Application of a new method for simultaneous phase and baseline correction of NMR signals (SINC)

Recently, we presented a new approach for simultaneous phase and baseline correction of nuclear magnetic resonance (NMR) signals (SINC) that is based on multiobjective optimization. The algorithm can automatically correct large sets of NMR spectra, which are commonly acquired when reactions and processes are monitored with NMR spectroscopy. The aim of the algorithm is to provide spectra that can be evaluated quantitatively, for example, to calculate the composition of a mixture or the extent of reaction. In this work, the SINC algorithm is tested in three different studies. In an in-house comparison study, spectra of different mixtures were corrected both with the SINC method and manually by different experienced users. The study shows that the results of the different users vary significantly and that their average uncertainty of the composition measurement is larger than the uncertainty obtained when the spectra are corrected with the SINC method. By means of a dilution study, we demonstrate that the SINC method is also applicable for the correction of spectra with low signal-to-noise ratio. Furthermore, a large set of NMR spectra that was acquired to follow a reaction was corrected with the SINC method. Even in this system, where the areas of the peaks and their chemical shifts changed during the course of reaction, the SINC method corrected the spectra robustly. The results show that this method is especially suited to correct large sets of NMR spectra and it is thus an important contribution for the automation of the evaluation of NMR spectra.     

自适应拉曼光谱成像数据去噪及其在植物细胞壁光谱分析中的应用

拉曼光谱成像数据存在基线漂移与宇宙射线干扰峰两类噪声信号,无法直接用于光谱分析研究,必须去除。现有单光谱去噪方法处理结果不稳定、可重复性差。针对这一问题,本研究提出了一种自适应拉曼光谱成像数据新型去噪法,采用优化的自适应迭代惩罚最小二乘法( Adaptive iteratively reweighted penalized least-squares,airPLS)和基于主成分分析( PCA)的干扰峰消除算法修正光谱基线漂移和宇宙射线干扰峰,具有输入参数少、光谱失真小、处理速度快、去噪结果稳定等优点。利用本方法去除了芒草( Miscanthus sinensis)细胞壁拉曼光谱成像数据(9010条光谱)中的噪声信号,并对去噪后数据进行PCA和聚类分析(CA),成功区分非植物光谱与植物光谱,分类结果优于未去噪数据。预期本方法可应用于其它光谱成像数据去噪,为光谱的解译和定量分析提供可靠的研究基础。     

Automatic compatibility tests of HSQC NMR spectra with proposed structures of chemical compounds

A recently introduced similarity measure is extended here for comparing two-dimensional spectra. Its applicability is demonstrated with heteronuclear single-quantum correlation (HSQC) NMR spectra. For testing the compatibility of a spectrum with the proposed chemical structure, first, the spectrum is predicted on the basis of that structure and then, the proposed comparison algorithm is applied. In this context, the topics of optimization are peak picking, signal intensity measures, and optimizing the parameters of the two-dimensional comparison method. The performance is analyzed with a test set of 289 structures of organic compounds and their HSQC and ¹H NMR spectra. The results obtained with HSQC spectra are better than those achieved using the previously described one-dimensional similarity test with ¹H NMR spectra alone.     

基于小波和轮廓提取的色谱基线算法研究

把基于小波和轮廓提取的基线算法引入了色谱基线提取领域;基于轮廓提取的算法为:通过构造一个滑动窗,沿着色谱数据滑动求窗内的最小值。把这些最小值插值平滑就获得了色谱基线。基于小波和轮廓提取的算法为:先用小波初步提取基线,然后把色谱数据减去小波提取的基线后再用轮廓提取算法获得基线,把小波提取的基线和轮廓提取的基线相加即为原数据的基线。对这两个算法进行了比较实验研究,结果表明:基于小波和轮廓提取的算法比轮廓提取的算法效果好,能更准确地提取色谱基线。     

MS2Grouper: Group assessment and synthetic replacement of duplicate proteomic tandem mass spectra

Shotgun proteomics experiments require the collection of thousands of tandem mass spectra; these sets of data will continue to grow as new instruments become available that can scan at even higher rates. Such data contain substantial amounts of redundancy with spectra from a particular peptide being acquired many times during a single LC-MS/MS experiment. In this article, we present MS2Grouper, an algorithm that detects spectral duplication, assesses groups of related spectra, and replaces these groups with synthetic representative spectra. Errors in detecting spectral similarity are corrected using a paraclique criterion-spectra are only assessed as groups if they are part of a clique of at least three completely interrelated spectra or are subsequently added to such cliques by being similar to all but one of the clique members. A greedy algorithm constructs a representative spectrum for each group by iteratively removing the tallest peaks from the spectral collection and matching to peaks in the other spectra. This strategy is shown to be effective in reducing spectral counts by up to 20% in LC-MS/MS datasets from protein standard mixtures and proteomes, reducing database search times without a concomitant reduction in identified peptides.     

