy

This paper presents an exhaustive how-to guide on measuring the total vanadium concentration and state of charge of vanadium electrolytes using UV–Visible spectroscopy. The study is provided with an open-access database (GitHub) that supports the methods and procedures and facilitates access to the calibration data. The study covers the three types of electrolyte solutions relevant to vanadium redox flow batteries, namely the anolyte VII/VIII, the catholyte VIV/VV, and the VIII/VIV commercial electrolyte, meant to be preconditioned to either VIII or VIV before battery operation. Analytical expressions to calculate the concentration of different vanadium species in the electrolyte solutions are provided based on either empirical correlations or spectral deconvolution methods. The paper also examines the limitations of the measurement technique and provides insightful recommendations for future research. The open-access database provided by the authors is expected to serve as a valuable repository for scholars and scientists working in the field of vanadium redox flow batteries.

Experimental strategies to improve drug-target identification in mass spectrometry-based thermal stability assays

Mass spectrometry (MS)-based thermal stability assays have recently emerged as one of the most promising solutions for the identification of protein-ligand interactions. Here, we have investigated eight combinations of several recently introduced MS-based advancements, including the Phase-Constrained Spectral Deconvolution Method, Field Asymmetric Ion Mobility Spectrometry, and the implementation of a carrier sample as improved MS-based acquisition approaches for thermal stability assays (iMAATSA). We used intact Jurkat cells treated with a commercially available MEK inhibitor, followed by heat treatment, to prepare a set of unfractionated isobarically-labeled proof-of-concept samples to compare the performance of eight different iMAATSAs. Finally, the best-performing iMAATSA was compared to a conventional approach and evaluated in a fractionation experiment. Improvements of up to 82% and 86% were demonstrated in protein identifications and high-quality melting curves, respectively, over the conventional approach in the proof-of-concept study, while an approximately 12% improvement in melting curve comparisons was achieved in the fractionation experiment.

Application of a new spectral deconvolution method for in vitro dosimetry in assessment of targeted alpha therapy.

The improvement of in vitro assessment of targeted alpha therapy (reproducibility, comparability of experiments…) requires precise evaluation of the dose delivered to the cells. To answer this need, a previous study proposed an innovative dosimetry method based on α-spectroscopy and a specific deconvolution process to recover the spatial distribution of 212 Pb isotopes inside in vitro culture wells. Nevertheless, although promising, the deconvolution method was time consuming and only tested for a simple isotope decay chain. The purpose of this work is to propose a new matrix deconvolution method of α spectra based on a constrained-non-negative-maximum-likelihood decomposition, both faster and offering a greater modelling flexibility, allowing to study independently the kinetics of each of the daughter nuclides of complex decay chains (illustrated here with 223 Ra) in in vitro culture wells. Firstly, the performance of the new method was fully evaluated through Monte Carlo simulations of in vitro irradiations. Different spatial distributions of 212 Pb and 223 Ra, the corresponding α spectra measured by a silicon detector and the doses delivered to the cells were simulated with Geant4. The deconvolution results were then compared to the simulation results. Secondly, measurements were carried out in culture wells without cells containing 15kBq of 212 Pb or 9.3kBq of 223 Ra, placed above silicon detectors recording α spectra in real time. The matrix deconvolution was then applied to determine the spatial and temporal distribution of all α-emitting daughters of studied isotopes. The matrix deconvolution was proved to recover the simulated distribution gradients, ensuring simulated doses within 3% for both tested radionuclides, with errors on dose normally distributed around the reference value (consequently not exhibiting any bias), even in the case of complex decay chains as 223 Ra. The experimental study of 212 Pb and 223 Ra showed highly inhomogeneous distributions and time evolution of the concentration gradients, consistent with the previous study. Furthermore, it highlighted the complex kinetics of 223 Ra with different distributions of its α-emitting daughters (219 Rn, 215 Po, 215 At, 211 Bi, 211 Po). This study validates a new deconvolution method, fast and flexible, that proved to be accurate and reliable. This method allowed to reveal the complexity of isotopes kinetics in in vitro experiments, especially with complex decay chains. Experimental dosimetry, necessary to improve reliability of in vitro studies in targeted alpha therapy, is demonstrated to be feasible with the proposed method.

Quantitative 1H NMR with global spectral deconvolution approach for the determination of gamma-aminobutyric acid in Chinese yam (Dioscorea polystachya Turczaninow).

