Spectral Deconvolution Method Research Articles

Monitoring Breast Cancer Response to Treatment Using Stokes Shift Spectroscopy of Blood Plasma.

With the emerging trend of personalized cancer treatment, there is a need to develop noninvasive/minimally invasive techniques for treatment monitoring. In this regard, in this work fluorescence analysis of blood plasma of breast cancer patients has been used for the evaluation of response to treatment. This approach delivers information not only about the change in biochemical constituents but also about the altered metabolic pathway. Spectral deconvolution method is employed to compute the fluorescence intensity, peak wavelength, and full-width half maxima for different endogenous fluorophores. The fluorescence measurements were made on blood plasma collected from 10 normal subjects, 10 pre-treated cancer patients, and 10 post-treated patients. Besides, variations in relative concentration of tryptophan, collagen, NADH, and FAD, peak shifts and broadening of peaks are observed for tryptophan, NADH, and FAD, in blood plasma of pre-treated cancer patients indicating both biochemical and microenvironmental changes at cellular level. Further, the spectral profile of blood plasma of post-treated patients found to be similar to blood plasma of normal subjects. Linear discriminant analysis showed that pre-treated and post-treated breast cancer is discriminated with a sensitivity and specificity of 100% and 100% respectively.

Synchronous Luminescence Spectroscopy as a Tool in the Discrimination and Characterization of Oral Cancer Tissue.

High incidence of oral cancer is primarily due to ongoing tobacco epidemic. In this work, synchronous luminescence spectroscopy (SLS) has been used to characterize and discriminate oral cancer tissue. Spectral deconvolution method is employed to compute the fluorescence intensity, peak wavelength, and full width half maxima for different endogenous fluorophores. The fluorescence measurements were made on 21 normal and 88 oral squamous cell carcinoma biopsy tissues. Besides, variations in relative concentration of collagen, NADH, and FAD, peak shifts and broadening of peaks are observed for tryptophan, NADH, and FAD, in oral cancer tissues indicating both biochemical and micro environmental changes at cellular level. Linear discriminant analysis showed that oral cancer tissue is discriminated with a sensitivity and specificity of 100% and 95.2% respectively.

A Novel Fault Diagnosis Method of Gearbox Based on Maximum Kurtosis Spectral Entropy Deconvolution

Minimum entropy deconvolution (MED) is widely used in the gearbox fault diagnosis because it can enhance the energy of the impact signal. However, it is sensitive to single abnormal impulsive oscillation. This is because it takes kurtosis as the objective function and solves the optimal filter by iteration. In addition, the filter length is not adaptive and needs to be determined artificially. This paper proposes a maximum kurtosis spectral entropy deconvolution (MKSED) method and applies it to bearing fault diagnosis. Considering that the kurtosis spectral entropy has the advantage of highlighting the continuous impact oscillation, the kurtosis spectral entropy is chosen as the objective function of deconvolution. At the same time, kurtosis spectral entropy is also used as the fitness function of improved local particle swarm optimization algorithm (LPSO), and the filter length is optimized by LPSO, which makes that MKSED adaptively determines the length of the filter while solving the deconvolution, so that it can accurately extract the continuous pulse signal. The results of the simulation signal analysis show that the proposed MKSED method is superior to MED, and the proposed method is applied to bearing fault diagnosis, which verifies its ability to extract continuous impact.

New hybrid metaheuristic algorithm for scintillator gamma ray spectrum analysis

The Sodium Iodide detector (NaI(Tl)) is one of the most widely used nuclear devices in gamma-ray spectrometry due to its high efficiency and low price. However, this detector has low energy resolution and spectra measured by this detector are associated with Gaussian broadening. Therefore, the detector cannot resolve the photopeaks with very close energies. To overcome this problem, spectral deconvolution methods such as boosted ML-EM and boosted Gold algorithms have been proposed, that somewhat resolve the complex spectrum. But these methods cannot obtain a spectrum consisting of narrow photopeaks. Therefore, due to the importance of spectral deconvolution and its applications, there is always a need for a more efficient and precise method. In this study, a new multi-step method based on metaheuristic algorithms is introduced for deconvolution of NaI(Tl) detector spectrum. The new method is used for deconvolution of measured and simulated complex spectra and results are compared with the results of previous methods. The results show that the new multi-step spectral deconvolution method has a very high accuracy and efficiency in deconvolution of the complex spectra.

Micro-Data-Independent Acquisition for High-Throughput Proteomics and Sensitive Peptide Mass Spectrum Identification.