基于导数直方图和神经网络的色谱基线提取算法

根据基线信号的变化特征,提出了一种新的色谱基线提取算法－基于一阶导数直方图和神经网络的色谱基线提取算法。鉴于信号总是存在着噪声,含噪声的基线信号一阶导数值也较大,并且分布无规律,而采用了多分辨率小波变换滤波技术。通过选择阈值门限,本算法可精确提取出信号的基线,尤其适用于非基线对称类的色谱信号的基线（趋势函数）提取。     

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm “Baseline Correction Combined Partial Least Squares (BCC-PLS)”, which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7–19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33–68%).     

Modified indirect hard modeling for non-invasive Raman measurements containing surface interference

Non-invasive Raman spectroscopy has been used in an increasing number of applications in recent years. However, in situations where surface signal is excessive, the acquired spectrum of probed sample suffers from surface interference in either conventional backscattering Raman or specially designed Raman methods. A computational method for Raman spectral recovery is required. Strong overlapping of Raman bands and intense fluorescence are the main obstacles that hinder the spectral recovery. In this paper, we present a modified version of an indirect hard modeling algorithm to extract the true Raman spectrum of the probed sample in a two-layer system. The proposed algorithm requires two spectra. By an iterative stepwise optimization, it models one spectrum as a combination of a scaling of the other spectrum, a polynomial baseline and the Raman peaks of the probed sample. It addresses the issue of Raman bands overlapping as well as intense fluorescence interference. The performance of the algorithm is evaluated on experimental Raman spectra. Comparative studies show that the proposed algorithm provides better results for Raman spectral recovery.     

基于能量重分配的波长偏移校正方法

针对原子光谱分析中波长偏移问题,提出了一种基于能量重分配原理实现波长偏移校正的方法.用抛物线插值法对能量密度分布进行反演,以谱图相似度为判断指标,通过三分法确定最佳偏移量,并根据最佳偏移量与能量密度分布对光谱强度进行重新计算,实现了亚像素级别的波长校正.对微型光纤光谱仪与单道扫描光谱仪获得的谱图进行了算法验证,使Eu元素的背景噪音平均降低了48%,Mo元素的信号稳定度由20%改善至约5%.该方法对具有结构化特征的谱图具有较好的适用性,且无须寻峰算法配合使用,有助于提高光谱分析精确度.     

Evaluation of a spectral searching algorithm for the comparison of Raman band positions

Raman spectroscopic identification of unknown materials involves often the comparison of the spectrum of the unknown spectrum with previously recorded reference spectra or data from literature. However, when spectra with many Raman bands or spectra of mixtures are involved, searching can be quite complex. Different chemometrical approaches have been proposed, but these have some drawbacks. Therefore, in this paper a novel approach is proposed, which is based on a multivariate comparison of Raman band positions. Different similarity measures can be used and are evaluated with spectra of test samples that were recorded on different spectrometers, using different laser wavelengths. Moreover, this study evaluates the performances of this algorithm for identifying different compounds in mixtures, by using an iterative approach.     

Drug–target interaction prediction by integrating multiview network data

Drug–target interaction (DTI) prediction is a challenging step in further drug repositioning, drug discovery and drug design. The advent of high-throughput technologies brings convenience to the development of DTI prediction methods. With the generation of a high number of data sets, many mathematical models and computational algorithms have been developed to identify the potential drug–target pairs. However, most existing methods are proposed based on the single view data. By integrating the drug and target data from different views, we aim to get more stable and accurate prediction results.In this paper, a multiview DTI prediction method based on clustering is proposed. We first introduce a model for single view drug–target data. The model is formulated as an optimization problem, which aims to identify the clusters in both drug similarity network and target protein similarity network, and at the same time make the clusters with more known DTIs be connected together. Then the model is extended to multiview network data by maximizing the consistency of the clusters in each view. An approximation method is proposed to solve the optimization problem. We apply the proposed algorithms to two views of data. Comparisons with some existing algorithms show that the multiview DTI prediction algorithm can produce more accurate predictions. For the considered data set, we finally predict 54 possible DTIs. From the similarity analysis of the drugs/targets, enrichment analysis of DTIs and genes in each cluster, it is shown that the predicted DTIs have a high possibility to be true.     

Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks

The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of the over-fitting and finding a generalized baseline. The developed method has been applied on simulated and real metabolomic gas-chromatography (GC) and Raman data sets. The results revealed that the proposed method can be used to handle different types of baselines with cave, convex, curvelinear, triangular and sinusoidal patterns. For further evaluation of the performances of this method, it has been compared with benchmarking baseline correction methods such as corner-cutting (CC), morphological weighted penalized least squares (MPLS), adaptive iteratively-reweighted penalized least squares (airPLS) and iterative polynomial fitting (iPF). In order to compare the methods, the projected difference resolution (PDR) criterion has been calculated for the data before and after the baseline correction procedure. The calculated values of PDR after the baseline correction using iBRANN, airPLS, MPLS, iPF and CC algorithms for the GC metabolomic data were 4.18, 3.64, 3.88, 1.88 and 3.08, respectively. The obtained results in this work demonstrated that the developed iterative Bayesian regularized neural network (iBRANN) method in this work thoroughly detects the baselines and is superior over the CC, MPLS, airPLS and iPF techniques. A graphical user interface has been developed for the suggested algorithm and can be used for easy implementation of the iBRANN algorithm for the correction of different chromatography, NMR and Raman data sets.     

A new hybrid strategy for constructing a robust calibration model for near-infrared spectral analysis

A new hybrid algorithm is proposed for construction of a high-quality calibration model for near-infrared (NIR) spectra that is robust against both spectral interference (including background and noise) and multiple outliers. The algorithm is a combination of continuous wavelet transform (CWT) and a modified iterative reweighted PLS (mIRPLS) procedure. In the proposed algorithm the spectral interference is filtered by CWT at the first stage then mIRPLS is proposed to detect the multiple outliers in the CWT domain. Compared with the original IRPLS method, mIRPLS does not need to adjust variable parameters to achieve optimum calibration results, which makes it very convenient to perform in practice. The final PLS model is constructed robustly because both the spectral interference and multiple outliers are eliminated. In order to validate the effectiveness and universality of the algorithm, it was applied to two different sets of NIR spectra. The results indicate that the proposed strategy can greatly enhance the robustness and predictive ability of NIR spectral analysis.     

A practical approach for determination of mass spectral baselines

Precise determination of the baseline levels of mass spectra is critical for identification and quantification of analytes. Herein, we present a practical approach for determination of the baselines of mass spectra acquired under differential conditions. The baseline determined by this approach was the sum of baseline drift and noise level. The baseline drift was determined by averaging a number of lowest ion intensities. The noise level was determined based on the fact that an accelerated intensity change exists from noise to signal. This change was best revealed by the established accumulative layer thickness curve that was derived from the thicknesses of individual deducted layers. Deductions were performed sequentially layer by layer, each of which has a thickness of averaged lowest ion intensities from existing spectral data. The layer where the accelerated intensity change occurred was defined as a transition layer, which was determined from the polynomial regression in the sixth order of the accumulative layer thickness curve followed by resolving the roots of its fourth derivative. We validated the presence of this transition layer through determination of its convergence from various accumulative layer thickness curves generated by varying either the ending or the fineness of the sequential layer deductions. This simple, practical, program-based baseline determination approach should greatly increase the accuracy and consistency of identification and quantification by mass spectrometry, and facilitate the automation of data processing, thereby increasing the power of any high throughput methodology in general and of shotgun lipidomics in particular.     

A linear semi-infinite programming strategy for constructing optimal wavelet transforms in multivariate calibration problems

A novel strategy for the optimization of wavelet transforms with respect to the statistics of the data set in multivariate calibration problems is proposed. The optimization follows a linear semi-infinite programming formulation, which does not display local maxima problems and can be reproducibly solved with modest computational effort. After the optimization, a variable selection algorithm is employed to choose a subset of wavelet coefficients with minimal collinearity. The selection allows the building of a calibration model by direct multiple linear regression on the wavelet coefficients. In an illustrative application involving the simultaneous determination of Mn, Mo, Cr, Ni, and Fe in steel samples by ICP-AES, the proposed strategy yielded more accurate predictions than PCR, PLS, and nonoptimized wavelet regression.     

A spectral similarity measure using Bayesian statistics

A spectral similarity measure was developed that can differentiate subtle differences between two spectra. The spectra are digitalized into a vector. The difference between the two spectra is defined by a difference vector, which is one spectrum minus the other. The spectral similarity measure is transformed into a hypothesis test of the similarities and differences between the two spectra. The scalar mean of the difference vector is used as the statistical variable for the hypothesis test. A threshold for the hypothesis that the spectra are different was proposed. The Bayesian prior odds ratio was estimated from multiple spectra of the same sample. The posterior odds ratio was used to quantity the spectral similarity measure of the two spectra. Diffuse reflectance near-infrared spectra of tobacco samples of two formulations were used to demonstrate this method. The results show that this new method can detect subtle differences between the spectra.     