We developed a quantitative proton nuclear magnetic resonance (qNMR) with global spectral deconvolution (GSD) method to determine the gamma-aminobutyric acid content in Chinese yam with the proton signal at δH 2.30. Trimethylsilyl-2,2,3,3-tetradeuteropropionic acid sodium salt was set as the internal standard. The method was validated and showed admissible stability, repeatability, and precision. Compared to the traditional high-performance liquid chromatography method, this method did not involve tedious pre-treatment and expensive standard. Compared to ordinary qNMR, GSD algorithm could effectively remove the effect of noise, baseline distortions and signal overlapping. Overall, qNMR with GSD method is a rapid, simple and reliable method to quantitatively determine functional metabolites even overlapped with other compounds in herbs or foods.

Optimization of acquisition and processing times for the measurement of 1H to 13C polarization transfer kinetics

Solid-state NMR (ssNMR) is a unique technique allowing the study of macromolecular assemblies in their native state without prior modification. The proposed method simplifies the current long and tedious data-acquisition and processing protocols for Variable Contact Time (VCT) ssNMR experiments used in dynamic studies of macromolecular assemblies. Using cellulose nanocrystals as a model for polysaccharide assembly, the acquisition time was reduced by decreasing the number of scans and shortening the recycling time required for the 1H to 13C polarization transfers.•A tenfold reduction in the acquisition time for each kinetic point was achieved while maintaining a good signal to noise ratio.•The processing time for the pseudo-2D solid-state NMR data was also shortened by using signal intensities derived from peak picking rather than the classically-used spectral deconvolution method.•These optimizations enabled the molecular dynamic parameters T1ρH, THH, TCH to be accessed within a day.

Bandwidth function matrix-based spectral deconvolution with alternate minimization method

Spectroscopic data often suffer from spectral distortion and random noise. In this paper, a novel bandwidth function matrix-based spectral deconvolution with the alternate minimization method is proposed to recover the degraded spectra. Different from traditional spectral deconvolution methods which only employ or estimate one bandwidth function, the proposed method introduces a bandwidth function matrix composed of multiple bandwidth functions corresponding to the whole wavelength range of the spectrometer, and applies the alternate minimization method to alternately estimates the real spectrum and bandwidth function matrix until convergence. As a result, the correction error caused by the differences in bandwidth functions in the whole spectral band could be effectively eliminated. A series of experiments was carried out for various simulated and real measured spectra, and comparative results demonstrate the superior performance of the proposed method comparing with the traditional methods.

An approximate deconvolution method for the luminescence intensity ratio calculations from overlapping emissions

Two experimental practices in luminescence intensity ratio (LIR) thermometry based on the spectral deconvolution (SD) and separation at chosen wavelength (WS) were analyzed for the case when sharp emission is overlapping with a broad one, which is frequently the case for Cr3+ and Mn4+ in the intermediate or strong crystal fields. Their performances were compared showing that, depending on several parameters, separation at chosen wavelength method gives lower sensitivities than spectral deconvolution one. With requirement that the sharp emission quenches at high temperatures, which is also the case for the mentioned luminescent centers, an alternative method, named approximate deconvolution (AD) is proposed. The novel method retains the simplicity of the WS, while matching the sensitivities of the SD method. The detailed algorithm for the AD method is presented. Although the temperature resolution is not improved, the AD method allows fit to the Boltzmann equation and extraction of the physically relevant parameters. All three methods are compared using Cr3+ emission in LiAl5O8, MgAl2O4 and YAG showing that WS gives significantly lower sensitivities than SD and AD, while SD and AD are highly matching in performance.

Study of the Evolution of Pigments from Freshly Pressed to 'On-the-Shelf' Extra-Virgin Olive Oils by Means of Near-UV Visible Spectroscopy.

Spectroscopic non-destructive methods have high potentialities as fast, cheap and easy-to-be-used approaches to address olive oil quality and authenticity. Based on previous research where near-UV Visible spectroscopy was used to investigate extra-virgin olive oils (EVOOs) and their main pigments’ content (i.e., β-carotene, lutein, pheophytin a and pheophytin b), we have implemented the spectral deconvolution method in order to follow the EVOO’s life, from ‘freshly pressed’ to ‘on-the-shelf’ EVOO samples at different storage time. In the first part of the manuscript, the new implemented deconvolution spectroscopic method aimed to quantify two additional pigments, namely chlorophyll a and chlorophyll b, is described and tested on ‘ad hoc’ samples with known concentrations of chlorophylls. The effect of light exposure and acidification was investigated to test the reliability and robustness of the spectral deconvolution. In the second part of the work, this approach was used to study the kinetic of pigments’ degradation in several monocultivar fresh EVOO samples under optimal storage’s conditions. The results here reported show that this spectroscopic deconvolution approach is a good method to study fresh EVOOs too; moreover, the proposed method revealed to be sensitive to detect eventual stresses of olive oil samples stored in not-good conditions.

