A new method for the automated selection of the number of components for deconvolving overlapping chromatographic peaks

Mathematical deconvolution methods can separate co-eluting peaks in samples for which (chromatographic) separation fail. However, these methods often heavily rely on manual user-input and interpretation. This is not only time-consuming but also error-prone and automation is needed if such methods are to be applied in a routine manner.     

Raman and IR Spectroscopy of a Mixed CH3CN-H2O Solvent in the Cn Stretching Region

Raman and IR spectroscopy have been used to elucidate the solvation process in a mixed water-acetonitrile solvent in the CN stretching region. The number and positions of the components forming the spectral contour are established by Fourier deconvolution and Factor analysis and their areas are determined by fitting. The forms of existence of acetonitrile in the mixed solvent are discussed.     

自动化质谱图解卷积和鉴定系统用于鉴定含痕量杂原子的石油馏分（英文）

Here we present a simple yet effective gas chromatography-mass spectrometry (GC-MS) identification approach for the detection of heteroatom-containing compounds (HACCs) in petroleum fractions. The MS/AMDIS (Automated Mass Spectral Deconvolution and Identification System) program was used to identify parts per million (ppm) HACC concentrations in petroleum fractions in place of traditional techniques (extraction and standard injection). Polycyclic aromatic sulfur heterocycles (S-PAHs) were used as model compounds to confirm the validity of the AMDIS identifiers, which were compared with extracted results using the off-line X-calibur software. AMDIS was able to identify ppm concentrations of S-PAHs in oil condensate. There was good agreement between experimental and AMDIS identification results for S-PAHs in oil condensate. AMDIS was also used to detect nitrogen-containing compounds (NCCs) and alkylphenols in oil condensate. Our results confirmed the presence of 2-methylbenzothiazole, carbazole, and 2,4-ditertbutyl phenol. In a crude oil sample, AMDIS identification of m/z=191 biomarkers was consistent with empirical results. Therefore, AMDIS can help to reduce the number of experimental steps in identification protocols.     

Non-negatively constrained image deblurring with an inexact interior point method

Nonlinear image deblurring procedures based on probabilistic considerations have been widely investigated in the literature. This approach leads to model the deblurring problem as a large scale optimization problem, with a nonlinear, convex objective function and non-negativity constraints on the sign of the variables. The interior point methods have shown in the last years to be very reliable in nonlinear programs. In this paper we propose an inexact Newton interior point (IP) algorithm designed for the solution of the deblurring problem. The numerical experience compares the IP method with another state-of-the-art method, the Lucy Richardson algorithm, and shows a significant improvement of the processing time.     

Functional speciation of metal-dissolved organic matter complexes by size exclusion chromatography coupled to inductively coupled plasma mass spectrometry and deconvolution analysis

High performance size exclusion chromatography coupled to inductively coupled plasma mass spectrometry (HP-SEC–ICP-MS), in combination with deconvolution analysis, has been used to obtain multielemental qualitative and quantitative information about the distributions of metal complexes with different forms of natural dissolved organic matter (DOM). High performance size exclusion chromatography coupled to inductively coupled plasma mass spectrometry chromatograms only provide continuous distributions of metals with respect to molecular masses, due to the high heterogeneity of dissolved organic matter, which consists of humic substances as well as biomolecules and other organic compounds. A functional speciation approach, based on the determination of the metals associated to different groups of homologous compounds, has been followed. Dissolved organic matter groups of homologous compounds are isolated from the aqueous samples under study and their high performance size exclusion chromatography coupled to inductively coupled plasma mass spectrometry elution profiles fitted to model Gaussian peaks, characterized by their respective retention times and peak widths. High performance size exclusion chromatography coupled to inductively coupled plasma mass spectrometry chromatograms of the samples are deconvoluted with respect to these model Gaussian peaks. This methodology has been applied to the characterization of metal–dissolved organic matter complexes in compost leachates. The most significant groups of homologous compounds involved in the complexation of metals in the compost leachates studied have been hydrophobic acids (humic and fulvic acids) and low molecular mass hydrophilic compounds. The environmental significance of these compounds is related to the higher biodegradability of the low molecular mass hydrophilic compounds and the lower mobility of humic acids. In general, the hydrophilic compounds accounted for the complexation of around 50% of the leached metals, with variable contributions of humic and fulvic acids, depending on the nature of the samples and the metals.     

Adaptive nonparametric estimation for Lévy processes observed at low frequency

This article deals with adaptive nonparametric estimation for Lévy processes observed at low frequency. For general linear functionals of the Lévy measure, we construct kernel estimators, provide upper risk bounds and derive rates of convergence under regularity assumptions.     

Decoding neural events from fMRI BOLD signal: A comparison of existing approaches and development of a new algorithm

Neuroimaging methodology predominantly relies on the blood oxygenation level dependent (BOLD) signal. While the BOLD signal is a valid measure of neuronal activity, variances in fluctuations of the BOLD signal are not only due to fluctuations in neural activity. Thus, a remaining problem in neuroimaging analyses is developing methods that ensure specific inferences about neural activity that are not confounded by unrelated sources of noise in the BOLD signal. Here, we develop and test a new algorithm for performing semiblind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that treats the neural event as an observable, but intermediate, probabilistic representation of the system's state. We test and compare this new algorithm against three other recent deconvolution algorithms under varied levels of autocorrelated and Gaussian noise, hemodynamic response function (HRF) misspecification and observation sampling rate. Further, we compare the algorithms' performance using two models to simulate BOLD data: a convolution of neural events with a known (or misspecified) HRF versus a biophysically accurate balloon model of hemodynamics. We also examine the algorithms' performance on real task data. The results demonstrated good performance of all algorithms, though the new algorithm generally outperformed the others (3.0% improvement) under simulated resting-state experimental conditions exhibiting multiple, realistic confounding factors (as well as 10.3% improvement on a real Stroop task). The simulations also demonstrate that the greatest negative influence on deconvolution accuracy is observation sampling rate. Practical and theoretical implications of these results for improving inferences about neural activity from fMRI BOLD signal are discussed.     

超声检测中维纳逆滤波解卷积方法的改进研究

维纳逆滤波是利用解卷积技术提高超声检测纵向分辨率的算法之一。本文分析了其传统算法 对噪声适应性不理想的原因,提出一种改进方法,利用检测系统信噪比先验知识,进行频域解卷积处 理,在此基础上,对检测系统频带内的处理结果建模,插补估计出频带外受噪声干扰影响较大的数 据,然后再变换到时域。计算机模拟及相应实验结果表明,这些改进方法较为理想,具有较强的噪声 适应能力。     

Aberration correction and its automatic control in scanning electron microscopes

An automatic aberration correction method has been implemented in scanning electron microscopes (SEM). Necessity of the automatic aberration correction is discussed. The procedure of the automatic aberration correction is explained in detail, where deconvolution techniques are used in order to extract probe information from SEM images. Due to the precise digitization and the usage of proper combinations of correction fields, linearity has been found between the amplitude of each aberration and the corresponding field strength. Experimental results are shown which demonstrate that the aberrations are corrected automatically by a linear feedback control method. After the automatic aberration correction, the image quality has been improved drastically.