State-of-the-art strategies for proteomics are not able to rapidly interrogate complex peptide mixtures in an untargeted manner with sensitive peptide and protein identification rates. We describe a data-independent acquisition (DIA) approach, microDIA (μDIA), that applies a novel tandem mass spectrometry (MS/MS) mass spectral deconvolution method to increase the specificity of tandem mass spectra acquired during proteomics experiments. Using the μDIA approach with a 10 min liquid chromatography gradient allowed detection of 3.1-fold more HeLa proteins than the results obtained from data-dependent acquisition (DDA) of the same samples. Additionally, we found the μDIA MS/MS deconvolution procedure is critical for resolving modified peptides with relatively small precursor mass shifts that cause the same peptide sequence in modified and unmodified forms to theoretically cofragment in the same raw MS/MS spectra. The μDIA workflow is implemented in the PROTALIZER software tool which fully automates tandem mass spectral deconvolution, queries every peptide with a library-free search algorithm against a user-defined protein database, and confidently identifies multiple peptides in a single tandem mass spectrum. We also benchmarked μDIA against DDA using a 90 min gradient analysis of HeLa and Escherichia coli peptides that were mixed in predefined quantitative ratios, and our results showed μDIA provided 24% more true positives at the same false positive rate.

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

Abstract. Multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare well with other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM1 and PM10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.

Spectral semi-blind deconvolution methods based on modified φHS regularizations

Deconvolution method has been widely used for spectral resolution enhancement. In order to preserve the detailed information and suppress noise better, trimmed φHS regularization and weighted φHS regularization are proposed in this paper. Then the semi-blind deconvolution methods with trimmed φHS regularization (SBD-THS) and with weighted φHS regularization (SBD-WHS) are presented. The results of deconvolving simulated degraded spectra and real experiment spectra demonstrate that SBD-THS and SBD-WHS can enhance spectral resolution effectively while estimating the parameter of blur kernel accurately. In particular, SBD-WHS can produce great performance on preserving local details and suppressing noise.

Step-scan differential Fourier transform infrared photoacoustic spectroscopy (DFTIR-PAS): a spectral deconvolution method for weak absorber detection in the presence of strongly overlapping background absorptions.

The determination of small absorption coefficients of trace gases in the atmosphere constitutes a challenge for analytical air contaminant measurements, especially in the presence of strongly absorbing backgrounds. A step-scan differential Fourier transform infrared photoacoustic spectroscopy (DFTIR-PAS) method was developed to suppress the coherent external noise and spurious photoacoustic (PA) signals caused by strongly absorbing backgrounds. The infrared absorption spectra of acetylene (C2H2) and local air were used to verify the performance of the step-scan DFTIR-PAS method. A linear amplitude response to C2H2 concentrations from 100 to 5000ppmv was observed, leading to a theoretical detection limit of 5ppmv. The differential mode was capable of eliminating the coherent noise and dominant background gas signals, thereby revealing the presence of the otherwise hidden C2H2 weak absorption. Thus, the step-scan DFTIR-PAS modality was demonstrated to be an effective approach for monitoring weakly absorbing gases with absorption bands overlapped by strongly absorbing background species.

Morphology and microstructure of co-pyrolysis char from bituminous coal blended with lignocellulosic biomass: Effects of cellulose, hemicellulose and lignin

Co-pyrolysis of coal and lignocellulose biomass and coal is the key step of other co-thermochemical conversion, and conversion of co-pyrolysis char is the rate-determining step of co-gasification and co-combustion. In this paper, the influence of biomass model compounds (cellulose, hemicellulose, and lignin, abbreviated as CE, HCE and LIG) on the co-pyrolysis char structure transformation was investigated. Carbon structure and surface morphology of co-pyrolysis char were examined by Raman spectroscopy and scanning electron microscope (SEM). A comprehensive comparison of Raman spectral deconvolution methods based on various fitting functions and peak numbers was explored, and Gaussian-Lorentzian-function with no less than nine peaks showed the best performance. Three biomass model show different effects on the transformation of microstructure structure. The addition of CE increased the ordering of char structure. HCE promoted the disordering degree of microstructure structure and reached the maximum at 50% HCE mass ratio. The microstructure structure changes of co-pyrolysis char for 25% and 50% LIG mass ratios were not evident, while 75% LIG increased the disordering degree of the co-pyrolysis char. Fractal analysis was applied for describing the char surface morphology quantitatively with two and three-dimensional fractal dimensions. CE decreased the fractal dimensions of co-pyrolysis, and the influence of HCE and LIG depended on the mass ratio.

Blind deconvolution using the similarity of multiscales regularization for infrared spectrum

Band overlap and random noise exist widely when the spectra are captured using an infrared spectrometer, especially since the aging of instruments has become a serious problem. In this paper, via introducing the similarity of multiscales, a blind spectral deconvolution method is proposed. Considering that there is a similarity between latent spectra at different scales, it is used as prior knowledge to constrain the estimated latent spectrum similar to pre-scale to reduce artifacts which are produced from deconvolution. The experimental results indicate that the proposed method is able to obtain a better performance than state-of-the-art methods, and to obtain satisfying deconvolution results with fewer artifacts. The recovered infrared spectra can easily extract the spectral features and recognize unknown objects.