ANALYSIS OF GAMMA-RAY SPECTRUM OBTAINED WITH CDZNTE-DETECTORS USING THE ROOT CERN SOFTWARE FRAMEWORK

Semiconductor CdZnTe gamma spectrometers allow the implementation of new radiation monitoring technologies. However, the energy resolution from 5 to 10 keV and the small size of the detector may result in the detector being unable to resolve photopeaks with very close energies. Algorithms deduce the source identity from the spectrum and must rely on a well-established peak separation in cases where multiple sources are involved. Some techniques exist, but they are computationally expensive, both in memory and processor speed. Typically they can lead to critical performance problems when run on single board computers. To solve this problem, spectral deconvolution methods such as Gold or Richardson-Lucy algorithmshave been proposed, that somewhat resolve the complex spectrum. The realized functions of spectra processing implemented in the ROOT CERN allow solving the indicated contradictions. The article presents the results of using ROOT CERN on a single-board computer for analyzing the spectra measured with a CdZnTe spectrometer. The results show that the spectral deconvolution methods used have high accuracy and efficiency in the deconvolution of complex spectra.

Ras–guanine nucleotide complexes: A UV spectral deconvolution method to analyze protein concentration, nucleotide stoichiometry, and purity

The many members of the Ras superfamily are small GTPases that serve as molecular switches. These proteins bind the guanine nucleotides GTP and GDP with picomolar affinities, thereby stabilizing on- and off-signaling states, respectively. Quantitative in vitro Ras studies require accurate determination of total protein, its fractional occupancy with guanine nucleotide, and spectroscopic purity. Yet the high nucleotide affinity of Ras and the overlapping UV spectra of the protein and bound nucleotide make such determinations challenging. Here we describe a generalizable UV spectral deconvolution method to analyze the total protein concentration, total nucleotide stoichiometry, and purity of Ras complexes.

EISRP: Efficient infrared signal restoration processing for object tracking in human-robot interaction

Infrared (IR) imaging spectral signal can accurately distinguish different industrial metal materials in the robot vision sensing. The novel IR imaging system can assist the robot limb to gasp the object in the human-robot interaction processing. However, the low-resolution problem is often existed in the IR spectral signals. To solve this issue, we present a novel IR spectral deconvolution method via total variation regularization for robust spectral signal recognition. To reveal the properties of high-resolution IR spectrum, the histogram of spectral gradients is computed and fitted by the logarithmic function. Then, the low-resolution and high-resolution IR spectra can be distinguished by total variation-regularized prior property. To optimize the non-convex deconvolution model, alternating minimization algorithm is introduced to ensure that the instrument function and high-resolution spectral signals converge quickly. The performance of the proposed method is evaluated on the simulated IR spectrum and real spectrum. A large number of quantitative and qualitative experimental results verify that the proposed method can obtain robust results and significantly outperform the existing state-of-the-art spectral deconvolution method. The high-resolution IR spectrum promotes the development of IR spectral recognition in human-robot interaction.

EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution.

Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation.

Polarization gating technique extracts depth resolved fluorescence redox ratio in oral cancer diagnostics.

Mortality of oral cancer is often due to late diagnosis. Effective non-invasive diagnostic techniques may increase the survival rate based on an earlier diagnosis.. We report on the application of the polarization gating technique for isolating weakly scattered and highly scattered components of fluorescence emission from the superficial and deeper layers of tissue due to intrinsic fluorophores NADH and FAD. The fluorescence polarization spectra were collected from 21 normal and 67 oral squamous cell carcinoma biopsy tissues. The tissues were excited at 350 nm and the fluorescence emission had two peaks corresponding to NADH, and FAD respectively. The spectra were analyzed using the polarization gating technique along with the spectral deconvolution method to derive the optical redox ratio from different layers of tissue. The fractional change in redox ratio between superficial and deeper layers of tissue exhibits excellent statistical significance (p<10-3) which may be due to a shift in the metabolic pathway from oxidative phosphorylation to glycolysis in the cancer cell. Further, variation in collagen intensity in deeper layers of tissue is observed which may be attributed to the breakdown of collagen fibers in the stroma. Linear discriminant analysis showed that oral cancer tissue is discriminated with a better accuracy using polarization gating technique than that of conventional fluorescence spectroscopy.