FTIR microscopy of biological cells and tissue: data analysis using resonant Mie scattering (RMieS) EMSC algorithm

Transmission and transflection infrared microscopy of biological cells and tissue suffer from significant baseline distortions due to scattering effects, predominantly resonant Mie scattering (RMieS). This scattering can also distort peak shapes and apparent peak positions making interpretation difficult and often unreliable. A correction algorithm, the resonant Mie scattering extended multiplicative signal correction (RMieS-EMSC), has been developed that can be used to remove these distortions. The correction algorithm has two key user defined parameters that influence the accuracy of the correction. The first is the number of iterations used to obtain the best outcome. The second is the choice of the initial reference spectrum required for the fitting procedure. The choice of these parameters influences computational time. This is not a major concern when correcting individual spectra or small data sets of a few hundred spectra but becomes much more significant when correcting spectra from infrared images obtained using large focal plane array detectors which may contain tens of thousands of spectra. In this paper we show that, classification of images from tissue can be achieved easily with a few (<10) iterations but a reliable interpretation of the biochemical differences between classes could require more iterations. Regarding the choice of reference spectrum, it is apparent that the more similar it is to the pure absorption spectrum of the sample, the fewer iterations required to obtain an accurate corrected spectrum. Importantly however, we show that using three different non-ideal reference spectra, the same unique correction solution can be obtained.     

基于临近比较的快速拉曼光谱基线校正方法

拉曼光谱成像技术是基于拉曼散射效应所开发的一项现代检测技术,在现代生产、科学研究过程中使用非常广泛。拉曼光谱信号受荧光效应和仪器等方面的影响,往往会产生基线漂移,严重影响对信号特征的进一步提取。因此,必须对拉曼光谱信号进行基线校正。传统的基线校正方法,只针对单一光谱信号,计算量较大,在处理由大量拉曼信号组成的成像数据时,耗时较长且效果不佳。该文提出一种基于临近比较的快速基线校正方法,根据在相同背景下采集的光谱之间的相关性,实现快速基线校正,提高了拉曼成像数据的处理速度。     

Attenuation correction of SPECT imaging by dual energy method--quantification of 111In-labeled monoclonal anti-tumor antibody localization

Application of radiolabeled monoclonal anti-tumor antibodies for diagnosis and therapy has made remarkable progress in the past few years. Quantification of radiopharmaceutical localization is required adequate attenuation correction in SPECT imaging. Attenuation correction by transmission CT (TCT) data is one of the best method at present time. However, if a patient is moved between TCT and SPECT, this method is no more applicable. We developed a new attenuation correction algorithm by dual energy method, using 99mTc and 111In because of similarity of these linear attenuation coefficients. The new algorithm uses data of TCT with an external source of 99mTc, and requires another data from SPECT of 111In labeled monoclonal anti-tumor antibody, which are simultaneously obtained. TCT results in an attenuation map, which then serves as input into the final intrinsic correction algorithm to uncorrected SPECT data. In chest phantom experiment, the attenuation corrected SPECT images revealed nearly same distribution of actual radioactivity of 111In as compared to that of uncorrected one.     

A wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared spectral quantitative analysis

In this paper, we proposed a wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared (NIR) spectra quantitative analysis. The proposed method was incorporated with partial least square (PLS) to construct a prediction model. The proposed method chooses the current own optimal or the current global optimal to calculate the attractor. Then the particle updates its flight velocity by the attractor, and the particle state is updated by the random decision with the new velocity. Moreover, the root‐mean‐square error of cross‐validation is adopted as the fitness function for the proposed method. In order to demonstrate the usefulness of the proposed method, PLS with all wavelengths, uninformative variable elimination by PLS, elastic net, genetic algorithm combined with PLS, the discrete particle swarm optimization combined with PLS, the modified particle swarm optimization combined with PLS, the neighboring particle swarm optimization combined with PLS, and the proposed method are used for building the components quantitative analysis models of NIR spectral datasets, and the effectiveness of these models is compared. Two application studies are presented, which involve NIR data obtained from an experiment of meat content determination using NIR and a combustion procedure. Results verify that the proposed method has higher predictive ability for NIR spectral data and the number of selected wavelengths is less. The proposed method has faster convergence speed and could overcome the premature convergence problem. Furthermore, although improving the prediction precision may sacrifice the model complexity under a certain extent, the proposed method is overfitted slightly. Copyright © 2015 John Wiley & Sons, Ltd.