Spectral mapping of morphological features on the moon with MGM and SAM

Three types of morphological features observed in different lunar crustal terrains were studied and mapped using hyperspectral Moon mineralogy mapper (M3) data onboard Chandrayaan 1 mission in order to assess the utility of cascaded MGM-SAM spectral mixture modeling approach to characterize the surface materials, which may occur as mineral mixtures, at different topography of the lunar surface. Selected morphological features include: the impact melts in Orientale basin, sinuous rilles in Procellarum KREEP Terrane (PKT) and a rayed crater in Feldspathic Highland Terrane (FHT). Methodology involves extraction of spectrally pure pixels (endmembers) of the area using Pixel Purity Index (PPI), identification of mineralogy of the selected endmember spectrum using the Modified Gaussian Method (MGM) and mapping of mineralogically identified endmembers using the Spectral Angle Mapper (SAM) method.Mapping results demonstrate both the capabilities and the limitations of the MGM method of spectral deconvolution and the SAM method of spectral matching as effective tools for compositional characterizations of morphological features on the lunar surface. As a method of spectral deconvolution, MGM was able to identify and characterize both high- and low – Ca pyroxenes along with plagioclase feldspar. The Spectral Angle Mapper (SAM) was able to map identified mineral mixtures from MGM.

Identification and Measurement of Intermolecular Interaction in Polyester/Polystyrene Blends by FTIR-Photoacoustic Spectrometry

Fourier transform infrared photoacoustic spectrometry was used to reveal and identify n–π type intermolecular interaction formed in plastic comprising binary blends of polystyrene and a biodegradable polymer, either polylactic acid, polycaprolactone or poly(tetramethyleneadipate-co-terephthalate). This was the first reported direct and definitive experimental evidence of n–π type bond formation in thermoplastic blends of these polyesters with polystyrene. Also, an infrared method was devised that permits quantitative measurement of the degree of interaction between the two polymers at various concentrations in the blends. The method employs spectral deconvolution by least squares curve fitting of the polymer carbonyl band into its underlying peaks. In a new algorithm the method compares deconvoluted Gaussian/Lorentzian peaks of the polymer blends with deconvoluted peaks in the neat polymers and computes both the magnitude and direction of change in the n–π bond formation with change in polymer concentration. The results indicated that the degree of interaction was dependent on the type and concentration of the biodegradable polymer in the blend. These findings are supported by differential scanning calorimetry and thermogravimetric analyses. Unlike conventional spectral deconvolution methods, this technique with its new algorithm approximates infrared absorptivities of all the underlying peaks, and is thus a superior method that should be applicable to multicomponent polymer blends in general.

Multi-order blind deconvolution algorithm with adaptive Tikhonov regularization for infrared spectroscopic data

Infrared spectra often suffer from common problems of bands overlap and random noise. In this paper, we introduce a blind spectral deconvolution method to recover the degraded infrared spectra. Firstly, we present an analysis of the causes of band-side artifacts found in current deconvolution methods, and model the spectral noise with the multi-order derivative that are inspired by those analysis. Adaptive Tikhonov regularization is employed to preserve the spectral structure and suppress the noise. Then, an effective optimization scheme is described to alternate between IRF estimation and latent spectrum until convergence. Numerical experiments demonstrate the superior performance of the proposed method comparing with the traditional methods.

IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples.

In anatomic pathology, immunohistochemistry (IHC) serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703) of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%). This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and further help in worldwide patient stratification potentially benefitting various multinational clinical trial initiatives.

Maia X-ray fluorescence imaging: Capturing detail in complex natural samples

Motivated by the challenge of capturing complex hierarchical chemical detail in natural material from a wide range of applications, the Maia detector array and integrated realtime processor have been developed to acquire X-ray fluorescence images using X-ray Fluorescence Microscopy (XFM). Maia has been deployed initially at the XFM beamline at the Australian Synchrotron and more recently, demonstrating improvements in energy resolution, at the P06 beamline at Petra III in Germany. Maia captures fine detail in element images beyond 100 M pixels. It combines a large solid-angle annular energy-dispersive 384 detector array, stage encoder and flux counter inputs and dedicated FPGA-based real-time event processor with embedded spectral deconvolution. This enables high definition imaging and enhanced trace element sensitivity to capture complex trace element textures and place them in a detailed spatial context. Maia hardware and software methods provide per pixel correction for dwell, beam flux variation, dead-time and pileup, as well as off-line parallel processing for enhanced throughput. Methods have been developed for real-time display of deconvoluted SXRF element images, depth mapping of rare particles and the acquisition of 3D datasets for fluorescence tomography and XANES imaging using a spectral deconvolution method that tracks beam energy variation.