Spectral deconvolution associated to the Gaussian fit as a tool for the optimization of photovoltaic electrocoagulation applied in the treatment of textile dyes

The objective of this study was to investigate the color removal in a binary mixture of azo dyes from the photovoltaic electrocoagulation (EC) technique, using spectral deconvolution and the Gaussian fit for qualitative and quantitative determination of the physical color parameter. Initially, a conventional energy source was used to feed the EC reactor and the experimental design was conducted according to the Rotational Central Compound Design (RCCD). The spectral deconvolution method associated to the Gaussian fit aided in the description of the composition of the sample matrix, In the first step, through the Analysis of Variance, the RCCD and the three-dimensional surface response graphs, the optimized operating conditions were identified, which corresponded to 1320 A m−2 with an reaction time of 16.6 min, and an expected removal of 98.40% for Scarlet Red (SR) dye and 1160 A m−2 with a run time of 15.7 min and 97.9% removal for Turquoise Blue (TB) dye. Using the photovoltaic module as the power source of the EC reactor, a maximum removal of 97 ± 0.43% for TB dye and 98% ± 0.81 for SR was obtained. The results encourage the applicability of photovoltaic module-fed EC technology as a promising alternative for the treatment of effluents containing textile dyes, as well as the use of the spectral deconvolution method associated with the Gaussian fit, for the reliability and precision of the results.

Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm

Spectral distortion often occurs in spectral data due to the influence of the bandpass function of the spectrometer. Spectral deconvolution is an effective restoration method to solve this problem. Based on the theory of the maximum posteriori estimation, this paper transforms the spectral deconvolution problem into a multi-parameter optimization problem, and a novel spectral deconvolution method is proposed on the basis of Levenberg-Marquardt algorithm. Furthermore, a spectral adaptive operator is added to the method, which improves the effect of the regularization term. The proposed methods, Richardson-Lucy (R-L) method and Huber-Markov spectroscopic semi-blind deconvolution (HMSBD) method, are employed to deconvolute the white light-emitting diode (LED) spectra with two different color temperatures, respectively. The correction errors, root mean square errors, noise suppression ability, and the computation speed of above methods are compared. The experimental results prove the superiority of the proposed algorithm.

Bayesian-based spectral deconvolution with genetic algorithm

Spectral distortions exist widely due to the broadening effects of spectrometer. In this paper, a spectral deconvolution method using genetic algorithm is proposed to solve this problem. In the Bayesian framework, the spectral deconvolution model is constructed with Gaussian noise and Poisson noise hypothesis, and the prior term is constructed with adaptive Gauss-Markov priori. Genetic algorithm is employed to optimize the spectral deconvolution model to obtain the corrected spectrum. To verify the effectiveness of the proposed method, simulated degraded LED spectra with different colour temperature are corrected by the proposed method, Richardson-Lucy method and Levenberg-Marquardt method. Experimental results show that the proposed method can correct the spectra effectively.

Size-Dependent Biexciton Spectrum in CsPbBr3 Perovskite Nanocrystals

We characterize exciton–exciton interactions in weakly confined CsPbBr3 nanocrystals by combining fluence-dependent transient absorption spectroscopy with a robust spectral deconvolution method. Th...

Impact of the Conformational Variability of Oligopeptides on the Computational Prediction of Their CD Spectra.

Although successful in the structural determination of ordered biomolecules, the spectroscopic investigation of oligopeptides in solution is hindered by their complex and rapidly changing conformational ensemble. The measured circular dichroism (CD) spectrum of an oligopeptide is an ensemble average over all microstates, severely limiting its interpretation, in contrast to ordered biomolecules. Spectral deconvolution methods to estimate the secondary structure contributions in the ensemble are still mostly based on databases of larger ordered proteins. Here, we establish how the interpretation of CD spectra of oligopeptides can be enhanced by the ability to compute the same observable from a set of atomic coordinates. Focusing on two representative oligopeptides featuring a known propensity toward an α-helical and β-hairpin motif, respectively, we compare and cross-validate the structural information coming from deconvolution of the experimental CD spectra, sequence-based de novo structure prediction, and molecular dynamics simulations based on enhanced sampling methods. We find that small conformational variations can give rise to significant changes in the CD signals. While for the simpler conformational landscape of the α-helical peptide de novo structure prediction can already give reasonable agreement with the experiment, an extended ensemble of conformers needs to be considered for the β-hairpin sequence.