Spectral Deconvolution and Non-Overlap Bands Sampling for IMS-1 Hyperspectral Imager Data

In this paper, we attempt to realize ‘near true’ spectral signatures of the ground features achievable from IMS-1 hyperspectral Imager (HySI) by employing spectral deconvolution method. From spectral response function of 64 spectral bands, it is first inferred that their spectral bandwidths are higher than the Nyquist sampling, suggesting that the spectral details of ground features are likely to be affected. The proposed approach tries artificially to reduce effective bandwidth of each band, thereby resolving individual band spectrum separable for further spectroscopic investigation. The spectral bands are further sampled to non-overlapping 17 spectral bands at a sampling interval of 25 nm to enable extraction of unique spectral signatures of the ground features.

Laser ablation molecular isotopic spectrometry of water for 1D2/1H1 ratio analysis

Laser Ablation Molecular Isotopic Spectrometry (LAMIS) has been investigated for optical isotopic analysis of the deuterium to protium ratio in enriched water samples in ambient air at atmospheric pressure. Multivariate PLSR (Partial Least Squares Regression) based calibrations were carried out and validated using multiple statistical parameters. Comparisons of results are reported using two spectrometers having two orders of magnitude difference in spectral resolution. The accuracy and precision of isotopic analysis depends on the spectral resolution and the inherent isotope shift of the elements. The requirements for spectral resolution of the measurement system can be significantly relaxed when the isotopic abundance ratio is determined using chemometric processing of the spectra. Large isotopic shifts in the individual rotational branches of OH/OD molecular emission spectra were measured. Optimized temporal conditions for LAMIS measurements were established. Several sub-regions of spectra were used for PLSR calibration and the results demonstrate that both the emission intensity and degree of spectral differentiation affect the quality of the PLSR calibration. LAMIS results also were compared with traditional LIBS results obtained using PLSR and a spectral deconvolution method, demonstrating the advantages of LAMIS over LIBS with respect to isotopic composition determination.

Component analysis of fluorescence spectra of thiol DAB dendrimer/ZnSe-PEA nanoparticles

The fluorescence spectroscopy technique is an accurate method and has great utility in the interpretation of complex systems based on several emission bands. An interpretation of the system requires determination of the number, positions and intensities of the spectral components. In this work, the emission spectra of the synthesized ZnSe complex coated with O-phosphorylethanolamine (ZnSe-PEA), both with and without thiol DAB dendrimer generation 5 (S-DAB G5), were analyzed using a combination of asymmetric (log-normal) and symmetric (Gaussian) models. The method applied for the deconvolution of fluorescence spectra has proven to be very sensitive for observing the stability of the ZnSe-PEA complex after binding with S-DAB. The ZnSe-PEA emission spectrum contains two components. The positions of the emission maxima of these two components are not significantly affected by the presence of S-DAB G5 in the complex, which revealed the presence of a stable complex at a pH of 7. By applying the spectral deconvolution method, strong evidence was obtained that suggested that the ZnSe-PEA complex is stable after complexation with S-DAB G5.

Bayesian spectral deconvolution with the exchange Monte Carlo method

An analytical method to deconvolute spectral data into a number of simple bands is extremely important in the analysis of the chemical properties of matter. However, there are two fundamental problems with such deconvolution methods. One is how to determine the number of bands without resorting to heuristics. The other is difficulty in avoiding the parameter solution trapped into local minima due to the hierarchy and the nonlinearity of the system. In this study, we propose a novel method of spectral deconvolution based on Bayesian estimation with the exchange Monte Carlo method, which is an application of the integral approximation of stochastic complexity and the exchange Monte Carlo method. We also experimentally show its effectiveness on synthetic data and on reflectance spectral data of olivine, one of the most common minerals of terrestrial planets.

Determination of microscopic dissociation constants of 3-hydroxy-α-(methylamino)methyl-benzenemethanol by a spectral deconvolution method

Microscopic dissociation constants of 3-hydroxy-alpha-(methylamino)methyl-benzenemethanol have been calculated from the titration spectrophotomeric data (c = 3.8 x 10(-4) M. Ionic strength = 0.16; buffer system: H3BO3/KOH) by application of a spectral deconvolution method. The results found (pKa = 9.48; pKb = 9.71; pKc = 10.12 and pKd = 9.88) are in good concordance with those obtained from the conventional regression linear method (pKa = 9.45; pKb = 9.77; pKc = 10.14 and pKd = 9.81).